Question1)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:

Question 2.

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?

Question 3.

When the significance level is small enough in the F-test, we can reject the null hypothesis that there is no linear relationship.

Question 4.

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 ten years of experience.

What is the forecasted salary?

Question 5.

When both trend and seasonal components are present in time series, which of the following is most appropriate?

Question 6.

A scatter diagram is useful to determine if a relationship exists between two variables.

Question 7.

Time-series models attempt to predict the future by using historical data.

Question 8.

Which of the following is an assumption of the regression model?

Question 9.

Which of the following statements is false concerning the hypothesis testing procedure for a regression model?

Question 10.

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

Based on this equation, by how much could a teacher expect his or her salary to increase for every additional tear of service?