develop the estimated simple linear regression equation

0 is a constant (shows the value of Y when the value of X0) 1 the regression coefficient (shows how much Y changes for each unit change in X) Example 1: You have to study the. Where: X the value of the independent variable, Y the value of the dependent variable. (Round your answer to four decimal places. and the simple linear regression equation is: Y 0 + 1X. X 2- represent whether the refrigerator has the thru-the-door ice and water feature, and y represent the list price.)Īt 𝛼 = 0.05, is the thru-the-door ice and water feature a significant factor in the list price of a refrigerator? (Round your numerical values to four decimal places. Use this dummy variable to develop the estimated multiple regression equation to show how list price is related to cubic feet and the thru-the-door ice and water feature. Code the dummy variable with a value of 1 if the refrigerator has the thru-the-door ice and water feature and with 0 otherwise. (Round your answer to four decimal places.)ĭevelop a dummy variable that will account for whether the refrigerator has the thru-the-door ice and water feature. , and the coefficient of determination r 2 is 0.6999, so this multiple linear regression model explains approximately 70 of the variation in the sample values of alumni giving rate. (Round your answer to two decimal places.) The multiple simple linear regression equation is 1 2 3 20.7201 0.7482 0.0290 1.1920 y x x x. State the null and alternative hypotheses. Let x 1 represent the cubic feet and y represent the list price.)Īt the 0.05 level of significance, test whether the estimated regression equation developed in part (a) indicates a significant relationship between list price and cubic feet.

Develop the estimated simple linear regression equation to show how list price is related to the independent variable cubic feet.