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3. What is the dependent variable that was considered in the analysis? Do you think the dependent variable was chosen reasonably? Why? 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 t Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Advertising cost ($) 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: b₁x. Interpret the slope of the estimated regression line. 5. Interpret the R-square of the model. Y = bo + 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.

3. What is the dependent variable that was considered in the analysis? Do you think the dependent variable was chosen reasonably? Why? 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 t Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Advertising cost ($) 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: b₁x. Interpret the slope of the estimated regression line. 5. Interpret the R-square of the model. Y = bo + 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.

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 8E
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show all solutions. no immediate rounding. round off all final answers to four decimal places
3. What is the dependent variable that was considered in the analysis? Do you think the dependent variable was chosen reasonably? Why? For items 3-6, refer to the following software output. The Data Analysis Tool Pak in Excel was used to analyze the dataset above. Regression Statistics Multiple R t Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Advertising cost ($) 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: b₁x. Interpret the slope of the estimated regression line. 5. Interpret the R-square of the model. Y = bo + 6. Another retail merchant who sells face masks spent a total of $55 for advertising its product. Using the regression model in (4), predict the sales of this merchant.
Transcribed Image Text:3. What is the dependent variable that was considered in the analysis? Do you think the dependent variable was chosen reasonably? Why? For items 3-6, refer to the following software output. The Data Analysis Tool Pak in Excel was used to analyze the dataset above. Regression Statistics Multiple R t Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Advertising cost ($) 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: b₁x. Interpret the slope of the estimated regression line. 5. Interpret the R-square of the model. Y = bo + 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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6. another retail merchant who sells face masks spent a total $55 for advertising its product. using the regression model in (4), predict the sale of this merchant.

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