Tourism Impacts Of Resort Operations In Talisay, Batangas

In this tropical country, resort is one of the most leading establishments preferred by the emerging number of tourists in the Philippines for it offers a pleasant environment and ambiance that is conducive to comfort and healthful relaxation and rest. It is a very convenient destination for the tourists since it also provides food and dining, reception, accommodation, lodging, business facilities and other services. It is almost an all-in-one destination that can suffice the need and wants of the tourists. Stress and pressure that was brought by the monotony of everyday life have become a great factor in the trend of the tourism industry and they both laid the foundation for so many establishment and recreational facilities for the tourists’ enjoyment and sensual experience.In Region 4-A, particularly in Batangas, many resorts are already established and known. This province is recently emerging as one of the top providers of class resorts. Talisay, bounded on the north by Ta gaytay City, Laurel on the west, Tanauan on the east, and Taal Lake on the south is one great contributor to the tourism of the province. Of all the caldera towns, Talisay is the most direct access from Manila to the Volcano Island via Santa Rosa – Tagaytay. A slightly longer route, but equally good road condition, is via South Expressway – Tanauan.Tourism has been considered by Talisay as their economic focus. The natural attributes of the town as well as the very limited availability of land for  other economic ventures such as agriculture and other land based activities influence the focus of economic activity for the town. The advantage of tourism as a conceivable direction for the town lies in its natural beauty, engaging landform and favorable climate. Comparable developments have been proven that tourism thrives primarily because of varied activities and interesting culture with natural endowments serving as the initial lure.Because these elements are present in Talisay, it would be in a favorable position to explore its possibilities. It has recently reinvented itself into a resort town, making use of the outstanding view and access that they have to Taal Lake and Taal Volcano. The said lake and volcano is one of the major tourist destinations in the country that is why many investors have been interested to venture into operating resorts within the municipality of Talisay.As the arrival of international and domestic tourists in the area grows, number of resorts in Talisay also increased. Rise of the tourists’ need for satisfaction and quality made competitions among these resorts become tighter. Because of the tourists’ preference in choosing a resort, many resorts raised their own standards and different themes have been made. Such actions and innovations must be clearly monitored as well as its impacts to the tourism industry of Talisay, Batangas. Sustainability of the resorts and its operations must also be given adequ ate attention. Problems rising from these operations must be significantly studied and essential research must be done to arrive at possible solutions and feasible recommendations.Conceptual FrameworkThis study aimed to delve the tourism impacts of the resort operations in Talisay, Batangas. This was based on the data gathered from the resorts in Talisay as well as information congregated from the local government of the said municipality.Figure 1 A schematic diagram showing the relationship of the research variables.The paradigm shows the factors that must be thoroughly researched and studied to achieve the understanding of tourism impacts of resort operations in Talisay, Batangas.Statement of the ProblemThe primary purpose of this study is to state the tourism impacts of resort operations in Talisay, Batangas. Specifically, this research study will answer the following questions: 1.) What are the profile of the respondents in terms of: 1.1 years of residency 1.2 family income? 2.) What are the benefits of the community of Talisay from resort operations in terms of: 2.1 employment opportunities 2.2 infrastructure development 2.3 safety and security 2.4 recreation opportunities 2.5 environmental protection? 3.) What are the corporate social responsibilities of all resort owners to help sustain a healthy environment for the residents? 4.) Is there a significant difference on the benefits of the community of Talisay from resort operations when grouped according to their profile? HypothesisAfter conducting surveys, interviews and research, the proponents of the study realized that the resort operations in Talisay, Batangas do not have an impact on the tourism of the said municipality.Scope and LimitationThe study is limited only to the population of Talisay, Batangas. The information that will be needed for the research will be acquired through the local government of the municipality and from the resorts that are operating in the area. The proponents will conduc t an interview to the resort owners or operators as well as to the residents in the local community. The data that will be gathered through these sources will serve as the basis of this study.The study covers the marketing, management, financial, social, economic and  environmental aspects. The study will help the researchers respond accurately and appropriately in the burdens the resort owners should face in the future. It intends to provide information regarding the tourism impacts and how the resort operations will be sustained through time. It will also provide additional knowledge to the Talisay’s local government in developing the sustainable tourism in their locality.Significance of the StudyThis study was significant in understanding the tourism impact of resort operations in Talisay, Batangas. Furthermore, it will be beneficial to the following:Academe; With the aid of results that can obtain in this study, different techniques and strategies can develop in underst anding tourism impact of resort owners and its sustainability, particularly in Talisay, Batangas.Resort owners; The results that will be taken in this study can provide resort owners information and assistance in case they are in the same line of industry.Tourists; The contents of the study will give every tourist insights and knowledge about Talisay as tourist destination.Local government; The accomplishment of the study will endow supplementary information that can contribute in the improvement of their locality.Lastly, to Future researchers; This study will provide useful information that can be used as a basis to their would-be researches particularly those that will focus on topics related to sustainability of tourism industry.Definition of TermsFor the meaningful interpretation of this research study, the following terms were defined operationally and conceptually with basis from authorized sources. Resort. It is one of the most leading establishments preferred by the emerging number of tourists in the Philippines for it offers a pleasant environment and ambiance that is conducive to comfort and healthful relaxation and rest. Tourism. This comprises the activities of persons travelling to and staying in places outside their usual environment for not  more than that one consecutive year for leisure, business and other purposes. Tourism Impact. It is the effect of tourist destinations, including resorts, to the community where tourism occurs.

Past Paper

NSS MATHEMATICS IN ACTION HONG KONG DIPLOMA OF SECONDARY EDUCATION EXAMINATION MOCK PAPER MATHEMATICS Compulsory Part Paper 2 Time allowed: 1 hour 15 minutes 1. Read carefully the instructions on the Answer Sheet and insert the information required in the spaces provided. 2. There are 45 questions in this book. All questions carry equal marks. 3. Answer ALL Questions. You are advised to use an HB pencil to mark all the answers on the Answer Sheet. Wrong marks must be completely erased with a clean rubber. 4. You should mark only ONE answer for each question. If you mark more than one answer, you will receive NO MARKS for that question. . No marks will be deducted for wrong answers.  © Pearson Education Asia Limited NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 There are 30 questions in Section A and 15 questions in Section B. The diagrams in this paper are not necessarily drawn to scale. Choose the best answer for each question. Section A 1. If n is an integer, then 33 n = 9 n ? 1 6. Which of the following statements about the equation 3( x ? 2) 2 ? 6 x ? 2 is true? A. It has distinct, rational real roots. B. It has distinct, irrational real roots. C. It has equal real roots. D. It has no real roots. 7. It is known that a polynomial g(x) is ivisible by 2x + 3. Which of the following must be a factor of g(4x – 3)? 2 n ? 1 A. B. C. D. 2. x 2 ? y 2 ? 2 xy ? 4 ? A. B. C. D. 3. A. B. C. D. 8. 1. 5. 8049. 8053. a = 3, b = ? 2 . a = 3, b = ? 3 . a = ? 2, b = ? 2 . a = ? 2, b = ? 3 . B. C. D. 9. Let p be a constant. Solve the equation ( x ? p )( x ? p ? 1) ? x ? p . A. B. C. D.  © Pearson Education Asia Limited NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 –2– 1. 1 . 6 –1. –2. ? Peter sold a vase to Mary at a profit of 15 %. Later, Mary sold the vase to David for $ 6400 and gained $ 420. What was the cost price of the vase for Peter? A. B. C. D. x ? ?p x ? p ? 1 ? ? p or x ? p ? 1 x ? ? p or x ? p 2x ? 3 4x ? 3 8x ? 3 8x ? 9 If x is an integer satisfying 4x ? 1 , then the 2(1 ? x) ? 6 x and x ? ?2 greatest value of x is A. If 5a ? 2b ? a ? 4b ? 11 , then A. B. C. D. 5. ( x ? y ? 2)( x ? y ? 2) ( x ? y ? 2)( x ? y ? 2) ( x ? y ? 2)( x ? y ? 2) ( x ? y ? 2)( x ? y ? 2) If f ( x) ? x 2012 ? 2012 x ? 2012 , then 2 f (? 1) ? 3 = A. B. C. D. 4. ?1? . ?3? 3n ? 1 . 3n ? 2 . 35 n ? 2 . $ 5200 $ 5970 $ 6877 $ 7780 10. The scale of a map is 1: 250. If the area of a playground on the map is 20 cm2, what is the actual area of the playground? A. B. C. D. 11. 5000 cm2 125 m2 625 m2 5000 m2 A.B. C. D. Let an be the nth term of a sequence. If a1 ? ?2 , a2 ? 1 and a n ? 2 ? 4a n ? 1 ? a n for any positive integer n, then a5 = A. B. C. D. 14. The circumference of a circle is measured to be 10 cm, correct to the nearest 0. 5 cm. Which of the following is a possible area of the circle? 15. 86. 66. 46. 26. In the figure, CDE and BDF are straight lines. If DF = DE and AB // CE, find ?ABD. 12. It is given that s varies jointly as t2 and u. If t is increased by 15% and u is decreased by 20%, then s A. B. C. D. is decreased by 8 %. is decreased by 5. 8 %. is increased by 5. 8 %. is increased by 8 %. 13. If z ? y and y 2 ? 4. 2 cm2 8. 55 cm2 8. 14 cm2 7. 11 cm2 A. B. C. D. 76? 104? 116? 128? 16. In the figure, a = 1 , which of the x following is true? III. z2 ? y2 1 x? y 2 3xz is a non-zero constant. A. B. C. D. I and II only I and III only II and III only I, II and III I. II.  © Pearson Education Asia Limited NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 A. B. C. D. –3– 40?. 45?. 50?. 55?. 17. In the figure, ABCD is a rhombus and FBC is a triangle. If FA = 2 cm and BC = 3 cm, find ED. 19. In the figure, a circular cone is cut into two parts A and B by a plane parallel to the base. 4 that of the 9 original cone, find the ratio of the olumes of A and B. If the base area of A is A. B. C. D. A. B. C. D. 1 cm 1. 2 cm 1. 5 cm 1. 8 cm 18. The figure shows a right pyramid with a square base and a slant edge of length 17 cm. If the total length of the edges of the pyramid is 132 cm, find the total surface area of the pyramid. 2:3 8 : 19 8 : 27 19 : 27 20. Through which of the following transformations, would figure A be transformed to figure B? I. Translation II. Rotation III. Reflection A. B. C. D. A. B. C. D. 544 cm2 608 cm2 736 cm2 800 cm2 II III I and III only II and III only 21. If the point P(7, –1) is rotated clockwise about the origin through 90? o Q, what is the distance between P and Q? A. B. C. D.  © Pearson Education Asia Limited NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 –4– 5 units 72 units 10 units 128 units 22. If a > 0, b > 0 and c < 0, which of the following may represent the graph of the straight line ax ? by ? c ? 0 ? 23. In the figure, 2BC = 5AC. Find sin ? . A. 2 29 A. B. C. B. D. 24. 29 2 cos(180? ? ? ) 1 ? ? sin(180? ? ? ) tan(90? ? ? ) A. B. C. D. C. 2 5 5 2 tan 2 ? tan ? 1 †“1 25. In the figure, O is the centre of the circle ABCD. Find x. D. A. B. C. D. 36? 40? 42? 45? 26. What is the area of the circle x2 + y2 + 12x ? y + 9 = 0? A. B. C. D.  © Pearson Education Asia Limited NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 –5– 9? 43? 52? 61? 27. Two fair dice are thrown once. What is the probability of getting a sum of 4 or 6? A. B. C. D. 1 6 2 9 5 9 5 36 30. The pie chart below shows the distribution of the nationalities of 60 students randomly selected from an international school. It is given that 9 of them are American. 28. The box-and-whisker diagram below shows the distribution of the heights (in cm) of 40 students in a class. Find the number of students whose heights are between 145 cm and 150 cm. A. B. C. D. 5 10 20 30If there are 840 students in the international school, estimate the number of Australian students in the school. A. 196 B. 208 C. 216 D. 224 Section B 31. 29. {a , a, a + d, a + 3d and a + 6d} is a grou p of numbers. Which of the following must be true? A. B. I. The mean of the group of numbers is a + 2d. II. The median of the group of numbers is a + d. III. The mode of the group of numbers is 2. A. B. C. D. C. D. I and II only I and III only II and III only I, II and III  © Pearson Education Asia Limited NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 1? –6– ab b ? ? 2 a ? b b? a 2 1 a2 a2 ? b2 b2 a2 ? b2 a 2 ? 2ab ? b 2 a2 ? b2 32. Which of the following best represent the graph of y ? 2 log 3 x ? x 2 x ? 1 34. Solve 16 ? 2 ? A. A. B. C. D. B. 15 ? 0. 2 5 2 5 or –3 2 5 log 8 log 5 ? log 2 log 4 35. If a and k are real numbers and a ? 11i ? (2 ? 3i )(3 ? ki) , then A. B. C. D. C. D. NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 ? ? 1 . ? 1. ? ? 1 . ? 1. 36. Find the maximum value of P = 1 – x – 4y subject to the following constraints. 1 ? x ? 3 2 ? y ? 4 ? ? ?2 y ? x ? 2 ? x ? 2 ? ?2 y ? 33. If ? and ? are the roots of the quadratic equation 4 x 2 ? 5 x ? 3 ? 0 , find the value 1 1 + . of 2? 2? 3 A. ? 5 2 B. ? 5 5 C. 8 5D. 6  © Pearson Education Asia Limited a ? 3, k a ? 3, k a ? 9, k a ? 9, k A. B. C. D. –7– 3 4 6 7 37. It is given that three positive numbers x, y and z are in geometric sequence. Which of the following must be true? I. x3, y3, z3 are in geometric sequence. II. 3x, 3y, 3z are in geometric sequence. III. log x2, log y2, log z2 are in arithmetic sequence. A. B. C. D. 40. The figure shows a circle with centre O. BC and BA are the tangents to the circle at C and D respectively. If ? BAC = 42? , find ? BOC. I and II only I and III only II and III only I, II and III 38. Find x in the figure, correct to the nearest integer. A. B.C. D. 66? 72? 84? 90? 41. The figure shows a triangular prism ABCDEF, where both ? ABF and ?DCE are right-angled isosceles A. B. C. D. 12 13 14 15 triangles. If AB = 10 and BC = 5, find the angle between the line AE and the plane ABCD, correct to the nearest degree. 39. Solve 1 + sin? cos ? = 3sin2? for 0? ? ? ? 360?. A. B. C. D. ? = 45? or 225? ? = 135? , 207? or 225? ? = 45? , 153? , 225? or 333? ? = 135? , 153? , 315? or 333?  © Pearson Education Asia Limited NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 A. B. C. D. –8– 14? 17? 22? 45? 42. The figure shows a circle which is symmetrical about the y-axis. A(4, –1) nd B are two end points of a diameter of the circle. If the equation of the tangent to the circle at B is 4 x ? 3 y ? 31 ? 0 , find the coordinates of the centre of the circle. A. B. C. D. 3 (0, ) 2 (0, 2) 5 (0, ) 2 1 ( ? , 2) 2 44. A box contains 50 bulbs and 8 of them are defective. Two bulbs are drawn at random from the box without replacement. Given that at least one bulb drawn is defective, find the probability that exactly one bulb drawn is defective. 4 A. 13 3 B. 5 4 C. 5 12 D. 13 45. In a Chinese test, the standard scores of the marks obtained by John and Mary are †“1. 05 and 0. 8 respectively. Which of the following are true? I.II. III. 43. There are 2 different English books and 4 different Chinese books on a table. If all the books are put onto a shelf and the two books at the two ends must be of different languages, in how many ways can the books be arranged? A. B. C. D. A. B. C. D. 32 40 192 384  © Pearson Education Asia Limited NSS MIA 2012 Mock Paper (Compulsory Part) – Paper 2 Mary performs better than John in the test. Compared with John, the mark obtained by Mary is closer to the mean mark of the test. The mark obtained by John is below the 16th percentile of the marks in the test. I and II only I and III only II and III only I, II and III End of test –9–

