Question

It seems logical that restaurant chains with more units (restaurants) would have greater sales. Ca 5 n we predict sales by measuring a restaurant chain's number of units? Presented below are data from nine large restaurant chains. Use the accompanying scatterplot, correlation matrix and simple linear regression output to answer the question that follows the output. Number of units in thousands Sales in billions Chain McDonald's Burger King Taco Bell Pizza Hut Wendy's KFC Subway Dairy Queen Hardee's 12.4 7.5 6.8 8.7 4.6 5.1 11.2 5.1 2.9 17.1 7.9 4.8 4.7 4.6 2.9 2.7 2.7 12 10 6 2.5 75 10 12.515 175 Units (x) Units () Sales (y Units (x Sales (y) 0.636126 Regression Statistics 0.636125902 0.404656163 0.319607043 2 601614486 Multiple R Square Adjusted R Square Standard Error Observations ANOVA df MS 32.20343668 32.20343668 4.75791125 0.065518364 Regression Residual Total 47.37878554 6.768397935 8 79.58222222 P-value Lower 95% Upper 95% Lower 95 0% Upper 95096 4.633131413 1441374831 3.214383458 0.014767988 1.224821532 8 041441294 1.224821532 8.041441294 0.439724072 0.201591433 2.181263682 0.065518364 -0.03696392 0.916412063 003696392 0.916412063 Coefficients Standard Error Stat Intercept Units (x) The correct regression equation to predict Sales based on Units, according to this analysis, is.. Sales 4.63+0.44(Units) Sales 4.63-0.44(Units) Sales = 0.44 + 4.63(Units) Sales 0.44- 4.63(Units)

Answer 1

Explanation:

Hello!

A regression model was estimated in order to test if the number of units of a restaurant chain affecta the sales of said restaurant chains.

The estimated model is:

Y= a + bX

Where

Y: is the sales of a restaurant chain

X: number of units of a restaurant chain

Looking at the computed data, the estimated coefficients are:

a= 4.633131413

b= 0.439724072

The correct regression equation to predict Sales based on Units, according to this analysis, is..

Y= 4.63 + 0.44X

I hope it helps!