Question 1194165
**Interpretation of Regression Statistics Table**

**Coefficients Table**

| Coefficient | Standard Error | t Stat | P-value |
|---|---|---|---|
| Intercept | [Value] | [Value] | [Value] | [Value] |
| Advertising | [Value] | [Value] | [Value] | [Value] |
| No. of Sales Representatives | [Value] | [Value] | [Value] | [Value] |
| Customer Satisfaction | [Value] | [Value] | [Value] | [Value] |

* **Intercept:** Represents the estimated sales of Kahwa when all independent variables (advertising spending, number of sales representatives, and customer satisfaction) are zero.
* **Advertising:** 
    * **Coefficient:** Represents the estimated increase in sales for each unit increase in advertising spending, holding other variables constant. 
    * **Standard Error:** Measures the variability of the estimated coefficient.
    * **t-statistic:** Tests the statistical significance of the coefficient. A higher t-statistic (in absolute value) indicates stronger evidence that advertising spending has a significant impact on sales.
    * **P-value:** The probability of observing a t-statistic as extreme or more extreme than the one observed, assuming the null hypothesis (that the coefficient is zero) is true. A low p-value (typically less than 0.05) suggests that advertising spending has a statistically significant impact on sales.
* **Number of Sales Representatives:** 
    * Interpretation similar to the "Advertising" coefficient.
* **Customer Satisfaction:** 
    * Interpretation similar to the "Advertising" coefficient.

**ANOVA Table**

| Source | SS | df | MS | F | P-value |
|---|---|---|---|---|---|
| Regression | [Value] | [Value] | [Value] | [Value] | [Value] |
| Residual | [Value] | [Value] | [Value] |  |  |
| Total | [Value] | [Value] |  |  |  |

* **Model Sum of Squares (Regression SS):** Variation in sales explained by the regression model (i.e., by the independent variables).
* **Residual Sum of Squares (Residual SS):** Variation in sales not explained by the model (i.e., unexplained by the independent variables).
* **Total Sum of Squares (Total SS):** Total variation in sales.
* **Degrees of Freedom (df):** 
    * Model df: Number of independent variables (3 in this case)
    * Residual df: Number of observations - number of predictors - 1
    * Total df: Number of observations - 1
* **Mean Square (MS):** Sum of Squares divided by degrees of freedom.
* **F-statistic:** Tests the overall significance of the regression model. A higher F-statistic with a low p-value indicates that the model is statistically significant in explaining the variation in sales.
* **P-value:** The probability of observing an F-statistic as large as the one obtained, assuming the null hypothesis that the model has no explanatory power. A low p-value indicates that the model is statistically significant.

**Key Considerations:**

* **Statistical Significance:** Look for low p-values (typically less than 0.05) for both individual coefficients and the overall model (F-statistic). This indicates that the independent variables have a statistically significant impact on sales.
* **Coefficient Interpretation:** Pay close attention to the signs and magnitudes of the coefficients. A positive coefficient indicates a positive relationship between the independent variable and sales.
* **Model Fit:** Evaluate the R-squared and Adjusted R-squared values to assess how well the model fits the data. 
* **Residual Analysis:** Examine the residuals (the difference between actual and predicted sales) to check for any patterns or trends. Ideally, residuals should be randomly distributed around zero.

By analyzing the regression statistics and ANOVA table, you can gain valuable insights into the factors that drive sales of Kashmiri Kahwa and make informed decisions regarding marketing and sales strategies.

**Note:** To obtain the actual numerical values for the coefficients, standard errors, t-statistics, p-values, and ANOVA table, you would need to run the regression analysis using statistical software such as R, Python (with libraries like statsmodels or scikit-learn), or specialized statistical packages.