Om Heizer Om10 Ism 04

Chapter FORECASTING Discussion Questions 1.? Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When quantitative data are difficult to obtain, qualitative models may be appropriate. 2.? Approaches are qualitative and quantitative. Qualitative is relatively subjective; quantitative uses numeric models. 3.? Short-range (under 3 months), medium-range (3 months to 3 years), and long-range (over 3 years). 4.? The steps that should be used to develop a forecasting system are: (a)?Determine the purpose and use of the forecast (b)? Select the item or quantities that are to be forecasted (c)? Determine the time horizon of the forecast (d)? Select the type of forecasting model to be used (e)? Gather the necessary data (f)? Validate the forecasting model (g)? Make the forecast (h)? Implement and evaluate the results 5.? Any three of: sales planning, production planning and budgeting, cash budgeting, analyzing various operating plans. 6.? There is no mechanism for growth in these models; they are built exclusively from historical demand values. Such methods will always lag trends. .? Exponential smoothing is a weighted moving average where all previous values are weighted with a set of weights that decline exponentially. 8.? MAD, MSE, and MAPE are common measures of forecast accuracy. To find the more accurate forecasting model, forecast with each tool for several periods where the demand outcome is known, and calculate MSE, MAPE, or MAD for each. The smaller error indicates the better forecast. 9.? The Delphi technique involves: (a)? Assembling a group of experts in such a manner as to preclude direct communication between identifiable members of the group (b)?Assembling the responses of each expert to the questions or problems of interest (c)? Summarizing these responses (d)? Providing each expert with the summary of all responses (e)? Asking each expert to study the summary of the responses and respond again to the questions or problems of interest. (f)? Repeating steps (b) through (e) several times as necessary to obtain convergence in responses. If convergence has not been obtained by the end of the fourth cycle, the responses at that time should probably be accepted and the process terminated—little additional convergence is likely if the process is continued. 0.? A time series model predicts on the basis of the assumption that the future is a function of the past, whereas an associative model incorporates into the model the variables of factors that might influence the quantity being forecast. 11.? A time series is a sequence of evenly spaced data points with the four components of trend, seasonality, cyclical, and random variation. 12.? When the smoothing constant, (, is large (close to 1. 0), more weight is given to recent data; when ( is low (close to 0. 0), more weight is given to past data. 13.? Seasonal patterns are of fixed duration a nd repeat regularly.Cycles vary in length and regularity. Seasonal indices allow â€Å"generic† forecasts to be made specific to the month, week, etc. , of the application. 14.? Exponential smoothing weighs all previous values with a set of weights that decline exponentially. It can place a full weight on the most recent period (with an alpha of 1. 0). This, in effect, is the naive approach, which places all its emphasis on last period’s actual demand. 15.? Adaptive forecasting refers to computer monitoring of tracking signals and self-adjustment if a signal passes its present limit. 16.?Tracking signals alert the user of a forecasting tool to periods in which the forecast was in significant error. 17.? The correlation coefficient measures the degree to which the independent and dependent variables move together. A negative value would mean that as X increases, Y tends to fall. The variables move together, but move in opposite directions. 18.? Independent variable (x) is said to explain variations in the dependent variable (y). 19.? Nearly every industry has seasonality. The seasonality must be filtered out for good medium-range planning (of production and inventory) and performance evaluation. 20.? There are many examples.Demand for raw materials and component parts such as steel or tires is a function of demand for goods such as automobiles. 21.? Obviously, as we go farther into the future, it becomes more difficult to make forecasts, and we must diminish our reliance on the forecasts. Ethical Dilemma This exercise, derived from an actual situation, deals as much with ethics as with forecasting. Here are a few points to consider:  ¦ No one likes a system they don’t understand, and most college presidents would feel uncomfortable with this one. It does offer the advantage of depoliticizing the funds al- location if used wisely and fairly.But to do so means all parties must have input to the process (such as smoothing constants) and all data need to be open to everyone.  ¦ The smoothing constants could be selected by an agreed-upon criteria (such as lowest MAD) or could be based on input from experts on the board as well as the college.  ¦ Abuse of the system is tied to assigning alphas based on what results they yield, rather than what alphas make the most sense.  ¦ Regression is open to abuse as well. Models can use many years of data yielding one result or few years yielding a totally different forecast.Selection of associative variables can have a major impact on results as well. Active Model Exercises* ACTIVE MODEL 4. 1: Moving Averages 1.? What does the graph look like when n = 1? The forecast graph mirrors the data graph but one period later. 2.? What happens to the graph as the number of periods in the moving average increases? The forecast graph becomes shorter and smoother. 3.? What value for n minimizes the MAD for this data? n = 1 (a naive forecast) ACTIVE MODEL 4. 2: Exponential Smoothing 1.? Wha t happens to the graph when alpha equals zero? The graph is a straight line.The forecast is the same in each period. 2.? What happens to the graph when alpha equals one? The forecast follows the same pattern as the demand (except for the first forecast) but is offset by one period. This is a naive forecast. 3.? Generalize what happens to a forecast as alpha increases. As alpha increases the forecast is more sensitive to changes in demand. *Active Models 4. 1, 4. 2, 4. 3, and 4. 4 appear on our Web site, www. pearsonhighered. com/heizer. 4.? At what level of alpha is the mean absolute deviation (MAD) minimized? alpha = . 16 ACTIVE MODEL 4. 3: Exponential Smoothing with Trend Adjustment .? Scroll through different values for alpha and beta. Which smoothing constant appears to have the greater effect on the graph? alpha 2.? With beta set to zero, find the best alpha and observe the MAD. Now find the best beta. Observe the MAD. Does the addition of a trend improve the forecast? alpha = . 11, MAD = 2. 59; beta above . 6 changes the MAD (by a little) to 2. 54. ACTIVE MODEL 4. 4: Trend Projections 1.? What is the annual trend in the data? 10. 54 2.? Use the scrollbars for the slope and intercept to determine the values that minimize the MAD. Are these the same values that regression yields?No, they are not the same values. For example, an intercept of 57. 81 with a slope of 9. 44 yields a MAD of 7. 17. End-of-Chapter Problems [pic] (b) | | |Weighted | |Week of |Pints Used |Moving Average | |August 31 |360 | | |September 7 |389 |381 ( . 1 = ? 38. 1 | |September 14 |410 |368 ( . 3 = 110. 4 | |September 21 |381 |374 ( . 6 = 224. 4 | |September 28 |368 |372. | |October 5 |374 | | | |Forecast 372. 9 | | (c) | | | |Forecasting | Error | | |Week of |Pints |Forecast |Error |( . 20 |Forecast| |August 31 |360 |360 |0 |0 |360 | |September 7 |389 |360 |29 |5. 8 |365. 8 | |September 14 |410 |365. 8 |44. 2 |8. 84 |374. 64 | |September 21 |381 |374. 64 |6. 36 |1. 272 |375. 12 | |Se ptember 28 |368 |375. 912 |–7. 912 |–1. 5824 |374. 3296| |October 5 |374 |374. 3296 |–. 3296 |–. 06592 |374. 2636| The forecast is 374. 26. (d)? The three-year moving average appears to give better results. [pic] [pic] Naive tracks the ups and downs best but lags the data by one period. Exponential smoothing is probably better because it smoothes the data and does not have as much variation. TEACHING NOTE: Notice how well exponential smoothing forecasts the naive. [pic] (c)? The banking industry has a great deal of seasonality in its processing requirements [pic] b) | | |Two-Year | | | |Year |Mileage |Moving Average |Error ||Error| | |1 |3,000 | | | | | |2 |4,000 | | | | | |3 |3,400 |3,500 |–100 | |100 | |4 |3,800 |3,700 |100 | |100 | |5 |3,700 |3,600 |100 | |100 | | | |Totals| |100 | | |300 | | [pic] 4. 5? (c)? Weighted 2 year M. A. ith . 6 weight for most recent year. |Year |Mileage |Forecast |Error ||Error| | |1 |3,000 | | | | |2 |4,000 | | | | |3 |3,400 |3,600 |–200 |200 | |4 |3,800 |3,640 |160 |160 | |5 |3,700 |3,640 |60 |60 | | | | | | | 420 | | Forecast for year 6 is 3,740 miles. [pic] 4. 5? (d) | | |Forecast |Error ( |New | |Year |Mileage |Forecast |Error |( = . 50 |Forecast | |1 |3,000 |3,000 | ?0 | 0 |3,000 | |2 |4,000 |3,000 |1,000 |500 |3,500 | |3 |3,400 |3,500 | –100 |–50 |3,450 | |4 |3,800 |3,450 | 350 |175 |3,625 | |5 |3,700 |3,625 | 75 |? 38 |3,663 | | | |Total |1,325| | | | The forecast is 3,663 miles. 4. 6 |Y Sales |X Period |X2 |XY | |January |20 |1 |1 |20 | |February |21 |2 |4 |42 | |March |15 |3 |9 |45 | |April |14 |4 |16 |56 | |May |13 |5 |25 |65 | |June |16 |6 |36 |96 | |July |17 |7 |49 |119 | |August |18 |8 |64 |144 | |September |20 |9 |81 |180 | |October |20 |10 |100 |200 | |November |21 |11 |121 |231 | |December |23 |12 |144 |276 | |Sum | 18 |78 |650 |1,474 | |Average |? 18. 2 | 6. 5 | | | (a) [pic] (b)? [i]? NaiveThe coming January = December = 23 [ii]? 3-month moving (20 + 21 + 23)/3 = 21. 33 [iii]? 6-month weighted [(0. 1 ( 17) + (. 1 ( 18) + (0. 1 ( 20) + (0. 2 ( 20) + (0. 2 ( 21) + (0. 3 ( 23)]/1. 0 = 20. 6 [iv]? Exponential smoothing with alpha = 0. 3 [pic] [v]? Trend? [pic] [pic] Forecast = 15. 73? +?. 38(13) = 20. 67, where next January is the 13th month. (c)? Only trend provides an equation that can extend beyond one month 4. 7? Present = Period (week) 6. a) So: where [pic] )If the weights are 20, 15, 15, and 10, there will be no change in the forecast because these are the same relative weights as in part (a), i. e. , 20/60, 15/60, 15/60, and 10/60. c)If the weights are 0. 4, 0. 3, 0. 2, and 0. 1, then the forecast becomes 56. 3, or 56 patients. [pic] [pic] |Temperature |2 day M. A. | |Error||(Error)2| Absolute |% Error | |93 |— | — |— |— | |94 |— | — |— |— | |93 |93. 5 | 0. 5 |? 0. 25| 100(. 5/93) | = 0. 54% | |95 |93. 5 | 1. 5 | ? 2. 25| 100(1. 5/95) | = 1. 58% | |96 |94. 0 | 2. 0 |? 4. 0 0| 100(2/96) | = 2. 08% | |88 |95. 5 | 7. | 56. 25| 100(7. 5/88) | = 8. 52% | |90 |92. 0 | 2. 0 |? 4. 00| 100(2/90) | = 2. 22% | | | | |13. 5| | | 66. 