Regression line by using the given data and show the economical plausibility, goodness of F and the slope of the regression line

Step 1: Regression Analysis Regression analysis is a term used to understand the relationship between the independent variable and the dependent variable. In our case, it helps us determine the relationship between advertisement cost and revenues using the given data. Step 2: Econimical Plausibility To determine the economical plausibility of the relationship between advertisement cost and revenue, we look at Table 1 and Figure 1. Figure 1 shows the relationship between revenue and advertisement cost. It is economically plausible because as the advertisement cost increases, the revenue also increases. This indicates a direct relationship between the two variables. Step 3: Goodness of F The goodness of F is determined by the distance between the predicted values and the actual data points. In our case, the distance between the predicted value and the dots is very high. This indicates a good goodness of F, meaning the regression line is appropriate and accurate. Step 4: Slope of the Regression Line The slope of the regression line represents the rate at which the dependent variable (revenue) changes with respect to the independent variable (advertisement cost). In our case, the slope of the regression line is steep, indicating a strong relationship between the two variables. This indicates that the regression line is good for predicting the weekly total cost. Step 5: High-Low Method To determine the function relating advertisement cost and revenues using the high-low method, we need the following information: - Highest revenue: $83,000-Highestadvertisementcost:$4,500 - Lower revenue: $43,000-Loweradvertisementcost:$500 Step 6: Predicting the Effect of Advertisement Cost To determine the effect on revenue if $1,000 is spent on advertisement, we need to use a method to predict the effect of advertisement cost.