a. Using regression analysis, estimate consumption as a linear function of GNP, price, and the previous year's electricity consumption. (NOTE: assume 1968 consump- tion was $367.7 billion.) Wiite the equation, 1-statistics, and the coefficient of de- termination. Are the signs of the estimated coefficients consistent with economic theory? Which of the coefficients are statistically significant at the 0.05 level? b. In 1984, GNP was $3661.3 billion and the price of electricity was 7.16 cents p kilowatt-hour. Use the estimating equation from part (a) to predict electric consumption for 1984.

a. Using regression analysis, estimate consumption as a linear function of GNP, price, and the previous year's electricity consumption. (NOTE: assume 1968 consump- tion was $367.7 billion.) Wiite the equation, 1-statistics, and the coefficient of de- termination. Are the signs of the estimated coefficients consistent with economic theory? Which of the coefficients are statistically significant at the 0.05 level? b. In 1984, GNP was $3661.3 billion and the price of electricity was 7.16 cents p kilowatt-hour. Use the estimating equation from part (a) to predict electric consumption for 1984.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

16. Data on electric power consumption (in bilfions of kilowatt-hour). GNP (in bil-.
lions of dollars), and electricitynies (in cenis per iatowatt-houri for the period
1969-i983 are shown here.
lear
Consumption
GNP
Irice
1969
407.9
$ 944.0
2.09¢
1970
447.8
992.7
2.10
1971
479.1
1,077.6
2.19
1972
511.4
1.185.9
2.29
1973
554.2
1,326.4
2.38
1974
555.0
1,434.2
2.83
1975
S86.1
1.594.2
3.21
1976
613.1
1,718.0
3.45
1977
652.3
.1,918.3
3.78
1978
679.2
2,163.9
4.03
1979
696.0
2,417.8
4.43
1980
134.4
2,63!.7
5.12
1981
730.5
2,957.8
S.S0
1982
732.7
3,069.3
1.44
1983
750.9
3.304.8
6.83
a. Using regression analysis, estimate consumption as a linear function of GNP, price,
and the previous year's electricity consumption. (NOTE: assume 1968 consump-
tion was $367.7 billion.) Write the equation, 1-statistics, and the coefficient of de-
termination. Are the signs of the estimated coefficients consistent with economic
theory? Which of the coefficients are statistically significant at the 0.05 level?
b. In 1984, GNP was $3661.3 billion and the price of electricity was 7.16 cents p
kilowatt-hour. Use the estimating equation from part (a) to predict electric E
consumption for 1984.

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