Question

Using the model, predict sales for the fourth quarter of year 2015 You are projecting future sales and have constructed a linear model using historical sales data for each of the four quarters between years 2011 and 2014. Answer questions 24-28 using the information below Regression Statistics Obs Quarter Year Sales Sales 1 2011 71 Multiple R 0.995 7 2011 49 R Square 0.990 90 58 3 2011 Adjusted R Square 0.986 80 4 2011 78 Standard Error 1.561 70 68 1 2012 Observations 16 60 41 6 2012 5 7 2012 50 73 ANOVA 8 2012 40 df 62 9 1 2013 30 38 10 2013 4 Regression 20 2013 11 3 53 Residual 11 10 2013 12 4 70 Total 15 0 1 13 2014 5 10 11 12 13 14 151 16 6 7 8 9 14 7 2014 37 Coefficient 50 15 3 2014 Sales Intercept 81.75 4 65 16 2014 S1 -9.575 17 1 2015 S2 -33.55 18 2015 S3 19 2015 -18.525 4 20 2015 Time -1.025 61.25 62.2 51.5 54.75 None of the above How would you describe second quarter sales using the regression model? You are projecting future sales and have constructed a linear model using historical sales data for each of the four quarters between years 2011 and 2014. Answer questions 24-28 using the information below Regression Statistics Obs Quarter Year Sales Sales 1 1 2011 71 Multiple R 0.995 2011 49 R Square 0.990 90 2011 58 Adjusted R Square 0.986 80 4 4 2011 78 1561 Standard Error 70 68 2012 Observations 16 60 6 2012 41 50 7 2012 55 ANOVA 4 2012 73 40 df 2013 62 30 Regression Residual 10 2013 38 20 11 2013 53 11 10 12 4 2013 70 Total 15 13 1 2014 59 6 7 8 9 10 11 12 13 14 15 16 14 2014 32 Coefficient Sales 15 2014 50 Intercept 81.75 16 4 2014 65 $1 -9.575 17 1 2015 S2 -33.55 18 2015 S3 19 3 2015 -18.525 20 4 2015 Time -1.025 On average, we expect to sell about 33.6 fewer units in the 2nd quarter compared to the 4th quarter We expect to sell about 61.6 fewer units than we normally sell We expect to sell about 40 units in the 2nd quarter of 2015 We expect to sell one more unit in the 2nd quarter of 2015 than we sold in the 2nd quarter of 2014 We can't answer a question about 2nd quarter sales using this model Assume that the sales prices of the houses in your neighborhood follow a normal distribution with a mean S350,000 and standard deviation $50,000. Answer questions 28 and 29. Solutions based on z-table If you price your house at $385,000, what is the probability that other homes in the neighborhood will be priced higher than your house (round to the nearest decimal)? 24,2% 15.7% 19.2% 21.4% O None of the above

Answer 1

A C D G N E H M Centered 4 Quarter foreçast Step5: Calculate Seasonal Index values for 4 quarters =AVERAGE (G2,G6, G 10,G 14) =AVERAGE(G3,G7,G 11,G15) =AVERAGE(G4,G8, G 12,G16) =AVERAGE (G5,G9, G 13,G17) Absolute Seasonal Moving Moving Avearage average Trend Seasonal Forecast Quarter Erron sales 1 Year quarter 65.00 2011 1 71 1.09 761 0.61 1.1017 5.13 49 3 2011 2 64.00 0.77 43.87 .6855 63.01 0.92 4 2011 58 64.00 63,63 43.19 14.81 0.9488 5 2011 4 78 63.25 62.25 62.01 4 1.2777 Step6: Calculate forecast values for 4 quarters =F2*$K$2) =F3*$K$3 Step3: Calculate trend 6 2012 5 68 Step2: Calculate Center of 2 values of 4 quarter =FORECAST(B2,$E$4:$E$15,$B 7 41 Step1: Calculate 4 8 $4:$B$15) 55 =F4*$K$4 =F5*$K$5 moving average Step4: Calculate Seasonal quarter moving 9 73 values for actual sales =SUM(D4:D5)/2 average 10 62 1.09 39.09 22 Repeat the same for 4 more sets of quarters values C2/F2 =SUM(C2:C5)/4 38 38.40 11 0.68 0 12 53 55.75 55.38 Step7: Calculate absolute error 55.00 96 13 2013 12 70 54,25 ABS(C2-H2) 90 53.13 14 13 59 53.50 65 2014 52.13 67 15 2014 14 32 52.75 16 2014 15 50 51.50 51.04 0.98 34.99 15.01 70 17 2014 16 65 50.04 1.30 34.30 30.70 60 18 2015 17 49.04 33.62 33.62 19 2015 18 48.05 32.94 32,94 50 20 2015 19 47,05 32.25 32.25 40 21 2015 20 46.05 31.57 31.57 30 22

30 22 Function Arguments 7 X 23 20 24 FORECAST 10 25 x B2 = 1 26 {63.625;62.25;60.875;59.875;58.5;57... SES4:SES15 1 2 3 4 5 6 7 27 Known x's 3:4;5;6;7;8,9;10;11;12;13;14 SBS4:SBS15 28 65.0011655 29 Calculates, or predicts, a future value along a linear trend by using existing values. 30 x is the data point for which you want to predict a value and must be a numeric value. 31 32 33 Formula result 65.00 34 35 Help on this function OK Cancel 36 37 38 20

A C D F H Normal Probabilities 1 2 Common Data 2 4 Mean 350000 5 Standard Deviation 50000 6 Probability for X > 7 8 X Value 385000 Z Value 0.7 STANDARDIZE (B8, B4,B5) 0.2420 1-NORMDIST(B8,B4,B5,TRUE) 10 P(X>385000) 11 12 X-L0.7 Z:=. 13 X 385000 := 350000 = 50000 14 15 0.7 16 2 (11 (z-u 121(0) 17 18 1 dz=0.24196 =1 P2=1- 19 e 0= V2.T O 20 21 22 6 23 P(X 385000) 24.2% 24 25 26