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Next SEVEN questions are based on the following regression model To determine the impact of variations in price on sales the management of Big Bob's Burger Barn sets different prices in its burger joints in 75 stores located in different cities. Using the sales and price data, a simple regression is run with sales (in thousands of dollars) as the dependent variable and price (in dollars) as the independent variable. Use the following calculations and the accompanying regression summary output to answer questions 23-30 Exy = 32903.4 X = 5.6812 Ex2 = 2444.66 y = 77.6773 n= 75 DEPENDENT VARIABLE = SALES Regression Statistics Multiple R R Square Adjusted R Square 0.4237 Standard Error Observations 75 ANOVA df SS MS F Stat P-value Regression 55.41569094 1.55E-10 Residual Total 3643.851467 Coefficients Standard Error Stat P-value Lower 95% Upper 95% Intercept 6.2133 19.9094 3.19818E-31 111.3198 136.0859 PRICE 1.55E-1023 The numerator of the formula to find the slope coefficient in the regression equation is_ -179.453 -186.631 -194.096 -201.860 24 The model predicts that when raising the price by $1, sales would change by $_ _ thousand. -11.113 -10.002 9.002 8.101 25 The predicted sales for a price of $6.00 per burger is $ thousand. 83.207 79.047 anop 75.095 71.340 26 Given that E(y -y)? = 1572.44 the sample data show that fraction of variations is sales is explained by price. 0.508 0.432 0.367 0.312 27 The regression result shows the observed sales deviate from the predicted sales, on average, by $ thousand. 7.373 6.267 5.327 4.528 28 The standard error of the slope coefficient b, is 1.28 1.088 0.925 ).786 29 The test statistic for the null hypothesis that a change in price has no impact on sales is: 10.211 9.190 anop 8.271 -7.444 30 The margin of error for a 95% interval estimate for the population slope parameter is: 2.169 2.603 3.123 3.748