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supersour supersour
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3 weeks ago
Use the following to answer the questions below:

A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.

Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.

The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend

PredictorCoefSE CoefTP
Constant83.5604.23819.720.000
High Temp 0.529180.070207.540.000
Weekend-25.1683.724 -6.76 0.000
S = 29.8162   R-Sq = 24.7%   R-Sq(adj) = 24.2%

Analysis of Variance

SourceDFSSMSFP
Regression2904814524150.890.000
Residual Error310275592889
Total312366073

Another possible predictor they recorded was the average temperature over the course of each day. Regression output for the model that uses High Temp, Weekend, and Avg. Temp is provided. Explain why these results differ so drastically from those for the two-predictor model.

The regression equation is
Sullivan Student Center = 81.9 + 0.839 High Temp - 25.1 Weekend - 0.337 Avg. Temp

PredictorCoefSE CoefTP
Constant81.8814.83716.930.000
High Temp0.83890.43511.930.055
Weekend-25.0533.730-6.720.000
Avg. Temp-0.33720.4673-0.720.471

S = 29.8393        R-Sq = 24.8%        R-Sq(adj) = 24.1%

Analysis of Variance

SourceDFSSMSFP
Regression3909453031534.050.000
Residual Error309275129890
Total31266073
Textbook 
Statistics: Unlocking the Power of Data

Statistics: Unlocking the Power of Data


Edition: 3rd
Authors:
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joshamjosham
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