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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. 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 | 1,265.1 | 343.6 | |||
FloorSpace | 11.455 | 1.54 | |||
Competing Ads | -6.453 | 3.908 | |||
Price | -0.14956 | 0.08880 | |||
t value = Coefficient / SE
eg: t value for Price = -0.14956/0.0888 = -1.684
degrees of freedom (df) = n-2 = 24-2 = 22
p-value = TDIST(abs(t value),df,2)
eg: p-value of Price = TDIST(1.684,22,2) = 0.1063
Hence, Completed Table:
Predictor |
Coefficient |
SE |
tcalc |
p-value |
Intercept |
1,265.10 |
343.6 |
3.682 |
0.0013 |
FloorSpace |
11.455 |
1.54 |
7.438 |
0.0000 |
Competing Ads |
-6.453 |
3.908 |
-1.651 |
0.1129 |
Price |
-0.14956 |
0.0888 |
-1.684 |
0.1063 |
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Check my workCheck My Work button is now enabled Item 5 Item 5 Observations are taken...
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 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...
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