As a marketing manager for TriFood, you want to determine whether store Sales (# sold in one month) of TriPower bars are related to price (in cents) of TriPower bars and in-store promotional expenditures (in dollars) for TriPower bars. You conduct a multiple regression analysis with store Sales (Y) as the response variable, and Price (X1) and Promotion (X2) as explanatory variables. Use the pictured Excel regression output below to answer the questions. a) Interpret the value for R square. Interpret the estimated coefficient for price. b) State the hypotheses for assessing the statistical significance of the overall regression equation. Does the model overall fit the data (yes or no?) f) An external consultant to TriFoods believes that for every $1 increase in promotional expenditures, sales will increase by 4.7 units. Test the consultant's hypothesis at a 5% significance level using both approaches (tcalc vs tcrit and p-value vs a).

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As a marketing manager for TriFood, you want to determine whether store Sales (# sold in one month) of TriPower bars are related to price (in cents) of TriPower bars and in-store promotional expenditures (in dollars) for TriPower bars. You conduct a multiple regression analysis with store Sales (Y) as the response variable, and Price (X1) and Promotion (X2) as explanatory variables. Use the pictured Excel regression output below to answer the questions.

a) Interpret the value for R square. Interpret the estimated coefficient for price.

b) State the hypotheses for assessing the statistical significance of the overall regression equation. Does the model overall fit the data (yes or no?)

f) An external consultant to TriFoods believes that for every $1 increase in promotional expenditures, sales will increase by 4.7 units. Test the consultant's hypothesis at a 5% significance level using both approaches (tcalc vs tcrit and p-value vs a). 

### Regression Analysis Results

The table below presents the results of the regression analysis conducted for the dataset.

#### Regression Statistics:
- **Multiple R**: 0.8705
- **R Square**: 0.7577
- **Adjusted R Square**: 0.7421
- **Standard Error**: 638.0653
- **Observations**: 34

#### ANOVA Table:
| Source           | df | SS            | MS           | F        | Significance F  |
|------------------|----|---------------|--------------|----------|-----------------|
| Regression       | 2  | 39472730.8    | 19736365.4   | 48.4771  | 2.86258E-10     |
| Residual         | 31 | 12620946.7    | 407127.3     |          |                 |
| Total            | 33 | 52093677.4    |              |          |                 |

#### Coefficients Table:
| Coefficients | Standard Error | t Stat  | P-value | Lower 95%  | Upper 95%  |
|--------------|----------------|---------|---------|------------|------------|
| Intercept    | 5837.5208      | 628.1502| 9.2932  | 0.0000     | 4556.3999  | 7118.6417  |
| Price        | -53.2173       | 6.8522  | -7.7654 | 0.0000     | -67.1925   | -39.2421   |
| Promotion    | 3.6131         | 0.6852  | 5.2728  | 0.0000     | 2.2155     | 5.0107     |

### Explanation of Terms:
1. **Multiple R**: This is the correlation coefficient that measures the strength and direction of a linear relationship between the variables.
2. **R Square**: The proportion of variance in the dependent variable that is predictable from the independent variables.
3. **Adjusted R Square**: Adjusted R Square corrects R Square for the number of predictors in the model.
4. **Standard Error**: An estimate of the standard deviation of the error term.
5. **Observations**
Transcribed Image Text:### Regression Analysis Results The table below presents the results of the regression analysis conducted for the dataset. #### Regression Statistics: - **Multiple R**: 0.8705 - **R Square**: 0.7577 - **Adjusted R Square**: 0.7421 - **Standard Error**: 638.0653 - **Observations**: 34 #### ANOVA Table: | Source | df | SS | MS | F | Significance F | |------------------|----|---------------|--------------|----------|-----------------| | Regression | 2 | 39472730.8 | 19736365.4 | 48.4771 | 2.86258E-10 | | Residual | 31 | 12620946.7 | 407127.3 | | | | Total | 33 | 52093677.4 | | | | #### Coefficients Table: | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |--------------|----------------|---------|---------|------------|------------| | Intercept | 5837.5208 | 628.1502| 9.2932 | 0.0000 | 4556.3999 | 7118.6417 | | Price | -53.2173 | 6.8522 | -7.7654 | 0.0000 | -67.1925 | -39.2421 | | Promotion | 3.6131 | 0.6852 | 5.2728 | 0.0000 | 2.2155 | 5.0107 | ### Explanation of Terms: 1. **Multiple R**: This is the correlation coefficient that measures the strength and direction of a linear relationship between the variables. 2. **R Square**: The proportion of variance in the dependent variable that is predictable from the independent variables. 3. **Adjusted R Square**: Adjusted R Square corrects R Square for the number of predictors in the model. 4. **Standard Error**: An estimate of the standard deviation of the error term. 5. **Observations**
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