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# Multiple choice question form regression analysis

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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 10. Whenever regression analysis is used to predict values of Y that are within the range of the X data, the process is known as interpolation.

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.