75 | | |14. 94% | MAD = 13. 5/5 = 2. 7 (d)? MSE = 66. 75/5 = 13. 35 (e)? MAPE = 14. 94%/5 = 2. 99% 4. 9? (a, b) The computations for both the two- and three-month averages appear in the table; the results appear in the figure below. [pic] (c)? MAD (two-month moving average) = . 750/10 = . 075 MAD (three-month moving average) = . 793/9 = . 088 Therefore, the two-month moving average seems to have performed better. [pic] (c)? The forecasts are about the same. [pic] 4. 12? t |Day |Actual |Forecast | | | | |Demand |Demand | | |1 |Monday |88 |88 | | |2 |Tuesday |72 |88 | | |3 |Wednesday |68 |84 | | |4 |Thursday |48 |80 | | |5 |Friday | |72 |( Answer | Ft = Ft–1 + ((At–1 – Ft–1) Let ( = . 25. Let Monday forecast demand = 88 F2 = 88 + . 25(88 – 88) = 88 + 0 = 88 F3 = 88 + . 25(72 – 88) = 88 – 4 = 84 F4 = 84 + . 25(68 – 84) = 84 – 4 = 80 F5 = 80 + . 25(48 – 80) = 80 – 8 = 72 4. 13? (a)? Exponential smoothing, ( = 0. 6: | | |Exponential |Absolute | |Year |Demand |Smoothing ( = 0. |Deviation | |1 |45 |41 |4. 0 | |2 |50 |41. 0 + 0. 6(45–41) = 43. 4 |6. 6 | |3 |52 |43. 4 + 0. 6(50–43. 4) = 47. 4 |4. 6 | |4 |56 |47. 4 + 0. 6(52–47. 4) = 50. 2 |5. 8 | |5 |58 |50. 2 + 0. 6(56–50. 2) = 53. 7 |4. 3 | |6 |? |53. 7 + 0. 6(58–53. 7) = 56. 3 | | ( = 25. 3 MAD = 5. 06 Exponential smoothing, ( = 0. 9: | | |Exponential |Absolute | |Year |Demand |Smoothing ( = 0. |Deviation | |1 |45 |41 |4. 0 | |2 |50 |41. 0 + 0. 9(45–41) = 44. 6 |5. 4 | |3 |52 |44. 6 + 0. 9(50–44. 6 ) = 49. 5 |2. 5 | |4 |56 |49. 5 + 0. 9(52–49. 5) = 51. 8 |4. 2 | |5 |58 |51. 8 + 0. 9(56–51. 8) = 55. 6 |2. 4 | |6 |? |55. 6 + 0. 9(58–55. 6) = 57. 8 | | ( = 18. 5 MAD = 3. 7 (b)? 3-year moving average: | | |Three-Year |Absolute | |Year |Demand |Moving Average |Deviation | |1 45 | | | |2 |50 | | | |3 |52 | | | |4 |56 |(45 + 50 + 52)/3 = 49 |7 | |5 |58 | (50 + 52 + 56)/3 = 52. 7 |5. 3 | |6 |? | (52 + 56 + 58)/3 = 55. 3 | | ( = 12. 3 MAD = 6. 2 (c)? Trend projection: | | | |Absolute | |Year |Demand |Trend Projection |Deviation | |1 |45 |42. 6 + 3. 2 ( 1 = 45. 8 |0. 8 | |2 |50 |42. 6 + 3. 2 ( 2 = 49. 0 |1. 0 | |3 |52 |42. 6 + 3. 2 ( 3 = 52. 2 |0. 2 | |4 |56 |42. 6 + 3. 2 ( 4 = 55. 4 |0. | |5 |58 |42. 6 + 3. 2 ( 5 = 58. 6 |0. 6 | |6 |? |42. 6 + 3. 2 ( 6 = 61. 8 | | ( = 3. 2 MAD = 0. 64 [pic] | X |Y |XY |X2 | | 1 |45 | 45 | 1 | | 2 |50 |100 | 4 | | 3 |52 |156 | 9 | | 4 |56 |224 |16 | | 5 |58 |290 |25 | Then: (X = 15, (Y = 261, (XY = 815, (X2 = 55, [pic]= 3, [pic]= 52. 2 Therefore: [pic] (d)? Comparing the results of the forecasting methodologies for parts (a), (b), and (c). |Forecast Methodology |MAD | |Exponential smoothing, ( = 0. |5. 06 | |Exponential smoothing, ( = 0. 9 |3. 7 | |3-year moving average |6. 2 | |Trend projection |0. 64 | Based on a mean absolute deviation criterion, the trend projection is to be preferred over the exponential smoothing with ( = 0. 6, exponential smoothing with ( = 0. 9, or the 3-year moving average forecast methodologies. 4. 14 Method 1:MAD: (0. 20 + 0. 05 + 0. 05 + 0. 20)/4 = . 125 ( better MSE : (0. 04 + 0. 0025 + 0. 0025 + 0. 04)/4 = . 021 Method 2:MAD: (0. 1 + 0. 20 + 0. 10 + 0. 11) / 4 = . 1275 MSE : (0. 01 + 0. 04 + 0. 01 + 0. 0121) / 4 = . 018 ( better 4. 15 | |Forecast Three-Year |Absolute | |Year |Sales |Moving Average |Deviation | |2005 |450 | | | |2006 |495 | | | |2007 |518 | | | |2008 |563 |(450 + 495 + 518)/3 = 487. 7 |75. 3 | |2009 |584 |(495 + 518 + 563)/3 = 525. 3 |58. 7 | |2010 | |(518 + 563 + 584)/3 = 555. 0 | | | | | ( = 134 | | | | MAD = 67 | 4. 16 Year |Time Period X |Sales Y |X2 |XY | |2005 |1 |450 | 1 |450 | |2006 |2 |495 | 4 |990 | |2007 |3 |518 | 9 |1554 | |2008 |4 |563 |16 |2252 | |2009 |5 |584 |25 |2920 | | | | ( = 2610| |( = 55 | |( = 8166 | [pic] [pic] |Year |Sales |Forecast Trend |Absolute Deviation | |2005 |450 |454. 8 |4. 8 | |2006 |495 |488. 4 |6. | |2007 |518 |522. 0 |4. 0 | |2008 |563 |555. 6 |7. 4 | |2009 |584 |589. 2 |5. 2 | |2010 | |622. 8 | | | | | | ( = 28 | | | | | MAD = 5. 6 | 4. 17 | | |Forecast Exponential |Absolute | |Year |Sales |Smoothing ( = 0. 6 |Deviation | |2005 |450 |410. 0 |40. | |2006 |495 |410 + 0. 6(450 – 410) = 434. 0 |61. 0 | |2007 |518 |434 + 0. 6(495 – 434) = 470. 6 |47. 4 | |2008 |563 |470. 6 + 0. 6(518 – 470. 6) = 499. 0 |64. 0 | |2009 |584 |499 + 0. 6(563 – 499) = 537. 4 |46. 6 | |2010 | |537. 4 + 0. 6(584 – 537. 4) = 565. 6 | | | | | ( = 259 | | | | MAD = 51. 8 | | | |Forecast Exponential |Absolute | |Year |Sales |Smoothing ( = 0. |Deviation | |2005 |450 |410. 0 |40. 0 | |2006 |495 |410 + 0. 9(450 – 410) = 446. 0 |49. 0 | |2007 |518 |446 + 0. 9(495 – 446) = 490. 1 |27. 9 | |2008 |563 |490. 1 + 0. 9(518 – 490. 1) = 515. 2 |47. 8 | |2009 |584 |515. 2 + 0. 9(563 – 515. 2) = 558. 2 |25. 8 | |2010 | |558. 2 + 0. 9(584 – 558. 2) = 581. 4 | | | | |( = 190. 5 | | | |MAD = 38. 1 | (Refer to Solved Problem 4. 1)For ( = 0. 3, absolute deviations for 2005–2009 are 40. 0, 73. 0, 74. 1, 96. 9, 88. 8, respectively. So the MAD = 372. 8/5 = 74. 6. [pic] Because it gives the lowest MAD, the smoothing constant of ( = 0. 9 gives the most accurate forecast. 4. 18? We need to find the smoothing constant (. We know in general that Ft = Ft–1 + ((At–1 – Ft–1); t = 2, 3, 4. Choose either t = 3 or t = 4 (t = 2 won’t let us find ( because F2 = 50 = 50 + ((50 – 50) holds for any (). Let’s pick t = 3. Then F3 = 48 = 50 + ((42 – 50) or 48 = 50 + 42( – 50( or –2 = –8( So, . 25 = ( Now we can find F5 : F5 = 50 + ((46 – 50)F5 = 50 + 46( – 50( = 50 – 4( For ( = . 25, F5 = 50 – 4(. 25) = 49 The forecast for time period 5 = 49 units. 4. 19? Trend adjusted exponential smoothing: ( = 0. 1, ( = 0. 2 | | |Unadjusted | |Adjusted | | | |Month |Income |Forecast |Trend |Forecast ||Error||Error2 | |February |70. 0 | 65. 0 | 0. 0 | 65 |? 5. 0 |? 25. 0 | |March |68. 5 | 65. 5 | 0. 1 | 65. 6 |? 2. 9 |? 8. 4 | |April |64. 8 | 65. 9 | 0. 16 |66. 05 |? 1. 2 |? 1. 6 | |May |71. 7 | 65. 92 | 0. 13 |66. 06 |? 5. 6 |? 31. 9 | |June |71. | 66. 62 | 0. 25 |66. 87 |? 4. 4 |? 19. 7 | |July |72. 8 | 67. 31 | 0. 33 |67. 64 |? 5. 2 |? 26. 6 | |August | | 68. 16 | |68. 60 | |24. 3| | |113. 2| | MAD = 24. 3/6 = 4. 05, MSE = 113. 2/6 = 18. 87. Note that all numbers are rounded. Note: To use POM for Windows to solve this problem, a period 0, which contains the initial forecast and initial trend, must be added. 4. 20? Trend adjusted exponential smoothing: ( = 0. 1, ( = 0. 8 [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] 4. 23? Students must determine the naive forecast for the four months .The naive forecast for March is the February actual of 83, etc. |(a) | |Actual |Forecast ||Error| ||% Error| | | |March |101 |120 |19 |100 (19/101) = 18. 81% | | |April |? 96 |114 |18 |100 (18/96) ? = 18. 75% | | |May |? 89 |110 |21 |100 (21/89) ? = 23. 60% | | |June |108 |108 |? 0 |100 (0/108) ? = 0% | | | | | | |58 | | | 61. 16% | [pic] |(b)| |Actual |Naive ||Error| ||% Error| | | |March |101 |? 83 |18 |100 (18/101) = 17. 82% | | |April |? 96 |101 |? |100 (5/96) ? = 5. 21% | | |May |? 89 |? 96 |? 7 |100 (7/89) ? =? 7. 87% | | |June |108 |? 89 |19 |100 (19/108) = 17. 59% | | | | | | |49| | |48. 49% | | [pic] Naive outperforms management. (c)? MAD for the manager’s technique is 14. 5, while MAD for the naive forecast is only 12. 25. MAPEs are 15. 29% and 12. 12%, respectively. So the naive method is better. 4. 24? (a)? Graph of demand The observations obviously do not form a straight line but do tend to cluster about a straight line over the range shown. (b)? Least-squares regression: [pic] Assume Appearances X |Demand Y |X2 |Y2 |XY | |3 | 3 | 9 | 9 | 9 | |4 | 6 |16 | 36 |24 | |7 | 7 |49 | 49 |49 | |6 | 5 |36 | 25 |30 | |8 |10 |64 |100 |80 | |5 | 7 |25 | 49 |35 | |9 | ? | | | | (X = 33, (Y = 38, (XY = 227, (X2 = 199, [pic]= 5. 5, [pic]= 6. 33. Therefore: [pic] The following figure shows both the data and the resulting equation: [pic] (c) If there are nine performances by Stone Temple Pilots, the estimated sales are: (d) R = . 82 is the correlation coefficient, and R2 = . 68 means 68% of the variation in sales can be explained by TV appearances. 4. 25? |Number of | | | | | |Accidents | | | | |Month |(y) |x |xy |x2 | |January | 30 | 1 | 30 | 1 | |February | 40 | 2 | 80 | 4 | |March | 60 | 3 |180 | 9 | |April | 90 | 4 |360 |16 | |? Totals | |220 | | | [pic] The regression line is y = 5 + 20x. The forecast for May (x = 5) is y = 5 + 20(5) = 105. 4. 26 |Season |Year1 |Year2 |Average |Average |Seasonal |Year3 | | |Demand |Demand |Year1(Year2 |Season |Index |Demand | | | | |Demand |Demand | | | |Fall |200 |250 |225. 0 |250 |0. 90 |270 | |Winter |350 |300 |325. |250 |1. 30 |390 | |Spring |150 |165 |157. 5 |250 |0. 63 |189 | |Summer |300 |285 |292. 5 |250 |1. 17 |351 | 4. 27 | | Winter |Spring |Summer |Fall | |2006 |1,400 |1,500 |1,000 |600 | |2007 |1,200 |1,400 |2,100 |750 | |2008 |1,000 |1,600 |2,000 |650 | |2009 | 900 |1,500 |1,900 | 500 | | |4,500 |6,000 |7,000 |2,500 | 4. 28 | | | | |Average | | | | | | |Average |Quarterly |Seasonal | |Quarter |2007 |2008 |2009 |Demand |Demand |Index | |Winter | 73 | 65 | 89 | 75. 67 |106. 67 |0. 709 | |Spring |104 | 82 |146 |110. 67 |106. 67 |1. 037 | |Summer |168 |124 |205 |165. 67 |106. 67 |1. 553 | |Fall | 74 | 52 | 98 | 74. 67 |106. 67 |0. 700 | 4. 29? 2011 is 25 years beyond 1986. Therefore, the 2011 quarter numbers are 101 through 104. | | | | |(5) | | |(2) |(3) |(4) |Adjusted | |(1) |Quarter |Forecast |Seasonal |Forecast | |Quarter |Number |(77 + . 3Q) |Factor |[(3) ( (4)] | |Winter |101 |12 0. 43 | . 8 | 96. 344 | |Spring |102 |120. 86 |1. 1 |132. 946 | |Summer |103 |121. 29 |1. 4 |169. 806 | |Fall |104 |121. 72 | . 7 | 85. 204 | 4. 30? Given Y = 36 + 4. 3X (a) Y = 36 + 4. 3(70) = 337 (b) Y = 36 + 4. 3(80) = 380 (c) Y = 36 + 4. 3(90) = 423 4. 31 4. 33? (a)? See the table below. For next year (x = 6), the number of transistors (in millions) is forecasted as y = 126 + 18(6) = 126 + 108 = 234. Then y = a + bx, where y = number sold, x = price, and |4. 32? a) | x |y |xy |x2 | | | 16 | 330 | 5,280 |256 | | | 12 | 270 | 3,240 |144 | | | 18 | 380 | 6,840 |324 | | | 14 | 300 | 4,200 |196 | | | 60 |1,280 |19,560 |920 | So at x = 2. 80, y = 1,454. 6 – 277. 6($2. 80) = 677. 32. Now round to the nearest integer: Answer: 677 lattes. [pic] (b)? If the forecast is for 20 guests, the bar sales forecast is 50 + 18(20) = $410. Each guest accounts for an additional $18 in bar sales. |Table for Problem 4. 33 | | | | | |Year |Transistors | | | | | | | |(x) |(y) |xy |x2 |126 + 18x |E rror |Error2 ||% Error| | | |? 1 |140 |? 140 |? 1 |144 |–4 |? 16 |100 (4/140)? = 2. 86% | | |? 2 |160 |? 320 |? 4 |162 |–2 | 4 |100 (2/160)? = 1. 25% | | |? 3 |190 |? 570 |? 9 |180 |10 |100 |100 (10/190) = 5. 26% | | |? 4 |200 |? 800 |16 |198 |? 2 | 4 |100 (2/200) = 1. 00% | | |? |210 |1,050 |25 |216 |–6 |? 36 |100 (6/210)? = 2. 86% | |Totals |15 | | |900 | | |2,800 | | (b)? MSE = 160/5 = 32 (c)? MAPE = 13. 23%/5 = 2. 65% 4. 