A regression equation has been developed in order to help predict the amount of sales made in a clothing store

each day (Y) based on the number of square feet covered by the store’s floor space (X). A sample of 100 similar clothing stores is collected, with floor spaces ranging from 100 square feet to 300 square feet. For each store, the floor space (in square feet) and the amount of sales made that day (in dollars) are recorded. The following regression equation is calculated:

y^ = 2,035 + 58x

Select whether or not each of the following conclusions are correct from the regression analysis:

In simple linear regression, a common measure is the one given by the following formula:

∑(y_i – y^_i)²

Select the correct interpretation of what this formula means to the regression model:

A simple linear regression equation is to be constructed to determine if there is a linear relationship between a response variable (Y) and an explanatory variable (X). A random sample of size n has been collected and the values x_i and y_i for i = 1, 2, …, n have been recorded. The residuals (e_i) in this analysis are defined as the difference between the observed values of Y and the values of Y predicted by the regression equation.

The scatterplot or residual plots can be used to see if the regression equation is an appropriate model for the data. An assumption required for the simple linear regression equation to be valid is:

Looking for a Similar Assignment? Order now and Get 10% Discount! Use Coupon Code "Newclient"