Question 1. Question :
A prediction equation for starting salaries (in $1,000s) and SAT scores was performed using simple linear regression. In the regression printout shown above, what can be said about the level of significance for the overall model?
Click here to view the printout in Excel.
SAT is not a good predictor for starting salary.
The significance level for the intercept indicates the model is not valid.
The significance level for SAT indicates the slope is equal to zero.
The significance level for SAT indicates the slope is not equal to zero.
None of the above can be said about the level of significance.
Question 2. Question :
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?
Question 3. Question :
Which of the following statements is false concerning the hypothesis testing procedure for a regression model?
The F-test statistic is used.
The null hypothesis is that the true slope coefficient is equal to zero.
The null hypothesis is rejected if the adjusted r2 is above the critical value
An ? level must be selected.
The alternative hypothesis is that the true slope coefficient is not equal to zero.
Question 4. Question :
A scatter diagram is useful to determine if a relationship exists between two variables.
Question 5. Question :
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 December has an average temperature of 45 degrees and the heater is 2 years old with insulation that is 6 inches thick.
What is the forecasted monthly electric bill?
Question 6. Question :
Demand for soccer balls at a new sporting goods store is forecasted using the following regression equation: Y = 98 + 2.2X where X is the number of months that the store has been in existence. Let April be represented by X = 4. April is assumed to have a seasonality index of 1.15. What is the forecast for soccer ball demand for the month of April (rounded to the nearest integer)?
None of the above
Question 7. Question :
Which of the following is an assumption of the regression model?
The errors are independent.
The errors are not normally distributed.
The errors have a standard deviation of zero.
The errors have an irregular variance.
The errors follow a cone pattern.
Question 8. Question :
The coefficients of each independent variable in a multiple regression model represent slopes.
Question 9. Question :
Time-series models attempt to predict the future by using historical data.
Question 10. Question :
Demand for a particular type of battery fluctuates from one week to the next. A study of the last six weeks provides the following demands (in dozens): 4, 5, 3, 2, 8, 10 (last week). What is the the forecast demand for the next week using a three-week moving average.