34? Y = 7. 5 + 3. 5X1 + 4. 5X2 + 2. 5X3 (a)? 28 (b)? 43 (c)? 58 4. 35? (a)? [pic] = 13,473 + 37. 65(1860) = 83,502 (b)? The predicted selling price is $83,502, but this is the average price for a house of this size. There are other factors besides square footage that will impact the selling price of a house. If such a house sold for $95,000, then these other factors could be contributing to the additional value. (c)?Some other quantitative variables would be age of the house, number of bedrooms, size of the lot, and size of the garage, etc. (d)? Coefficient of determination = (0. 63)2 = 0. 397. This means that only about 39. 7% of the variability in the sales price of a house is explained by this regression model that only includes square footage as the explanatory variable. 4. 36? (a)? Given: Y = 90 + 48. 5X1 + 0. 4X2 where: [pic] If: Number of days on the road ( X1 = 5 and distance traveled ( X2 = 300 then: Y = 90 + 48. 5 ( 5 + 0. 4 ( 300 = 90 + 242. 5 + 120 = 452. 5 Therefore, the expected cost of the trip is $452. 50. (b)? The reimbursement request is much higher than predicted by the model. This request should probably be questioned by the accountant. (c)?A number of other variables should be included, such as: 1.? the type of travel (air or car) 2.? conference fees, if any 3.? costs of entertaining customers 4.? other transportation costs—cab, limousine, special tolls, or parking In addition, the correlation coefficient of 0. 68 is not exceptionally high. It indicates that the model explains approximately 46% of the overall variation in trip cost. This correlation coefficient would suggest that the model is not a particularly good one. 4. 37? (a, b) |Period |Demand |Forecast |Error |Running sum ||error| | | 1 |20 |20 |0. 00 |0. 00 |0. 00 | | 2 |21 |20 |1. 00 |1. 0 |1. 00 | | 3 |28 |20. 5 |7. 50 |8. 50 |7. 50 | | 4 |37 |24. 25 |12. 75 |21. 25 |12. 75 | | 5 |25 |30. 63 |–5. 63 |15. 63 |5. 63 | | 6 |29 |27. 81 |1. 19 |16. 82 |1. 19 | | 7 |36 |28. 41 |7. 59 |24. 41 |7. 59 | | 8 |22 |32. 20 |–10. 20 |14. 21 |10. 20 | | 9 |25 |27. 11 |–2. 10 |12. 10 |2. 10 | |10 |28 |26. 05 | 1. 95 |14. 05 | | | | | | |1. 95 | | | | | | | | | | | | | | | |MAD[pic]5. 00 | Cumulative error = 14. 05; MAD = 5? Tracking = 14. 05/5 ( 2. 82 4. 38? (a)? least squares equation: Y = –0. 158 + 0. 1308X (b)? Y = –0. 158 + 0. 1308(22) = 2. 719 million (c)? coefficient of correlation = r = 0. 966 coefficient of determination = r2 = 0. 934 4. 39 |Year X |Patients Y |X2 |Y2 |XY | |? 1 |? 36 | 1 |? 1,296 | 36 | |? 2 |? 33 | |? 1,089 | 66 | |? 3 |? 40 | 9 |? 1,600 |? 120 | |? 4 |? 41 |? 16 |? 1,681 |? 164 | |? 5 |? 40 |? 25 |? 1,600 |? 200 | |? 6 |? 55 |? 36 |? 3,025 |? 330 | |? 7 |? 60 |? 49 |? 3,600 |? 420 | |? 8 |? 54 |? 64 |? 2,916 |? 432 | |? 9 |? 58 |? 81 |? 3,364 |? 522 | |10 |? 61 |100 |? 3,721 |? 10 | |55 | | |478 | | |X |Y |Forecast |Deviation |Deviation | |? 1 |36 |29. 8 + 3. 28 ( ? 1 = 33. 1 |? 2. 9 |2. 9 | |? 2 |33 |29. 8 + 3. 28 ( ? 2 = 36. 3 |–3. 3 |3. 3 | |? 3 |40 |29. 8 + 3. 28 ( ? 3 = 39. 6 |? 0. 4 |0. 4 | |? 4 |41 |29. 8 + 3. 28 ( ? 4 = 42. 9 |–1. 9 |1. 9 | |? 5 |40 |29. 8 + 3. 28 ( ? 5 = 46. 2 |–6. 2 |6. 2 | |? 6 |55 |29. 8 + 3. 28 ( ? 6 = 49. 4 |? 5. 6 |5. 6 | |? 7 |60 |29. 8 + 3. 28 ( ? 7 = 52. 7 |? 7. 3 |7. 3 | |? |54 |29. 8 + 3. 28 ( ? 8 = 56. 1 |–2. 1 |2. 1 | |? 9 |58 |29. 8 + 3. 28 ( ? 9 = 59. 3 |–1. 3 |1. 3 | |10 |61 |29. 8 + 3. 28 ( 10 = 62. 6 |–1. 6 |1. 6 | | | | | | ( = | | | | | |32. 6 | | | | | |MAD = 3. 26 | The MAD is 3. 26—this is approximately 7% of the average number of patients and 10% of the minimum number of patients. We also see absolute deviations, for years 5, 6, and 7 in the range 5. 6–7. 3.The comparison of the MAD with the average and minimum number of patients and the comparatively large deviations during the middle years indicate that the forecast model is not exceptionally accurate. It is more useful for predicting general trends than the actual number of patients to be seen in a specific year. 4. 40 | |Crime |Patients | | | | |Year |Rate X |Y |X2 |Y2 |XY | |? 1 |? 58. 3 |? 36 |? 3,398. 9 |? 1,296 |? 2,098. 8 | |? 2 |? 61. 1 |? 33 |? 3,733. 2 |? 1,089 |? 2,016. 3 | |? 3 |? 73. |? 40 |? 5,387. 6 |? 1,600 |? 2,936. 0 | |? 4 |? 75. 7 |? 41 |? 5,730. 5 |? 1,681 |? 3,103. 7 | |? 5 |? 81. 1 |? 40 |? 6,577. 2 |? 1,600 |? 3,244. 0 | |? 6 |? 89. 0 |? 55 |? 7,921. 0 |? 3,025 |? 4,895. 0 | |? 7 |101. 1 |? 60 |10,221. 2 |? 3,600 |? 6,066. 0 | |? 8 |? 94 . 8 |? 54 |? 8,987. 0 |? 2,916 |? 5,119. 2 | |? 9 |103. 3 |? 58 |10,670. 9 |? 3,364 |? 5,991. 4 | |10 |116. 2 |? 61 |13,502. 4 |? 3,721 |? 7,088. 2 | |Column | |854. | | |478 | |Totals | | | | | | |months) |(Millions) |(1,000,000s) | | | | |Year |(X) |(Y) |X2 |Y2 |XY | |? 1 |? 7 |1. 5 |? 49 |? 2. 25 |10. 5 | |? 2 |? 2 |1. 0 | 4 |? 1. 00 |? 2. 0 | |? 3 |? 6 |1. 3 |? 36 |? 1. 69 |? 7. 8 | |? 4 |? 4 |1. 5 |? 16 |? 2. 25 |? 6. 0 | |? 5 |14 |2. 5 |196 |? 6. 25 |35. 0 | |? 6 |15 |2. 7 |225 |? 7. 9 |40. 5 | |? 7 |16 |2. 4 |256 |? 5. 76 |38. 4 | |? 8 |12 |2. 0 |144 |? 4. 00 |24. 0 | |? 9 |14 |2. 7 |196 |? 7. 29 |37. 8 | |10 |20 |4. 4 |400 |19. 36 |88. 0 | |11 |15 |3. 4 |225 |11. 56 |51. 0 | |12 |? 7 |1. 7 |? 49 |? 2. 89 |11. 9 | Given: Y = a + bX where: [pic] and (X = 132, (Y = 27. 1, (XY = 352. 9, (X2 = 1796, (Y2 = 71. 59, [pic] = 11, [pic]= 2. 26. Then: [pic] andY = 0. 511 + 0. 159X (c)?Given a tourist population of 10,000,000, the model predicts a ridership of: Y = 0. 511 + 0. 159 ( 10 = 2. 101, or 2,101,000 persons. (d)? If there are no tourists at all, the model predicts a ridership of 0. 511, or 511,000 persons. One would not place much confidence in this forecast, however, because the number of tourists (zero) is outside the range of data used to develop the model. (e)? The standard error of the estimate is given by: (f)? The correlation coefficient and the coefficient of determination are given by: [pic] 4. 42? (a)? This problem gives students a chance to tackle a realistic problem in business, i. e. , not enough data to make a good forecast.As can be seen in the accompanying figure, the data contains both seasonal and trend factors. [pic] Averaging methods are not appropriate with trend, seasonal, or other patterns in the data. Moving averages smooth out seasonality. Exponential smoothing can forecast January next year, but not farther. Because seasonality is strong, a naive model that students create on their own might be best. (b) One model might be: Ft+1 = At–11 That is forecastnext period = actualone year earlier to account for seasonality. But this ignores the trend. One very good approach would be to calculate the increase from each month last year to each month this year, sum all 12 increases, and divide by 12.The forecast for next year would equal the value for the same month this year plus the average increase over the 12 months of last year. (c) Using this model, the January forecast for next year becomes: [pic] where 148 = total monthly increases from last year to this year. The forecasts for each of the months of next year then become: |Jan. |29 | |July. |56 | |Feb. |26 | |Aug. |53 | |Mar. |32 | |Sep. |45 | |Apr. |35 | |Oct. |35 | |May. |42 | |Nov. |38 | |Jun. |50 | |Dec. |29 | Both history and forecast for the next year are shown in the accompanying figure: [pic] 4. 3? (a) and (b) See the following table: | |Actual |Smoothed | |Smoothed | | |Week |Value |Value |Forecast |Value |Forecast | |t |A(t) |Ft (( = 0. 2) |Err or |Ft (( = 0. 6)|Error | | 1 |50 |+50. 0 |? +0. 0 |+50. 0 |? +0. 0 | | 2 |35 |+50. 0 |–15. 0 |+50. 0 |–15. 0 | | 3 |25 |+47. 0 |–22. 0 |+41. 0 |–16. 0 | | 4 |40 |+42. 6 |? –2. 6 |+31. 4 |? +8. 6 | | 5 |45 |+42. 1 |? –2. 9 |+36. 6 |? +8. | | 6 |35 |+42. 7 |? –7. 7 |+41. 6 |? –6. 6 | | 7 |20 |+41. 1 |–21. 1 |+37. 6 |–17. 6 | | 8 |30 |+36. 9 |? –6. 9 |+27. 1 |? +2. 9 | | 9 |35 |+35. 5 |? –0. 5 |+28. 8 |? +6. 2 | |10 |20 |+35. 4 |–15. 4 |+32. 5 |–12. 5 | |11 |15 |+32. 3 |–17. 3 |+25. 0 |–10. 0 | |12 |40 |+28. 9 |+11. 1 |+19. 0 |+21. 0 | |13 |55 |+31. 1 |+23. 9 |+31. 6 |+23. 4 | |14 |35 |+35. 9 |? 0. 9 |+45. 6 |–10. 6 | |15 |25 |+36. 7 |–10. 7 |+39. 3 |–14. 3 | |16 |55 |+33. 6 |+21. 4 |+30. 7 |+24. 3 | |17 |55 |+37. 8 |+17. 2 |+45. 3 |? +9. 7 | |18 |40 |+41. 3 |? –1. 3 |+51. 1 |–11. 1 | |19 |35 |+41. 0 |? –6. 0 |+44. 4 |? –9. 4 | |20 |60 |+39. 8 |+20. 2 |+38. 8 |+21. 2 | |21 |75 |+43. 9 |+31. 1 |+51. 5 |+23. 5 | |22 |50 |+50. 1 |? –0. 1 |+65. 6 |–15. | |23 |40 |+50. 1 |–10. 1 |+56. 2 |–16. 2 | |24 |65 |+48. 1 |+16. 9 |+46. 5 |+18. 5 | |25 | |+51. 4 | |+57. 6 | | | | |MAD = 11. 8 |MAD = 13. 45 | (c)? Students should note how stable the smoothed values are for ( = 0. 2. When compared to actual week 25 calls of 85, the smoothing constant, ( = 0. 6, appears to do a slightly better job. On the basis of the standard error of the estimate and the MAD, the 0. 2 constant is better. However, other smoothing constants need to be examined. |4. 4 | | | | | | |Week |Actual Value |Smoothed Value |Trend Estimate |Forecast |Forecast | |t |At |Ft (( = 0. 3) |Tt (( = 0. 2) |FITt |Error | |? 1 |50. 000 |50. 000 |? 0. 000 |50. 000 | 0. 000 | |? 2 |35. 000 |50. 000 |? 0. 000 |50. 000 |–15. 000 | |? 3 |25. 000 |45. 500 |–0. 900 |44. 600 |–19. 600 | |? 4 |40. 000 |38. 720 |– 2. 076 |36. 644 | 3. 56 | |? 5 |45. 000 |37. 651 |–1. 875 |35. 776 | 9. 224 | |? 6 |35. 000 |38. 543 |–1. 321 |37. 222 |? –2. 222 | |? 7 |20. 000 |36. 555 |–1. 455 |35. 101 |–15. 101 | |? 8 |30. 000 |30. 571 |–2. 361 |28. 210 | 1. 790 | |? 9 |35. 000 |28. 747 |–2. 253 |26. 494 | 8. 506 | |10 |20. 000 |29. 046 |–1. 743 |27. 03 |? –7. 303 | |11 |15. 000 |25. 112 |–2. 181 |22. 931 |? –7. 931 | |12 |40. 000 |20. 552 |–2. 657 |17. 895 |? 22. 105 | |13 |55. 000 |24. 526 |–1. 331 |23. 196 |? 31. 804 | |14 |35. 000 |32. 737 |? 0. 578 |33. 315 | 1. 685 | |15 |25. 000 |33. 820 |? 0. 679 |34. 499 |? –9. 499 | |16 |55. 000 |31. 649 |? 0. 109 |31. 58 |? 23. 242 | |17 |55. 000 |38. 731 |? 1. 503 |40. 234 |? 14. 766 | |18 |40. 000 |44. 664 |? 2. 389 |47. 053 |? –7. 053 | |19 |35. 000 |44. 937 |? 1. 966 |46. 903 |–11. 903 | |20 |60. 000 |43. 332 |? 1. 252 |44. 584 |? 15. 416 | |21 |75. 00 0 |49. 209 |? 2. 177 |51. 386 |? 23. 614 | |22 |50. 000 |58. 470 |? 3. 94 |62. 064 |–12. 064 | |23 |40. 000 |58. 445 |? 2. 870 |61. 315 |–21. 315 | |24 |65. 000 |54. 920 |? 1. 591 |56. 511 | 8. 489 | |25 | |59. 058 |? 2. 100 |61. 158 | | To evaluate the trend adjusted exponential smoothing model, actual week 25 calls are compared to the forecasted value. The model appears to be producing a forecast approximately mid-range between that given by simple exponential smoothing using ( = 0. 2 and ( = 0. 6.Trend adjustment does not appear to give any significant improvement. 4. 45 |Month |At |Ft ||At – Ft | |(At – Ft) | |May |100 |100 | 0 | 0 | |June | 80 |104 |24 |–24 | |July |110 | 99 |11 |11 | |August |115 |101 |14 |14 | |September |105 |104 | 1 | 1 | |October |110 |104 |6 |6 | |November |125 |105 |20 |20 | December |120 |109 |11 |11 | | | | |Sum: 87 |Sum: 39 | |4. 