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

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

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

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

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

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

be estimated electricity sales,

be the number of customers, and

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

and

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

be estimated electricity sales, and

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,

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

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

be estimated electricity sales,

be the price, and

be the degree days.

Y=905,564.3+48,321.8X1+143.7X2

Part 15

Interpret the coefficients of regression equation.

Part 16

A company has estimated a regression equation to determine the effect of various predictor variables on the demand for
electricity sales. Prepare a series of regression estimates and discuss the results using the quarterly data for electrical
sales given in the data table below. Complete parts a through c below.
Click the icon to view the data table.
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.
Determine the multiple regression equation. Let be estimated electricity sales, X₁ be the number of customers, and
X₂ be the price.
ý = ( D) + ( D x + (Dx
(Type integers or decimals rounded to one decimal place as needed.)

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Follow-up Question

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.

Determine the multiple regression equation. Let

be estimated electricity sales, and

be the number of customers.

Y=enter your response here+enter your response hereX1

(Type integers or decimals rounded to one decimal place as needed.)

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