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1.An air conditioning and heating repair firm conducted a study to determine if the average outside temperature, thickness of the insulation, and age of the heating equipment could be used to predict the electric bill for a home during the winter months in Houston, Texas. The resulting regression equation was:

Y = 256.89 – 1.45X1 – 11.26X2 + 6.10X3, where Y = monthly cost, X1 = average temperature, X2 = insulation thickness, and X3 = age of heating equipment


Assume January has an average temperature of 40 degrees and the heater is 12 years old with insulation that is 2 inches thick.

What is the forecasted monthly electric bill?


2.A large school district is reevaluating its teachers’ salaries. They have decided to use regression analysis to predict mean teachers’ salaries at each elementary school. The researcher uses years of experience to predict salary. The resulting regression equation was:

Y = 23,313.22 + 1,210.89X, where Y = salary, X = years of experience


Assume a teacher has five years of experience. What is the forecasted salary?


3. If computing a causal linear regression model of Y = a + bX and the resultant r2 is very near zero, then one would be able to conclude that:


4. Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent). What are the forecast sales for the next day using a two-day moving average?