Please determine if the question is true or false. If the question is false than give a brief description why
T F 1. One of the objectives of simple linear regression is to predict the value of the independent variable X as a linear function of the dependent variable Y.
T F 2. Regression analysis is limited to establishing a relationship between two variables, X and Y.
T F 3. If a deterministic relationship exists between two variables, x and y, any value of x that is selected will determine a unique value of y.
T F 4. When trying to uncover relationships between variables, the recommended practice is to construct a scatter plot first before conducting a statistical analysis.
Use the following scatter plot to answer questions 5 - 9.
T F 5. The dependent variable shown in the plot is the selling price of the real estate property.
T F 6. The plot shows a total of 10 pairs of observations that incorporate the two variables, living area and selling price.
T F 7. The relationship between the two variables, living area and selling price, is such that a decrease in living area is accompanied by a decrease in selling price.
T F 8. There is likely a strong relationship between the two variables, living area and selling price, so a linear model is appropriate.
T F 9. Assuming the data was derived from a subdivision of houses, one would expect to see a selling price of $300,000 for a house that has 2,200 sq. ft. of living area.
T F 11. Extrapolation is most advisable if it is difficult to predict what the data relationship actually is beyond the range of the existing observations.
T F 12. Residuals can be computed by taking the difference between observed and predicted values of Y and squaring them to eliminate negative numbers.
T F 13. The sum of the residuals that surround a regression line will always be greater than or equal to zero.
T F 14. The stronger the relationship between X and Y, the closer the plotted points will be to the regression line.
T F 15. If two variables are highly correlated, the correlation coefficient will be at or near zero.
T F 16. The power of regression analysis is best illustrated by the fact that the presence of outliers has practically no impact on the values of the coefficients or their standard deviations.
Answer of multiple choice questions from regression analysis.