46 (a) | |X |Y |X2 |Y2 |XY | | |? 421 |? 2. 90 |? 177241 | 8. 41 |? 1220. 9 | | |? 377 | ? 2. 93 |? 142129 | 8. 58 |? 1104. 6 | | |? 585 |? 3. 00 |? 342225 | 9. 00 |? 1755. 0 | | |? 690 |? 3. 45 |? 476100 |? 11. 90 |? 2380. 5 | | |? 608 |? 3. 66 |? 369664 |? 13. 40 |? 2225. 3 | | |? 390 |? 2. 88 |? 52100 | 8. 29 |? 1123. 2 | | |? 415 |? 2. 15 |? 172225 | 4. 62 | 892. 3 | | |? 481 |? 2. 53 |? 231361 | 6. 40 |? 1216. 9 | | |? 729 |? 3. 22 |? 531441 |? 10. 37 |? 2347. 4 | | |? 501 |? 1. 99 |? 251001 | 3. 96 | 997. 0 | | |? 613 |? 2. 75 |? 375769 | 7. 56 |? 1685. 8 | | |? 709 |? 3. 90 |? 502681 |? 15. 21 |? 2765. 1 | | |? 366 |? 1. 60 |? 133956 | 2. 56 | 585. 6 | | |Column |6885 | |36. 6 | | | |totals | | | | | |January |400 |— |— | — |— | |February |380 |400 |— |20. 0 |— | |March |410 |398 |— |12. 0 |— | |April |375 | 399. 2 |396. 67 |24. 2 |21. 67 | |May |405 | 396. 8 |388. 33 |8. 22 |16. 67 | | | | |MAD = | |16. 11| | |19. 17| | (d)Note that Amit has more forecast observations, while Barbara’s moving average does not start until month 4. Also note that the MAD for Amit is an average of 4 numbers, while Barbara’s is only 2. Amit’s MAD for exponential smoothing (16. 1) is lower than that of Barbara’s moving average (19. 17). So his forecast seems to be better. 4. 48? (a) |Quarter |Contracts X |Sales Y |X2 |Y2 |XY | |1 |? 153 |? 8 |? 23,409 |? 64 |? 1,224 | |2 |? 172 |10 |? 29,584 |100 |? 1,720 | |3 |? 197 |15 |? 38,809 |225 |? 2,955 | |4 |? 178 |? 9 |? 31,684 |? 81 |? 1,602 | |5 |? 185 |12 |? 34,225 |144 |? 2,220 | |6 |? 199 |13 |? 39,601 |169 |? 2,587 | |7 |? 205 |12 |? 42,025 |144 |? ,460 | |8 |? 226 |16 |? 51,076 |256 |? 3,616 | |Totals | | 1,515 | | |95 | b = (18384 – 8 ( 189. 375 ( 11. 875)/(290,413 – 8 ( 189. 375 ( 189. 375) = 0. 1121 a = 11. 875 – 0. 1121 ( 189. 375 = –9. 3495 Sales ( y) = –9. 349 + 0. 1121 (Contracts) (b) [pic] 4. 49? (a) |Method ( Exponential Smoothing | | | |0. 6 = ( | | | |Year |Deposits (Y) |Forecast ||E rror| |Error2 | | 1 |? 0. 25 |0. 25 |0. 00 |? 0. 00 | | 2 |? . 24 |0. 25 |0. 01 |? 0. 0001 | | 3 |? 0. 24 |0. 244 |0. 004 |? 0. 0000 | | 4 |? 0. 26 |0. 241 |0. 018 |? 0. 0003 | | 5 |? 0. 25 |0. 252 |0. 002 |? 0. 00 | | 6 |? 0. 30 |0. 251 |0. 048 |? 0. 0023 | | 7 |? 0. 31 |0. 280 |0. 029 |? 0. 0008 | | 8 |? 0. 32 |0. 298 |0. 021 |? 0. 0004 | | 9 |? 0. 24 |0. 311 |0. 071 |? 0. 0051 | |10 |? 0. 26 |0. 68 |0. 008 |? 0. 0000 | |11 |? 0. 25 |0. 263 |0. 013 |? 0. 0002 | |12 |? 0. 33 |0. 255 |0. 074 |? 0. 0055 | |13 |? 0. 50 |0. 300 |0. 199 |? 0. 0399 | |14 |? 0. 95 |0. 420 |0. 529 |? 0. 2808 | |15 |? 1. 70 |0. 738 |0. 961 |? 0. 925 | |16 |? 2. 30 |1. 315 |0. 984 |? 0. 9698 | |17 |? 2. 80 |1. 906 |0. 893 |? 0. 7990 | |18 |? 2. 80 |2. 442 |0. 357 |? 0. 278 | |19 |? 2. 70 |2. 656 |0. 043 |? 0. 0018 | |20 |? 3. 90 |2. 682 |1. 217 |? 1. 4816 | |21 |? 4. 90 |3. 413 |1. 486 |? 2. 2108 | |22 |? 5. 30 |4. 305 |0. 994 |? 0. 9895 | |23 |? 6. 20 |4. 90 |1. 297 |? 1. 6845 | |24 |? 4. 10 |5. 680 |1. 580 |? 2. 499 | |25 |? 4. 50 |4. 732 |0. 232 |? 0. 0540 | |26 |? 6. 10 |4. 592 |1. 507 |? 2. 2712 | |27 |? 7. 0 |5. 497 |2. 202 |? 4. 8524 | |28 |10. 10 |6. 818 |3. 281 |10. 7658 | |29 |15. 20 |8. 787 |6. 412 |41. 1195 | (Continued) 4. 49? (a)? (Continued) |Method ( Exponential Smoothing | | | |0. 6 = ( | | | |Year |Deposits (Y) |Forecast ||Error| |Error2 | |30 |? 18. 10 |12. 6350 | 5. 46498 |29. 8660 | |31 |? 24. 10 |15. 9140 |8. 19 |67. 01 | |32 |? 25. 0 |20. 8256 |4. 774 |22. 7949 | |33 |? 30. 30 |23. 69 | 6. 60976 |43. 69 | |34 |? 36. 00 |27. 6561 | 8. 34390 |69. 62 | |35 |? 31. 10 |32. 6624 | 1. 56244 | 2. 44121 | |36 |? 31. 70 |31. 72 | 0. 024975 | 0. 000624 | |37 |? 38. 50 |31. 71 |6. 79 |? 46. 1042 | |38 |? 47. 90 |35. 784 |12. 116 |146. 798 | |39 |? 49. 10 |43. 0536 |6. 046 |36. 56 | |40 |? 55. 80 |46. 814 | 9. 11856 | 83. 1481 | |41 |? 70. 10 |52. 1526 |17. 9474 |322. 11 | |42 |? 70. 90 |62. 9210 | 7. 97897 |63. 66 | |43 |? 79. 10 |67. 7084 |11. 3916 |129. 768 | |44 |? 94. 0 0 |74. 5434 | 19. 4566 | 378. 561 | |TOTALS | |787. 30 | | | |150. 3 | | |1,513. 22 | |AVERAGE | 17. 8932 | | 3. 416 | 34. 39 | | | | |(MAD) |(MSE) | |Next period forecast = 86. 2173 |Standard error = 6. 07519 | Method ( Linear Regression (Trend Analysis) | |Year |Period (X) |Deposits (Y) |Forecast |Error2 | |? 1 |? 1 |0. 25 |–17. 330 |309. 061 | |? 2 |? 2 |0. 24 |–15. 692 |253. 823 | |? 3 |? 3 |0. 24 |–14. 054 |204. 31 | |? 4 |? 4 |0. 26 |–12. 415 |160. 662 | |? 5 |? 5 |0. 25 |–10. 777 |121. 594 | |? 6 |? 6 |0. 30 |? –9. 1387 |89. 0883 | |? 7 |? 7 |0. 31 |? –7. 50 |61. 0019 | |? 8 |? 8 |0. 32 |? –5. 8621 |38. 2181 | |? |? 9 |0. 24 |? –4. 2238 |19. 9254 | |10 |10 |0. 26 |? –2. 5855 |8. 09681 | |11 |11 |0. 25 |? –0. 947 |1. 43328 | |12 |12 |0. 33 |? 0. 691098 |0. 130392 | |13 |13 |0. 50 |? 2. 329 |3. 34667 | |14 |14 |0. 95 |? 3. 96769 |9. 10642 | |15 |15 |1. 70 |? 5. 60598 |15. 2567 | |16 |16 |2. 30 |? 7. 24 427 |24. 4458 | |17 |17 |2. 0 |? 8. 88257 |36. 9976 | |18 |18 |2. 80 |? 10. 52 |59. 6117 | |19 |19 |2. 70 |? 12. 1592 |89. 4756 | |20 |20 |3. 90 |? 13. 7974 |97. 9594 | |21 |21 |4. 90 |? 15. 4357 |111. 0 | |22 |22 |5. 30 |? 17. 0740 |138. 628 | |23 |23 |6. 20 |? 18. 7123 |156. 558 | |24 |24 |4. 10 |? 20. 35 |264. 083 | |25 |25 |4. 50 |? 21. 99 |305. 62 | |26 |26 |6. 10 |? 23. 6272 |307. 203 | |27 |27 |7. 70 |? 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1 |0. 40 |? 0. 25 |–12. 198 |? 12. 4482 |? 154. 957 | |? 2 |0. 40 |? 0. 24 |–12. 198 |? 12. 4382 |? 154. 71 | |? 3 |0. 50 |? 0. 24 |–10. 839 |? 11. 0788 |? 122. 740 | |? 4 |0. 70 |? 0. 26 |–8. 12 | 8. 38 | 70. 226 | |? 5 |0. 90 |? 0. 25 |–5. 4014 | 5. 65137 | 31. 94 | |? 6 |1. 00 |? 0. 30 |–4. 0420 | 4. 342 | 18. 8530 | |? 7 |1. 40 |? 0. 31 |? 1. 39545 | 1. 08545 | 1. 17820 | |? 8 |1. 70 |? 0. 32 |? 5. 47354 | 5. 5354 | 26. 56 | |? 9 |1. 30 |? 0. 24 |? 0. 036086 | 0. 203914 | 0. 041581 | |10 |1. 20 |? 0. 2 6 |–1. 3233 | 1. 58328 | 2. 50676 | |11 |1. 10 |? 0. 25 |–2. 6826 | 2. 93264 | 8. 60038 | |12 |0. 90 |? 0. 33 |–5. 4014 | 5. 73137 | 32. 8486 | |13 |1. 20 |? 0. 50 |–1. 3233 | 1. 82328 | 3. 32434 | |14 |1. 20 |? 0. 95 |–1. 3233 | 2. 27328 | 5. 16779 | |15 |1. 20 |? 1. 70 |–1. 3233 | 3. 02328 | 9. 14020 | |16 |1. 60 |? 2. 30 |? 4. 11418 | 1. 81418 | 3. 9124 | |17 |1. 50 |? 2. 80 |? 2. 75481 | 0. 045186 | 0. 002042 | |18 |1. 60 |? 2. 80 |? 4. 11418 | 1. 31418 | 1. 727 | |19 |1. 70 |? 2. 70 |? 5. 47354 | 2. 77354 | 7. 69253 | |20 |1. 90 |? 3. 90 |? 8. 19227 | 4. 29227 | 18. 4236 | |21 |1. 90 |? 4. 90 |? 8. 19227 | 3. 29227 | 10. 8390 | |22 |2. 30 |? 5. 30 |13. 6297 | 8. 32972 | 69. 3843 | |23 |2. 50 |? 6. 20 |16. 3484 |? 10. 1484 |? 102. 991 | |24 |2. 80 |? 4. 10 |20. 4265 |? 16. 3265 |? 266. 56 | |25 |2. 90 |? 4. 50 |21. 79 |? 17. 29 |? 298. 80 | |26 |3. 40 |? 6. 10 |28. 5827 |? 22. 4827 |? 505. 473 | |27 |3. 80 |? 7. 70 |34. 02 |? 26. 32 |? 6 92. 752 | |28 |4. 10 |10. 10 |38. 0983 |? 27. 9983 |? 783. 90 | |29 |4. 00 |15. 20 |36. 74 |? 21. 54 |? 463. 924 | |30 |4. 00 |18. 10 |36. 74 |? 18. 64 |? 347. 41 | |31 |3. 90 |24. 10 |35. 3795 |? 11. 2795 |? 127. 228 | |32 |3. 80 |25. 60 |34. 02 | 8. 42018 | 70. 8994 | |33 |3. 0 |30. 30 |34. 02 | 3. 72018 | 13. 8397 | |34 |3. 70 |36. 00 |32. 66 | 3. 33918 | 11. 15 | |35 |4. 10 |31. 10 |38. 0983 | 6. 99827 | 48. 9757 | |36 |4. 10 |31. 70 |38. 0983 | 6. 39827 |? 40. 9378 | |37 |4. 00 |38. 50 |36. 74 | 1. 76 | 3. 10146 | |38 |4. 50 |47. 90 |43. 5357 | 4. 36428 | 19. 05 | |39 |4. 60 |49. 10 |44. 8951 | 4. 20491 | 17. 6813 | |40 |4. 50 |55. 80 |43. 5357 |? 12. 2643 |? 150. 412 | |41 |4. 60 |70. 10 |44. 951 |? 25. 20 |? 635. 288 | |42 |4. 60 |70. 90 |44. 8951 |? 26. 00 |? 676. 256 | |43 |4. 70 |79. 10 |46. 2544 |? 32. 8456 |1,078. 83 | |44 |5. 00 |94. 00 |50. 3325 |? 43. 6675 |1,906. 85 | |TOTALS | | | |451. 223 |9,016. 45 | |AVERAGE | | | |? 10. 2551 |? 204. 92 | | | | | |? 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Childless Couples - Research Paper Example This essay would further revolve around couples with and without children and would provide the advantages and disadvantages of not having children. Couples who do not have children believe that they are better off without them as they can be happier this way. The advantages of not having children revolve around the expenses and nature of the child as he is born in this world. Nurturing and taking care of a child requires a lot of effort and this is considered stressful by many of the couples. Parenting requires effort in looking forward to every need of the child which becomes difficult for some of the couples (Chapati 2009). A professor of psychology states that immediately after marriage the couples get quite happy but later onwards after the couples bear a child the level of contentment drops. But he also asserts that married people are happier than the unmarried people because of the closeness that is involved in the relationship. He states "Figures show that married people are in almost every way happier than unmarried people – whether they are single, divorced, cohabiting". According to the professor when a couple is expecting a child the level of happiness rises high enough but as soon as the child is born the level of happiness descends. ... The psychologists analyze as to how couples get unhappy in these instances of child bearing (Devlin 2008). Another study carried out in Britain lately by British attitudes shows that married couples without children were the most happiest of all in terms of relationships. The research was backed by the Economic and Social Research Council and it showed that older couples were more discontent with their marriage than their young counterparts. The research also found that the couples became unhappy when their child was in a pre-school age. However after the child grew up into adolescence the relationship was healthy enough to be controlled. This clearly shows that child bearing couples have to face many problems unlike their counterparts who do not have to face the problems associated with child bearing (Martin 2011). A study also found that not bearing a child is also associated with better diet in childless couples. The study also found that the couples who had children ate a less he althy diet than their counterparts. The study in the agricultural economics found that the childless couples tend to consume more fruits and healthy food than their counterparts. The amount of meat consumed by the childless couples was also right whereas the ones with child consumed more of the dairy products. A professor from the University of Reading stated that â€Å"For whatever reason, the social dynamic in a household with children makes the diet on average more unhealthy.† This clearly shows that the childless couples have an edge in terms of diet over their counterparts. This again is considered as an advantage for those who do not bear a child (Bakalar

