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...

Extracted text: 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="">