($1,000s) ($1,000s) ($1,000s) 104 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 100 3.0 3.3 94 3.5 2.3 94 2.5 4.2 96 3.0 2.5 a. Develop an estimated regression equation with the amount of television advertising as the independent variable (to 1 decimal). Revenue = TVAdv b. Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables (to 2 decimals). Revenue = + TVAdv + NVAdv c. Is the estimated regression equation coefficient for television advertising expenditures the same in part (a) and in part (b)? - Select your answer - Interpret the coefficient in each case. In- Select your answer - v the coefficient is an estimate of the change in revenue due to a one-unit change in television advertising expenditures. In - Select your answer - v the coefficient is an estimate of the change in revenue due to a one-unit change in television advertising expenditures with the amount of newspaper advertising held constant. d. Predict weekly gross revenue for a week when $3,400 is spent on television advertising and $2,000 is spent on newspaper advertising? NOTE: To compute the predicted revenues, use the coefficients you have computed rounded to two decimals, as you have entered them here. Then, round your predicted revenue to the nearest dollar. 24

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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d. Predict weekly gross revenue for a week when $3,400 is spent on television advertising and $2,000 is spent on newspaper advertising?

NOTE: To compute the predicted revenues, use the coefficients you have computed rounded to two decimals, as you have entered them here. Then, round your predicted revenue to the nearest dollar.

# Regression Analysis: Television and Newspaper Advertising

### Data Overview

Below are revenue figures (in thousands of dollars) alongside advertising expenditures for television and newspapers:

| Revenue ($1,000s) | TV Advertising ($1,000s) | Newspaper Advertising ($1,000s) |
|-------------------|--------------------------|----------------------------------|
| 104 | 5.0 | 1.5 |
| 90 | 2.0 | 2.0 |
| 95 | 4.0 | 1.5 |
| 92 | 2.0 | 2.5 |
| 100 | 3.0 | 3.3 |
| 94 | 3.5 | 2.3 |
| 94 | 2.5 | 4.2 |
| 96 | 3.0 | 2.5 |

### Tasks

**a. Develop an estimated regression equation with the amount of television advertising as the independent variable (to 1 decimal).**
- **Equation:** Revenue = [Blank] + [Blank] · TVAdv

**b. Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables (to 2 decimals).**
- **Equation:** Revenue = [Blank] + [Blank] · TVAdv + [Blank] · NVAdv

**c. Is the estimated regression equation coefficient for television advertising expenditures the same in part (a) and in part (b)?**
- **Select your answer:**
- [Dropdown option]

**Interpret the coefficient in each case:**
- In part (a), the coefficient is an estimate of the change in revenue due to a one-unit change in television advertising expenditures.
- **Select your answer:**
- [Dropdown option]

- In part (b), the coefficient is an estimate of the change in revenue due to a one-unit change in television advertising expenditures with the amount of newspaper advertising held constant.
- **Select your answer:**
- [Dropdown option]

**d. Predict weekly gross revenue for a week when $3,400 is spent on television advertising and $2,000 is spent on newspaper advertising.**

*Note: To compute the predicted revenues, use the coefficients you have computed rounded to two decimals, as you have entered them here. Then, round

The image presents a scenario where Showtime Movie Theaters, Inc. aims to predict weekly gross revenue as a function of advertising expenditures, using historical data from eight weeks. The data is organized into a table with three columns:

1. **Weekly Gross Revenue ($1,000s)**
2. **Television Advertising ($1,000s)**
3. **Newspaper Advertising ($1,000s)**

The data points for each week are as follows:

- Week 1: Revenue: 104, TV: 5.0, Newspaper: 1.5
- Week 2: Revenue: 90, TV: 2.0, Newspaper: 2.0
- Week 3: Revenue: 95, TV: 4.0, Newspaper: 1.5
- Week 4: Revenue: 92, TV: 2.5, Newspaper: 2.5
- Week 5: Revenue: 100, TV: 3.0, Newspaper: 3.3
- Week 6: Revenue: 94, TV: 3.5, Newspaper: 2.3
- Week 7: Revenue: 95, TV: 4.2, Newspaper: 4.2
- Week 8: Revenue: 96, TV: 3.0, Newspaper: 2.5

Below the table are tasks related to regression analysis:

a. Develop an estimated regression equation using the amount of television advertising as the independent variable. The equation format given is:
$ \text{Revenue} = \boxed{} + \boxed{} \times \text{TVAdv} $

b. Develop an estimated regression equation with both television and newspaper advertising as the independent variables. The equation format provided is:
$ \text{Revenue} = \boxed{} + \boxed{} \times \text{TVAdv} + \boxed{} \times \text{NVAdv} $

c. Questioned whether the estimated regression equation coefficient for television advertising is the same in parts (a) and (b).

Additional instructions involve interpreting the coefficient:

- In [blank space], the coefficient is an estimate of the change in revenue due to a one-unit change in television advertising expenditures.
- In [another blank space], the coefficient is an estimate of the change in revenue due to a one-unit change in television advertising expenditures with the amount of newspaper

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