Purposes of Traditions or Rituals Essay Example | Topics and Well Written Essays - 1250 words

Purposes of Traditions or Rituals - Essay Example The meaning of a tradition as derived from Shah-Kazemi Reza is a belief or an object passed down within a society maintained in the present but originated from the past (41). Some common practical examples of traditions include holidays and clothes with social meaning like lawyer wigs or military officer spurs. A ritual on the other hand is a solemn or religious ceremony comprising of performed series in accordance with prescribed order. Traditions are places of comfort; touchstones as are in good times while during difficulties they are a place of mooring. In a Reith lecture said a tradition has several core elements. To begin with, it has a ceremonial ritual or ritualistic behavior, it involves a group of people; social in nature and it is collective and finally it has traditional guardians such as historians that have access to knowledge or the truth of tradition’s sacred rituals.... Traditions and rituals extent presence and function to the Roman Catholic Church. The church has â€Å"Catholic Mass† a service in a divine and consecrated area by an authorized minister (Jones 60). That central act is a sacrament of the performance of Eucharist. Such is an example of weekly church service whereby the reason for this regular ritual is much the same. In addition, a tradition or a ritual is purposeful in families since it promotes a sense of belonging, sense of purpose, identity, connection, and acts as a role model and develops stability and continuity in a fast-paced and hectic world. While a ritual is like a spiritual instruction book, complete with blueprints, it can have a specific reason for any one, or all people assembled to perform it. They include the rite of healing, passage or a ritual focused on helping crops to grow. Arguably, some critics have gone ahead to analyze the presence of Christmas on the twenty-fifth day of December every year. Some say it is a ritual commemorating the birth of Jesus. Others, although irreligious people, according to Skeptical play entitled purpose of a ritual, do not really get the function of a ritual or tradition (Strathern 14). After he carefully examined Jesus’ life and words, he came up with a startling conclusion and altered his course of life then posed a question, â€Å"could Jesus have been just a great teacher?† Scholarly arguing Christmas celebrations are a ritual to some while others do not even recognize its presence like those who go to church on Saturday. Another old tradition is that of the â€Å"best man† in a wedding. His purpose in old days was to; because people fought for brides, do the fighting while the couple said their vows Ritual actions are hardly limited to

