Electricity_Sales    Number_of_Customers    Price    Degree_Days 1,144,781    690,044.7    6.7651    547.4338 1,143,784    693,866.5    6.8928    -26.3267 1,184,600    697,890.9    6.8626    -1.6835 1,139,054    701,234.3    6.2738    12.4116 1,204,495    704,746.5    6.1591    606.3047 1,179,366    709,583.7    6.2017    148.1826 1,085,489    713,389.1    6.5480    -2.0032 1,160,943    717,401.4    5.9404    -83.5602 1,158,592    721,355.7    5.8960    66.9503 1,193,556    724,228.1    6.0853    24.2879 1,202,514    727,191.2    6.2554    -0.9467 1,174,335    729,230.4    6.3808    -56.3871 1,174,335    731,584.4    6.2768    -360.9842 1,161,770    734,456.2    6.5243    -192.4087 1,142,863    737,848.2    6.4216    -2.8573 1,196,627    739,084.7    6.2837    -168.6407 1,236,468    740,332.7    6.1659    551.9068 1,188,673    741,904.4    6.0801    55.7721 1,181,075    743,467.6    6.9015    -2.5041 1,203,114    743,895.9    6.4296    -159.8219 1,168,515    745,209.2    6.9283    -610.3438 1,224,423    748,664.4    6.4846    113.5806 1,417,430    751,690.6    6.2845    1.2786 1,255,205    755,482.9    6.9084    96.0549 1,251,512    758,648.6    6.8695    251.5787 1,245,558    762,147.7    6.3565    29.6604   a. Estimate a regression equation with electricity sales as the dependent​ variable, using the number of customers and the price as predictor variables. Interpret the coefficients. Part 2 Multiple regression models with k independent variables have the form shown​ below, where β0 is the Y​ intercept, βn is the slope of Y with variable Xn when all other variables are held​ constant, and εi is the random error in Y for observation i.   yi=β0+β1x1i+β2x2i+...+βKxKi+εi Part 3 Notice that this data set has only two independent variables. The multiple regression equation with two independent variables has the form shown​ below, where b0​, b1​, and b2 are the sample regression coefficients of the population parameters β0, β1, and β2.   Yi=b0+b1X1i+b2X2i Part 4 Use technology to determine a multiple regression​ equation, rounding to one decimal place. Let Y be estimated electricity​ sales, X1 be the number of​ customers, and X2 be the price.   Y=410,032.1+0.5X1+64,375.8X2 Part 5 Interpret the coefficients of the regression equation.   Regression coefficients in a multiple regression are called net regression​ coefficients; they estimate the mean change in Y per unit change in a particular​ X, holding constant the effect of the other X variables. Use the information and the values determined in the previous step to correctly interpret the coefficients of the regression equation. Part 6 b. Estimate a regression equation​ (electricity sales) using only number of customers as a predictor variable. Interpret the coefficient and compare the result from part a. Part 7 Notice that this data set has only one independent variable. The regression equation with one independent variable has the form shown​ below, where b0 and b1 are the sample regression coefficients of the population parameters β0 and β1.   Yi=b0+b1X1i Part 8 Use technology to determine a regression​ equation, rounding to one decimal place. Let Y be estimated electricity​ sales, and X1 be the number of customers.   Y=1,072,293+0.2X1 Part 9 Interpret the coefficients of the regression equation.   Regression coefficients in a regression equation are called net regression​ coefficients; they estimate the mean change in Y per unit change in a particular X. Use the information and the values determined in the previous step to correctly interpret the coefficients of the regression equation. Part 10 Compare the coefficient for the number of customers found in part a to the coefficient for the number of customers found in part b. Part 11 The coefficient for the number of customers in part​ a, 0.5​, is greater than the coefficient for the number of customers found in part​ b, 0.2. Part 12 c. Estimate a regression equation​ (electricity sales) using the price and degree days as predictor variables. Interpret the coefficients. Compare the coefficient for price with that obtained in part a. Part 13 Notice that this data set has only two independent variables. The multiple regression equation with two independent variables has the form shown​ below, where b0​, b1​, and b2 are the sample regression coefficients of the population parameters β0, β1, and β2.   Yi=b0+b1X1i+b2X2i Part 14 Use technology to determine a multiple regression​ equation, rounding to one decimal place. Let Y be estimated electricity​ sales, X1 be the​ price, and X2 be the degree days.   Y=905,564.3+48,321.8X1+143.7X2 Part 15 Interpret the coefficients of regression equation.   Regression coefficients in a multiple regression are called net regression​ coefficients; they estimate the mean change in Y per unit change in a particular​ X, holding constant the effect of the other X variables. Use the information and the values determined in the previous step to correctly interpret the coefficients of the regression equation. Part 16