5. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between TV budget and sales This R output is for a simple data set that contains, in thousands of dollars, TV budget for 200 different markets along with the Sales, in thousands of units, for each market. summary (model1) ## ## Call: ## Lm (formula = Sales - TV, data = train) ## ## Residuals: ## Min 10 Median 30 Маx ## -8.5816 -1.7845 -0.2533 2.1715 6.9345 ## ## Coefficients: Estimate Std. Error t value Pr(>|t|) ## (Intercept) 6.764098 0.607592 11.13 <2e-16 ***k ## TV 0.050284 0.003463 14.52 <2e-16 *** ## -- ## Signif. codes: O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 '' 1 #2# ## Residual standard error: 3.204 on 120 degrees of freedom ## Multiple R-squared: 0.6373, Adjusted R-squared: 0.6342 ## F-statistic: 210.8 on 1 and 120 DF, p-value: < 2.2e-16 confint(model1) ## 2.5 % 97.5 % ## (Intercept) 5.56110868 7.96708701 ## TV 0.04342678 0.05714057 a. Compute the standard deviation of errors. b. Construct a 95% confidence interval for slope B1. c. Using alpha=0.05, test whether B, is different from zero

5. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between TV budget and sales This R output is for a simple data set that contains, in thousands of dollars, TV budget for 200 different markets along with the Sales, in thousands of units, for each market. summary (model1) ## ## Call: ## Lm (formula = Sales - TV, data = train) ## ## Residuals: ## Min 10 Median 30 Маx ## -8.5816 -1.7845 -0.2533 2.1715 6.9345 ## ## Coefficients: Estimate Std. Error t value Pr(>|t|) ## (Intercept) 6.764098 0.607592 11.13 <2e-16 k ## TV 0.050284 0.003463 14.52 <2e-16 ## -- ## Signif. codes: O '' 0.001 '' 0.01 '' 0.05 '.' 0.1 '' 1 #2# ## Residual standard error: 3.204 on 120 degrees of freedom ## Multiple R-squared: 0.6373, Adjusted R-squared: 0.6342 ## F-statistic: 210.8 on 1 and 120 DF, p-value: < 2.2e-16 confint(model1) ## 2.5 % 97.5 % ## (Intercept) 5.56110868 7.96708701 ## TV 0.04342678 0.05714057 a. Compute the standard deviation of errors. b. Construct a 95% confidence interval for slope B1. c. Using alpha=0.05, test whether B, is different from zero

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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