Self-Evaluation paper Assignment Example | Topics and Well Written Essays - 1000 words

Self-Evaluation paper - Assignment Example Following discussion seeks to prove my point on grounds of logical evidence which I will be using to advance my interests. I have always remained very interested in the English language. It has always sounded very beautiful to me which served to augment my interest in majoring in this language despite not being a native speaker. I have written many essays during the course of this semester which served to refine my writing skills and boost my confidence. I perfectly well remember the traumatic state of cognitive dissonance I found myself in when I first landed in this foreign country. My knowledge of the English language used to be quite succinct back then as a result of which I was not very fluent in speaking this language let alone writing essays laced with all possible kinds of literary embellishments. I encountered many hurdles and went through countless heavy experiences, but what I gained as a result will continue to benefit me throughout life. I am of course speaking about what I learned during this semester and how in many different ways it helped me in becoming an accomplished writer who takes pride in his skills. It is not my intention to use this medium to build my reputation as a person who is cocksure of his credentials or performance and has become excessively haughty as a result. Rather, I am fully aware that despite amassing much valuable experience, I am still not an above average student. However, given the fact that I am international student and English is not my native language, it would not be empty mockery to suggest that I have put my skills to good use. These skills I acquired during the course of this semester which enabled me to become free from the clutches of some serious deficiencies. After deep analysis of all I went through this semester and all I gained in the process more importantly, it is safe to assume that this grade B is the grade I rightfully deserve. I do not want my professor to see me as a

History of Cities and their Architecture Essay Example | Topics and Well Written Essays - 2500 words

History of Cities and their Architecture - Essay Example Philip Johnson developed one of the chairs for his collection. Mies van der Rohe designed Barcelona Pavilion, which was developed from German Pavilion, during international exposition that was in Barcelona Spain. It was constructed with the aim of demonstrating modern movement of architecture of the world (Curtis, 2008). The building was originally named a Germany pavilion, as it was the expression of Germany after the First World War to indicate the culture of the country that is still rooted in the classical history. The pavilion was built with an aim of hosting King Alphonso XIII of Spain and officials from Germany during the time of exposition (Curtis, 2008). As compared to other buildings at the exposition, Mies understood that work he did as just a building and he did not aim in housing art or any sculpture. The building was just designed to place tranquility and escape from exposition, which was then to transform the pavilion into an inhabitable statue. By raising the pavilion on a plinth in conjunction with the narrow profile of the site, the Barcelona Pavilion has a low horizontal orientation that is accentuated by the low flat roof that appears to float over both the interior as well as the exterior. The furniture of the building also exhibits the duality of modernism and classicism. The now iconic Barcelona chair and ottoman were designed specifically for the Barcelona Pavilion. The Barcelona building served as a reception place for the King and Queen of Spain. After the exhibition in which the pavilion intended was closed, the resembling of the pavilion was in 1930. As time went, the pavilion became a key point not only to Mies but also to other architectures that operated in the 20th century. In 1980, there was setting of project by Oriol Bohigas who was the head of planning department to ensure that there was

Validity, Reliability, and Accuracy Assignment Example | Topics and Well Written Essays - 1250 words

Validity, Reliability, and Accuracy - Assignment Example This is essential not only to be fair to the student but also get an accurate representative of the class as a whole. In addition, writing such an assessment upfront will alleviate the potential for problems down the road. Students may, for example, become frustrated if an exam is perceived to be unfair. Much time may be spent after the exam with students questioning the very integrity of the assessment itself. In addition, if an exam does not properly test student comprehension about the given material, the teacher may reach a false assumption about the performance of the class, and thus their own teaching as well. With these aims in mind, the intent of this paper is to examine the validity and reliability of the hypothetical business management exam given in the preceding two pages. Validity When considering whether or not a teacher should be concerned over the poor performance shown by their students on a particular exam, one should first look at the assessment itself (Kubiszyn & Borich, 2013, p. 326). Exams need to be valid before their results can really be accepted. Simply because the class, on average, received a failing grade on an exam does not, in itself, indicate that they did not comprehend the material. Upon analyzing the exam, the teacher may discover certain problems with the test that make it invalid in the first place. It could well happen that the teacher re-writes a valid exam, gives it to the same set of students, and they all perform marvelously. For this reason, and others, the validity of any given assessment must be judged before any results on the part of the students are considered and recorded. One way to begin testing the validity of an exam is to consider the grade level of the material. If the exam is given to third... Validity, Reliability, and Accuracy When considering whether or not a teacher should be concerned over the poor performance shown by their students on a particular exam, one should first look at the assessment itself (Kubiszyn & Borich, 2013, p. 326). Exams need to be valid before their results can really be accepted. Simply because the class, on average, received a failing grade on an exam does not, in itself, indicate that they did not comprehend the material. According to Kubiszyn and Borich (2013), â€Å"The reliability of a test refers to the consistency with which it yields the same rank for individuals who take the test more than once† (p. 338). The indication here is that a student within a given class should rank in nearly the same place every time if the same exam is given to the same class. According to Kubiszyn and Borich (2013), â€Å"No test measures perfectly, and many tests fail to measure as well as we would like them to† (p. 348). The key is to realize that there will almost be some level of error in an exam, but the teacher must work hard to minimize that error to the greatest degree possible. Finally, there could be an error in scoring. This is particularly important to monitor if a human scores the exam. For this exam, the teacher can eliminate this error, to a great extent, by not scoring the exam when they are tired or in a rush. It is advisable to score an exam in batches, rather than all at once, to ensure that fatigue does no impact the marking of each response.

How should the state of Colorado generate funds for Higher Education Essay

How should the state of Colorado generate funds for Higher Education - Essay Example The state of the education system in the state is regarded as one of the most productive in the nation. In addition to that, it is a leader in the development of green technologies. The state promotes research initiatives and it is a major contributor to research in different areas. According to a report, the economic consequences of research institutions in the state are incredible. The recent reports show for the University of Colorado and Colorado State University show that each$1 of state general fund produces $13.2 of additional economic activity, therefore, it is imperative for the government to increase the general fund for Colorado for the interest of the country.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The reasons for the scarcity of funds in the State of Colorado despite its economic significance are several folds. An analysis of the root cause of the problem can provide us valuable suggestions for how to bridge the gap between the supply and demand of funds. Firstly, the higher education crisis is triggered due to the fact that Colorado’s system has failed to meet the demand created by demographic shifts. The most growing ethnic minority, Latinos, are underrepresented in the higher education system. In addition to that, the state imports a significant proportion of the population with postsecondary education while failing to accommodate the needs of its current citizens. The institutions in Colorado significantly differ in enrollment of low-income students as compared to the income level of the counties they serve. Finally, the state ranks at the lowest on the basis of state funding per FTE.

