Observations are taken on sales of a certain mountain bike in 21 sporting goods stores. The regression model was Y = total sales (thousands of dollars), X1 = display floor space (square meters), X2 = competitors' advertising expenditures (thousands of dollars), X3 = advertised price (dollars per unit). |
(a) |
Fill in the values in the table given here. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your t-values to 3 decimal places and p-values to 4 decimal places.) |
Predictor | Coefficient | SE | tcalc | p-value | |
Intercept | 1,293.9 | 389.2 | |||
FloorSpace | 11.088 | 1.63 | |||
Competing Ads | -6.653 | 3.962 | |||
Price | -0.14949 | 0.08342 | |||
(b-1) |
What is the critical value of Student's t in Appendix D for a two-tailed test at α = 0.01? (Round your answer to 3 decimal places.) |
t-value = |
(b-2) | Choose the correct option. | ||||||
|
for t =coefficient/std error
a)
Predictor | Coefficient | SE | tcalc | p-value |
Intercept | 1,293.90 | 389.2 | 3.325 | 0.0040 |
FloorSpace | 11.088 | 1.63 | 6.802 | 0.0000 |
Competing Ads | -6.653 | 3.962 | -1.679 | 0.1114 |
Price | -0.14949 | 0.08342 | -1.792 | 0.0909 |
b-1)
critical value of Student's t =2.898
b-2)
Only FloorSpace differs significantly from zero.
Observations are taken on sales of a certain mountain bike in 21 sporting goods stores. The...
Observations are taken on sales of a certain mountain bike in 30 sporting goods stores. The regression model was Y = total sales (thousands of dollars), X1 = display floor space (square meters), X2 = competitors' advertising expenditures (thousands of dollars), X3 = advertised price (dollars per unit). (a) Fill in the values in the table given here. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round...
Observations are taken on sales of a certain mountain bike in 22 sporting goods stores. The regression model was Y total sales (thousands of dollars), X1- display floor space (square meters), X2- competitors' advertising expenditures (thousands of dollars), X3 advertised price (dollars per unit). (a) Fill in the values in the table given here. (Negative values should be indicated by a minus sign. Leave no cells blank be certain to enter "O" wherever required. Round your t-values to 3 decimal...
Observations are taken on sales of a certain mountain bike in 30 sporting goods stores. The regression model was Y = total sales (thousands of dollars). X = display floor space square meters). X- competitors' advertising expenditures (thousands of dollars). X, advertised price (dollars per unit) Predictor Intercept FloorSpace Competing Ads Price Coefficient 1203 91 11.29 -8.889 -0.1448 (a) Write the fitted regression equation (Round your coefficient Competing Ads to 3 decimal places, coefficient Price to 4 decimal places, and...
Observations are taken on sales of a certain mountain bike in 30 sporting goods stores. The regression model was Y = total sales (thousands of dollars), X1 = display floor space (square meters), X2 = competitors’ advertising expenditures (thousands of dollars), X3 = advertised price (dollars per unit). Predictor Coefficient Intercept 1,287.26 FloorSpace 11.52 CompetingAds −6.934 Price −0.1476 (a) Write the fitted regression equation. (Round your coefficient CompetingAds to 3 decimal places, coefficient Price to 4 decimal places, and other...
Check my workCheck My Work button is now enabled Item 5 Item 5 Observations are taken on sales of a certain mountain bike in 24 sporting goods stores. The regression model was Y = total sales (thousands of dollars), X1 = display floor space (square meters), X2 = competitors' advertising expenditures (thousands of dollars), X3 = advertised price (dollars per unit). (a) Fill in the values in the table given here. (Negative values should be indicated by a minus sign....
A regression model to predict Y, the state burglary rate per 100,000 people for 2005, used the following four state predictors: X1 = median age in 2005, X2 = number of 2005 bankruptcies, X3 = 2004 federal expenditures per capita (a leading predictor), and X4 = 2005 high school graduation percentage. (a) Fill in the values in the table given here for a two-tailed test at α = 0.01 with 31 d.f. (Negative values should be indicated by a minus...
A regression model to predict Y, the state burglary rate per 100,000 people for 2005, used the following four state predictors: X1 = median age in 2005, X2 = number of 2005 bankruptcies, X3 = 2004 federal expenditures per capita (a leading predictor), and X4 = 2005 high school graduation percentage. (a) Fill in the values in the table given here for a two-tailed test at a = 0.01 with 33 d.f. (Negative values should be indicated by a minus...
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Observations are taken on net revenue from sales of a certain plasma TV at 30 retail outlets. A linear regression model was formed using the following variables: Y = net revenue (thousands of dollars); X1 = shipping cost (dollars per unit); X2 = expenditures on print advertising (thousands of dollars); and X3 = expenditures on electronic media ads (thousands of dollars). Partial regression output appears below. variables coefficient std. error t-value p-value Intercept ShipCost PrintAds WebAds 4.31 -0.08 2.26 2.49...
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