Question

Mark​ Gershon, owner of a musical instrument​ distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following​ table:

                                                                                                          

Maroon 5 TV Appearances	33	55	77	55	77	55
Demand for Guitars	44	66	88	66	1111	66

This exercise contains only parts​ b, c, and d.

​b) Using the​ least-squares regression​ method, the equation for forecasting is ​(round your responses to four decimal

​places)​:

Y​ =

nothing

​+

nothingx

​c) The estimate for guitar sales if Maroon 5 performed on TV

99

times​ =

nothing

sales ​(round your response to two decimal​ places).​d) The correlation coefficient

​(r​)

for this model​ =

nothing

​(round your response to four decimal​ places).The coefficient of determination

​(r squaredr2​)

for this model​ =

nothing

​(round your response to four decimal​ places).The percentage of variation in sales that can be explained by TV appearances​ =

nothing​%

​(round your response to two decimal​ places).

Answer 1

b))

HZ Data Analysis ? X к 1 Maroon 5 appearances Demand for guitars ок | Cancel Analysis Tools Histogram Moving Average Random Number Generation Rank and Percentile Regression Sampling t-Test: Paired Two Sample for Means t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Unequal Variances z-Test: Two Sample for Means Help

. Get Refresh 2 Fliter Text to Data Stocks Geography - All E dit Links Get & Transform Data Queries & Connections Regression 0 PRODUCT DEACTIVATED To keep using Excel without inte Input Input Y Range: Advanced ? Columns EDC X ta Tools ок SB$2:$B$7 | A2 Cancel Input X Range: $A$2:$A$7| Help 1 Maroon 5 appearances Demand for guitars Labels Confidence Level: Constant is Zero 95 % MONINI Output options O Output Range: O New Worksheet Ply: O New Workbook Residuals Residuals standardized Residuals Residual Plots Line Fit Plots Normal Probability Normal Probability Plots

PU B C D E F G H I J 1 SUMMARY OUTPUT 2 3 Regression Statistics 4 Multiple R 0.903542463 5 R Square 0.816388983 6 Adjusted R Square 0.770486229 7 Standard Error 1.150447483 8 Observations df 1 SS MS F gnificance F 23.53921569 23.53921569 17.78519 0.013507 5.294117647 1.323529412 28.83333333 5 10 ANOVA 11 12 Regression 13 Residual 14 Total 15 16 17 Intercept 18 X Variable 1 19 Coefficients Standard Error Stat P-value Lower 95%Upper 95%ower 95.0%pper 95.0% -0.8529 1.882123148 -0.453180323 0.673914 -6.07855 4.37267 -6.07855 4.37267 1.4412 0.341733825 4.217248533 0.013507 0.492371 2.389982 0.492371 2.389982

The equation is

Y = -0.8529 + 1.4412X

c)

When X = 9

Y = -0.8529 +1.4412*9 = 12.12 guitars

d)

Percentage of variation explained is given by R square value

Percentage of variation explained = 81.64 %