23.Enrollment in a particular class for the last four
\nsemesters has been 115, 120, 110, and 130. Suppose a
\none-semester moving average was used to forecast enrollment
\n(this is sometimes referred to as a na\u00c3\u00afve
\nforecast). Thus, the forecast for the second semester would
\nbe 115, for the third semester it would be 120,
\nand for the last semester it would be 110. What would the
\nMSE be for this situation?
\nA) 196.00
\nB) 230.67
\nC) 175.00
\nD) 11.60
\nE) None of the above<\/p>\n
27. Sales for boxes of Girl Scout cookies over a 4-month
\nperiod were forecasted as follows: 100, 120, 115, and
\n123. The actual results over the 4-month period were as
\nfollows: 110, 114, 119, 115. What was the MAD of the
\n4-month forecast?
\nA) 0
\nB) 5
\nC) 7
\nD) 108
\nE) None of the above<\/p>\n
33.
\nThe diagram below illustrates data with a<\/p>\n
A) negative
\ncorrelation coefficient.<\/p>\n
B) zero
\ncorrelation coefficient.<\/p>\n
C) positive
\ncorrelation coefficient.<\/p>\n
D) correlation
\ncoefficient equal to +1.<\/p>\n
E) None of the above<\/p>\n
36. A prediction equation for the number of people (in
\n1,000s) and the annual waste total (in metric tons) was performed using simple
\nlinear regression. In the regression printout shown below, what can be said
\nabout thelevel of significance for the overall model?
\nSUMMARY OUTPUT<\/p>\n
Regression Statistics<\/p>\n
Multiple R 0.715257514<\/p>\n
R Square 0.511593311<\/p>\n
Adjusted R Square 0.496330602<\/p>\n
Standard Error 50.43909112
\nObservations 34<\/p>\n
ANOVA
\ndf SS MS F Significance
\nF
\nRegression 1
\n85276.17996 85276.17996 33.51917 1.99773E-06
\nResidual 32
\n81411.26122 2544.101913
\nTotal 33
\n166687.4412<\/p>\n
Coefficients Standard
\nError t Stat P-value Lower
\n95%<\/p>\n
Intercept 612.3757625
\n 43.18367091 14.18072502
\n2.39E-15 524.4135038
\n# People 1.128638579
\n 0.194943283 5.789574096 2E-06 0.731552108
\n????????????????????????
\n????????????????????????
\nA) Population size is not a good predictor for the amount of
\nwaste.
\nB) The significance level for the intercept indicates the
\nmodel is not valid.
\nC) The significance level for population indicates the slope
\nis equal to zero.
\nD) The significance level for population indicates the slope
\nis not equal to zero.
\nE) None of the above<\/p>\n
41. Describe the purpose and structure of a scatter diagram.
\n(Points : 4)<\/p>\n
45. Demand for a particular type of battery fluctuates from
\none week to the next. A study of the last six
\nweeks provides the following demands (in dozens): 4, 5, 3,
\n2, 8, 10 (last week).
\nForecast demand for the next week using a two-week moving
\naverage. (Round to nearest hundredth
\n4
\n46. Demand for a particular type of battery fluctuates from
\none week to the next. A study of the last six
\nweeks provides the following demands (in dozens): 4, 5, 3,
\n2, 8, 10 (last week).
\nForecast demand for the next week using a three-week moving
\naverage. (Round to nearest hundredth)<\/p>\n
47. Use simple exponential smoothing with ? = 0.3 to
\nforecast battery sales for February through May. Assume that the forecast for
\nJanuary was for 22 batteries.<\/p>\n
Month Automobile
\nBattery Sales Forecasted
\nBattery Sales
\nJanuary42 ——-
\nFebruary 33
\nMarch 28
\nApril 59 <\/p>\n
48.
\nThe following table represents the number of applicants at
\npopular private college in the last four years.<\/p>\n
Month New members
\n2007 10,067
\n2008 10,940
\n2009 11,116
\n2010 10,999<\/p>\n
Assuming ? = 0.2, ? = 0.3, an initial forecast of 10,000 for
\n2007, and an initial trend adjustment of 0 for 2007,use exponential smoothing
\nwith trend adjustment to come up with a forecast for 2011 on the number of
\napplicants.<\/p>\n
Forecast for 2011 = __________ (Fill in the blank with
\ncorrect answer)<\/p>\n
49.
\nUsing the diagram below. The predicted demand of 2014 using
\nthe regression model is (Round
\nanswer to nearest whole number)<\/p>\n
50.
\nGeorge Crowder is conducting research on monthly expenses
\nfor a waste management company. His dependent variable is annual waste in
\nmetric tons while his independent variable is number of people (in thousands)
\nthat live in 25 selected cities. Below is his Excel output.<\/p>\n
A) What is
\nthe prediction equation for the annual metric tons of waste based on the
\npopulation?
\nB) Based on
\nhis model, each additional (1000) increase in people the predicted waste in
\nmetric tons is increased by how much?
\nC) Based on
\nthe significance F-test, is this model a good prediction equation?
\nD) What
\npercent of the variation in waste is explained by the size of the population?
\nE) Can the
\nnull hypothesis that the slope is zero be rejected? Why or why not?
\nF) What is
\nthe value of the correlation coefficient and what conclusions can you draw
\nabout the model based on the correlation coefficient.<\/p>\n","protected":false},"excerpt":{"rendered":"
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