The facility manager at a pharmaceutical company wants to build a regression model to forecast monthly electricity cost. Three main variables are thought to dictate electricity cost: average outdoor temperature, working days per month, tons of product produced. A portion of the regression output is included below. Lower Upper Regression Statistics Coefficients P-value 95% 95% Multiple R 0.8160 Intercept 12850 0.30 -15201.55 43279.93 R Square 0.7134 Ave Temp 102 0.01 27.36 158.21 Adjusted R Sq. 0.5406 Work Days 523 0.55 -1199.89 2092.17 Standard Error 1644.2626 Tons -28 0.81 -278.03 224.02 (a) What proportion in variation in electricity cost is explained by variation in the explanatory variables? (b) At the 5% significant level, is there is average outdoor temperature a significant predictor of electricity costs? Explain briefly. (c) Estimate the electricity costs in a month with an average outdoor temperature of 72 degrees, 22 working days, and 4 tons of product produced.

The facility manager at a pharmaceutical company wants to build a regression model to forecast monthly electricity cost. Three main variables are thought to dictate electricity cost: average outdoor temperature, working days per month, tons of product produced. A portion of the regression output is included below. Lower Upper Regression Statistics Coefficients P-value 95% 95% Multiple R 0.8160 Intercept 12850 0.30 -15201.55 43279.93 R Square 0.7134 Ave Temp 102 0.01 27.36 158.21 Adjusted R Sq. 0.5406 Work Days 523 0.55 -1199.89 2092.17 Standard Error 1644.2626 Tons -28 0.81 -278.03 224.02 (a) What proportion in variation in electricity cost is explained by variation in the explanatory variables? (b) At the 5% significant level, is there is average outdoor temperature a significant predictor of electricity costs? Explain briefly. (c) Estimate the electricity costs in a month with an average outdoor temperature of 72 degrees, 22 working days, and 4 tons of product produced.

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

The facility manager at a pharmaceutical company wants to build a regression model to forecast monthly electricity cost. Three
main variables are thought to dictate electricity cost: average outdoor temperature, working days per month, tons of product
produced. A portion of the regression output is included below.
Lower
Upper
Regression Statistics
Coefficients
P-value
95%
95%
Multiple R
0.8160
Intercept
12850
0.30
-15201.55
43279.93
R Square
0.7134
Ave Temp
102
0.01
27.36
158.21
Adjusted R Sq.
0.5406
Work Days
523
0.55
-1199.89
2092.17
Standard
Error
1644.2626
Tons
-28
0.81
-278.03
224.02
(a)
What proportion in variation in electricity cost is explained by variation in the explanatory variables?
(b) At the 5% significant level, is there is average outdoor temperature a significant predictor of electricity costs? Explain briefly.
(c) Estimate the electricity costs in a month with an average outdoor temperature of 72 degrees, 22 working days, and 4 tons
of product produced.

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