a.You make the bold assumption that there is a linear relationship between number of ads and sales revenues. Find the intercept, b0, and the slope, b1, of the estimated regression equation that will minimize the sum of square residuals. b.A new advertising campaigns includes 5 ads. Based on what you calculated in part 1.a, how much sales do you expect? Respond just based on the point estimate.
a.You make the bold assumption that there is a linear relationship between number of ads and sales revenues. Find the intercept, b0, and the slope, b1, of the estimated regression equation that will minimize the sum of square residuals. b.A new advertising campaigns includes 5 ads. Based on what you calculated in part 1.a, how much sales do you expect? Respond just based on the point estimate.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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a.You make the bold assumption that there is a linear relationship between number of ads and sales revenues. Find the intercept, b0, and the slope, b1, of the estimated regression equation that will minimize the sum of square residuals.
b.A new advertising campaigns includes 5 ads. Based on what you calculated in part 1.a, how much sales do you expect? Respond just based on the point estimate.

Transcribed Image Text:**1. Introduction**
The following six advertising campaigns show the number of ads (x) and the resulting sales revenues (y) from each advertising campaign. Table 1 illustrates the data.
**TABLE 1. Sample data for sales (y) and number of ads (x)**
| Obs. | x | y | \( x_i - \bar{x} \) | \( (x_i - \bar{x})^2 \) | \( y_i - \bar{y} \) | \( (x_i - \bar{x})(y_i - \bar{y}) \) | \( \hat{y}_i \) | \( e_i \) | \( (y_i - \bar{y})^2 \) | \( (\hat{y}_i - \bar{y})^2 \) |
|------|----|----|-------------------|---------------------|-------------------|---------------------------|----------|---------|---------------------|-------------------------|
| 1 | 10 | 49 | | | | | | | | |
| 2 | 6 | 41 | | | | | | | | |
| 3 | 16 | 49 | | | | | | | | |
| 4 | 8 | 36 | | | | | | | | |
| 5 | 12 | 46 | | | | | | | | |
| 6 | 8 | 43 | | | | | | | | |
This table provides a dataset used for analyzing the relationship between the number of advertisements (x) and the sales revenue (y). The columns marked \( x_i - \bar{x} \), \( (x_i - \bar{x})^2 \), \( y_i - \bar{y} \), \( (x_i - \bar{x})(y_i - \bar{y}) \), \( \hat{y}_i \), \( e_i \), \( (y_i - \bar{y})^2 \), and \( (\hat{y}_i - \bar{y})^2 \) are part of the calculations
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