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
your t-values to 3 decimal places and p-values to 4 decimal
places.)
Predictor | Coefficient | SE | tcalc | p-value | |
Intercept | 1,238.3 | 329.7 | |||
FloorSpace | 11.221 | 1.99 | |||
Competing Ads | -6.731 | 3.989 | |||
Price | -0.14013 | 0.08743 | |||
(b-1) What is the critical value of Student's
t in Appendix D for a two-tailed test at α = .01?
(Round your answer to 3 decimal places.)
t-value =
(b-2) Choose the correct option.
Only CompetingAds differs significantly from zero.
Only FloorSpace differs significantly from zero.
Only Price differs significantly from zero.
a)
Predictor | Coefficient | SE | tcalc | p-value |
Intercept | 1,238.30 | 329.7 | 3.756 | 0.0009 |
FloorSpace | 11.221 | 1.99 | 5.639 | 0.0000 |
Competing Ads | -6.731 | 3.989 | -1.687 | 0.1035 |
Price | -0.14013 | 0.08743 | -1.603 | 0.1211 |
b-1)
critical value of Student's t =2.779
b-2)
Only FloorSpace differs significantly from zero.
Observations are taken on sales of a certain mountain bike in 30 sporting goods stores. The...
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...
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), 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...
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...
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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