Recent Salary top ups Controversies in Ireland Essay Example for Free

Recent Salary top ups Controversies in Ireland Essay Of late, the issue concerning salary has been very controversial in Ireland. Early this year, some controversies relating to salary top-ups at charities have erupted in Ireland leading to a total damage threatening the sector. As a result of these controversies, few issues which are hazardous to the sector have emerged. Researchers and stakeholders have been worried by issues such as duration and the extent to which these issues will last and their relative impacts to various charities. There are some charities in the country specifically identified as donations or other funding sources that are used in improving executives’ salaries. As a result of these issues, majority of innocent bystanders are likely to experience a negative impact based on guilt by association. The question that majority are asking themselves is, what the future holds and how they can improve it. The research paper is mainly structured into two main categories that are used in describing the structure of the project. The first part involves the research part of the project. While the second part captures the application involving techniques and the methods used when carrying out the research. However, the first part enhances the researcher’s understanding on the recent controversies relating to salary top-ups at charities in Ireland and the emerging issues. It comprises of research definition, strategy, design and methodology used to provide a clear understanding of the report. The second part covers the broader part of the report taking the application approach. This part includes the sampling methods applied, instruments used in the research, the proposed data analysis techniques, the budget and timeline for the research estimates. Since the start of the year 2014 and slightly there before, the issue relating to top-ups salary controversy has rampantly emerged in Ireland. The issue concerning the recent controversies made majority of Irish people to become less likely to donate to charity. This is according to the latest report of Ipsos, Irish times and MRBI opinion poll. The poll also discovered that voting age population brought a total opposition. They are opposing the practice of using donations in paying top-ups of the executives’ salaries in the charity sector (Scarrow 2009: pp.193-210). The Irish government discovered that this move will negatively affect the sector and cause a negative experience to the innocent bystanders. Majority of citizens are unwilling to support the sector and thus threatening to cancel their donations. The call for yhe research project was to address the issue and discover its future prosperity and strategize on how to improve it. 1. Research definition                     Research comprises of an undertaken creative work on a systematic basis with an aim of increasing the stock of knowledge (Valbuena 2009: p.27). It includes the knowledge of a given scenario, society and culture, and the use of such knowledge to devise new applications. It is used to confirm or establish facts, reaffirm the previous work results, solve existing or new problems and support theorems. A research project can take the form of an expansion on past field work. Research takes different forms such as scientific research, research in the humanities, and artistic research. Scientific research involves application of scientific methods to harness curiosity. It gives scientific theories and information that explains properties and the nature of a given scenario. Research concerned with humanities involves methods such as semiotics and hermeneutics. It a form of research that explores details and issues surrounding a scenario, but not searching for an ultimate correct answer to a question. Artistic research also referred to as ‘practice-based research’ takes form when creative work is put into consideration both the research object and the research itself. The recent controversies in Ireland concerning salary top-ups are humanitarian issues. Research in the humanities is the best form of research used as it entails details and issues surrounding a scenario, but not giving a specific answer to a question. Other methods were excluded on the basis that they are not based on exploring a scenario. Through the research carried out, it was discovered that 96% of the respondents opposed the use of donations to top-up the salaries of the executives. The remaining had different stands as 2% felt it was acceptable while the other 2% had no opinion. The research spanned all regions, classes and party affiliations. On the issue concerning charity, 69% withdrew their likelihood of donating, 23% argued that it will make no difference, 4% found the question irrelevant as they do not contribute and 4% had no opinion. There was a similar response from people in all regions, classes, and supporters of groupings and political parties. The research was con ducted for two days using a representative sample of 1,000 voters from all constituencies. 1.1 Drop in donations                     The research confirmed that Ireland fundraising professionals were claiming that there was a drop of 40% in the charity donations. This was as a result of top-up payments controversy (Harvey 2012: pp.2008-2012). The umbrella professional fundraisers group claimed that they were constantly receiving phone calls from people who wanted to cancel their donations. Anne Hanniffy the HSE Chief Executive Officer, argued that salaries top-ups using donations was having a devastating effect on the sector. She claimed that revelations were a â€Å"million miles† from experiences and activities of most organizations, but it revealed that all charities were tarred using the same brush. This scenario brings the most serious period that the Irish non-profit sector is facing. Organizations are extremely concerned that people who are least able to survive without their support like disabled people, sick children and needy families will be most affected by the existing crisis. Despite the organizational opinions concerning the existing crisis of salary top-ups, Health Service Executives (HSE) and other organizations called upon the government to address the issue (Moran 2012: pp.137-147). Mr. Bell also warned the government that if this issue of pay top-ups was not addressed on equitable basis, it would bring a lot of challenges when trying to address different reforms. Different arguments has risen in Ireland concerning the controversial issue of salary top-ups using donations. The head of Ireland fundraising claimed that HSE used just a small portion of charities to meet its executives salary. She argued that top-up payments were not an issue as they totally relied on fundraising. She also claimed that it was difficult to reveal whether the recent surrounding controversy will have an effect to the sector. Central Remedial Clinic (CRC) confirmed that they have been recently using public donations to top-up their executives’ salaries. They are given financial support by a separate company known as supporters and friends of Central Remedial Clinic. 2. Research strategy                     A research strategy refers to a plan of action that gives direction to a person’s effort, thus enabling one to conduct a systematic research (Denzin 2010: pp.1-28). It involves discovering new ideas, thinking actively concerning the ideas and working with them. On a research strategy, a researcher may use the existing information and draw up his or her own conclusions, integrate and synthesize original ideas concerning the current scenario. Research strategies are of different forms such as non experimental that has no explicit manipulation, and experimental which manipulates some factors of a given issue. The Irish scenario applies experimental strategy as there are some factors surrounding the situation. They need to be manipulated and come with solutions to the existing issues. Non experimental is excluded on the basis that it does not allow factor manipulation. In a labor party national conference held at Killarney, social protection minis ter’s daughter Ms Burton revealed that donated funds used by CRC to top-up salaries was â€Å"extremely disturbing† (Edwards 2009: pp.595-615). During the meeting, Ms Burton demanded some issues to be made clear. Some of the issues to be manipulated include organizations to come out and clarify the disclosed issues, CRC to provide its fundraising details and their addition sources of income, lastly CRC was expected to give an account of how they have spent that money. She also emphasized that the public needed an assurance that their money generously donated to charities were used for the correct purpose. The internal audit carried out last year concerning HSE revealed that almost â‚ ¬250, 000 was spent annually (Crilly 2013: p.8). The money was used to cater for allowances of six senior executives, in addition to their state-funded salaries. These allowances utilized by the CRC were termed as unauthorized and that they have breached the pay policy of the public sector. The clinic has also been accused of misusing the funds donated to vulnerable adults and children. The company had â‚ ¬ 14 million in total funds at the end of year 2011 when it stopped giving services to the less privileged in the society. Instead of directing the money to the children and adult care, the clinic invested the money on capital projects. On the issue regarding salaries of the senior organizational members, the clinic revealed that since 2009, they agreed with HSE to increase the salaries of 9 individuals at the management level. 3. Research methodology and design                     Methodology refers to a systematic, theoretical analysis of the applied methods to a field of study (Eiben 2012: pp.582-587). It, typically and encompasses concepts like theoretical model, paradigm, phases and qualitative or quantitative techniques. It is not set to provide solutions but to provide a theoretical underpinning for elaborating the best method can be used. On the other hand, research design is the overall strategy chosen to integrate different study components in a logical and a coherent way (Parahoo 2010: p.142). It ensures effective ways of addressing the research problem. It is the blueprint for the measurement, collection and data analysis. Research design is broadly categorized into descriptive research, exploratory research, Qualitative research and non-experimental research. 3.1 Exploratory research                     Polit et al (2011: p.19) argued that explorative research is carried out when investigating a new area or when a little information is known about the area of interest. It is used to investigate nature of a phenomenon and other related factors. 3.2 Descriptive research                     According to Grove and Burns (2009: p.201), it is a form of research designed to reveal a picture of a given scenario as it happens naturally. It can be used to justify recent practices, make judgments and develop theories. 3.3 Qualitative research                     According to Grove and Burns (2009: p.19), it is a systematic subjective approach applied to describe situations and life experiences. It is also a form of social enquiry focusing on how people make and interpret sense of their experience and their living world. 3.4 Non-experimental research                     It is used in studies with an aim of describing a situation where it is unethical for independent variables to be manipulated (Polit 2009: p.178). It is a suitable research of studying people in nursing sector. The research project concerning the recent scenario in Ireland applied exploratory research. It is used by researchers when they want to produce hypotheses of what is happening in a situation. The recent controversies concerning salary top-up in Ireland needs to be explored. This will minimize the existing misappropriation of funds between organizations and their senior management. Some of the organizations like CRC have been misusing donations contributed to help vulnerable people in the society to other projects. The other research designs were excluded by the fact that the scenario required exploration but not coming up with a specific answer. 4. Sampling methods                     They are classified as either non probability or probability. In a probability case, each item of the population has a non-zero probability of being selected. It involves random sampling, stratified sampling, and systematic sampling. Non probability sampling includes convenience sampling, judgment sampling, snowball sampling and quota sampling. The research based on the recent controversies in Ireland took a stratified sampling. In this form, a subset of population is selected who are believed to have at least one characteristic in common. The research was carried out in specific areas where people with similar characteristics are expected to disclose certain information. For instance, in the labor party national conference held at Killarney, CRC senior management were ordered to disclose their sources of finance and money obtained through donations. They were also to give an account of how they have spent that money. Other alternatives were excluded as the scenario involved a specific group but not the whole population. 5. Instruments used in the research                     An instrument refers to a generic term used by researchers for a measurement device that may be in form of a test, survey, questionnaire, interview, a set of guidelines for observation, or a research tool (Voss 2012: pp.195-219). In the Ireland scenario of salaries top-ups controversies, some different research instruments have been put into consideration. During the background research, questionnaires and interview were applied when obtaining people’s opinions concerning the recent issue. An interview was also carried out when CRC senior management was requested to give an account of how they have spent donated money. 6. Proposed techniques in data analysis                     Data analysis refers to a process of cleaning, inspecting, transforming, and data modeling. This is carried out with an aim of discovering useful information, coming up with conclusions, and supporting the process of decision making (Gorski 2009: p.759). It is a multiple approaches and facets, encompassing different techniques under a variety of names. Data analysis is done using two major techniques namely univariate and multivariate analysis. Multivariate analysis is the proposed technique for the research project. This technique gives a wider variety of opinions as it involves multiple measurements. Univariate is excluded on the bases that it is focused on a single variable and the research involves different variables. 7. Estimated budget and timeline for the research                     Every research project needs to be economical and timely. The above research is estimated to take at least 3-4months. This will facilitate quick actions being taken to address the issues surrounding the charity sector before it collapses. The project will be more economical considering the existing financial issues in the countries. Considering all matters at hand, the project has an estimated budget of â‚ ¬1.2 million. In conclusion, the recent controversies in Ireland need to be addressed urgently before the whole charity sector collapses. Research has revealed that majority of the Irish citizens are unwilling to continue supporting the sector. The move to top-up executives’ salaries has made people to lose hope with the sector. Many organizations such as CRC have taken the advantage of the issue to divert vulnerable money to capital projects. A global humanitarian assistance report of July 2010 ranked the country as the fourth most generous in per citizen donations. To maintain this, the government needs to take substantive actions to solve the problem. References Eiben, G. M., Hammond, S., Schaw, C. (2012). Research methods in psychology. London: Sage Publications. Valbuena, J., Shaver, P. R. (2009). Handbook of attachment: theory, research, and clinical applications (2nd ed.). New York: Guilford Press. Parahoo, W. K. (2010). E-learning by design (2nd ed.). San Francisco, CA: Pfeiffer. Mann, T. (2010). The Oxford guide to library research (3rd ed.). New York: Oxford University Press. Denzin, R., Kumar, R. (2010). The long view from Delhi: to define the Indian grand strategy for foreign policy. New Delhi: Academic Foundation in association with Indian Council for Research on International Economic Relations. Northern Ireland;. (2009). London: Labour Party. Symposium programme. (2011). Dublin: Central Remedial Clinic. Harvey, C. H. (2012). The act for the more effectual application of charitable donations and bequests in Ireland: (7 8 Victoria, cap. xcvii) : with explanatory notes on the several sections. London: J.W. Parker. Voss, B. G. (2012). Discourse as a normative instrument analysis of mental illness on a disability services discussion list. Columbia, Mo.: University of Missouri-Columbia. Gorski, W. L. (2009). Microsoft ® Excel Data Analysis and Business Modeling. New York: OReilly Media, Inc. Grove, E. Burn, R . (2009). Adventures in social research: data analysis using IBM SPSS statistics (7th ed.). Los Angeles: Pine Forge Press. Polit et al. (2011). Collaborative interdisciplinary team teaching in Japan a study of practitioner and student perspectives. Australia: Macquarie University. Source document