For items 3-6, refer to the following software output. The Data Analysis ToolPak in Excel was used to analyze the dataset above. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Advertising cost (S) 0.634837268 0.403018356 0.343320192 50.2256962 df 12 1 10 11 SS MS F Significance F 17030.04442 17030.04 6.750934 0.026570895 25226.20558 2522.621 42256.25 Coefficients Standard Error t Stat P-value 279.2893401 68.69285999 4.06577 0.002265 3.220812183 1.239605445 2.598256 0.026571 4. Present the simple linear regression model using the following format: Y = bo. b₂x. Interpret the slope of the estimated regression line. 6. Another retail merchant who sells face masks spent a total of $55 for advertising its product. Using the regression model in (4), predict the sales of this merchant.
For items 3-6, refer to the following software output. The Data Analysis ToolPak in Excel was used to analyze the dataset above. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Advertising cost (S) 0.634837268 0.403018356 0.343320192 50.2256962 df 12 1 10 11 SS MS F Significance F 17030.04442 17030.04 6.750934 0.026570895 25226.20558 2522.621 42256.25 Coefficients Standard Error t Stat P-value 279.2893401 68.69285999 4.06577 0.002265 3.220812183 1.239605445 2.598256 0.026571 4. Present the simple linear regression model using the following format: Y = bo. b₂x. Interpret the slope of the estimated regression line. 6. Another retail merchant who sells face masks spent a total of $55 for advertising its product. Using the regression model in (4), predict the sales of this merchant.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
show all the solutions. no immediate rounding. round off the final answer in 4 decimal places

Transcribed Image Text:For items 3-6, refer to the following software output. The Data Analysis ToolPak in
Excel was used to analyze the dataset above.
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
Advertising cost (S)
0.634837268
0.403018356
0.343320192
50.2256962
df
12
1
10
11
SS
MS
F Significance F
17030.04442 17030.04 6.750934 0.026570895
25226.20558 2522.621
42256.25
Coefficients Standard Error t Stat P-value
279.2893401 68.69285999 4.06577 0.002265
3.220812183 1.239605445 2.598256 0.026571
4. Present the simple linear regression model using the following format: Y = bo.
b₁x. Interpret the slope of the estimated regression line.
6. Another retail merchant who sells face masks spent a total of $55 for advertising
its product. Using the regression model in (4), predict the sales of this merchant.
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