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Multiple choice question form regression analysis

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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.

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Solution Summary

Answer of multiple choice questions from regression analysis.

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Multiple choice questions:

1. _____ is a procedure for deriving a mathematical relationship, in the form of an equation between a single metric dependent variable and a single metric independent variable.

a. chi-square
b. part correlation
c. multiple regression
d. bivariate regression

2. _______ are hypothesis testing procedures that assume that the variables of interest are measured on at least an interval scale.

a. parameter tests
b. parametric tests
c. nonparametric tests
d. none of the above

3. The ______ is a symmetric bell-shaped distribution that is useful for small (n<30) testing.

a. t distribution
b. frequency distribution
c. chi-square distribution
d. F distribution

4. The degrees of freedom for the t statistic to test hypothesis about one mean are _____

a. n
b. n - 1
c. n1 + n2
d. n1 + n2 -2

5. The _____ is a statistical test of the equality of the variance of two populations.

a. z test
b. t test
c. paired sample test
d. F test

6. The total variation in y is _____.

a. SSy
b. SSwithin
c. SSbetween
d. SSy

7. Which of the following statements in not correct about the alternative hypothesis

a. There is not way to determine whether the alternative hypothesis is true.
b. The alternative hypothesis represents the conclusion for which the evidence is sought.
c. The alternative hypothesis is the opposite of the null hypothesis.
d. None of the statements are correct.

8. Hypothesis tests can be used to relate to:

a. tests of strengths
b. tests of association
c. tests of differences
d. b and c are correct

9. Also know as SSerror, _____ is the variation in Y due to the variation within each of the categories of X. This variation is not accounted for by X.

a. SSy
b. SSwithin
c. SSbetween
d. SSy

10. How consumers' intentions to buy a brand vary with different levels of price and different levels of distribution is best analyzed via _____.

a. n-way ANOVA
b. one-way ANOVA
d. Regression

11. Is a statistical procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables.

a. regression
b. partial correlation coefficient
d. Product movement correlation

12. The degrees of freedom for the t statistic to test hypothesis about two independent samples is __________

a. n
b. n - 1
c. n1 + n2
d. n1 + n2 -2

13. Univariate techniques can be classified based on _____.

a. whether the data are metric or nonmetric
b. whether one two, or more than two samples are involved
c. whether interdependence techniques or dependence techniques are to be used
d. a and b are correct

14. A hypothesis test produces a t statistic of t = 2.30. If the researcher is using a two-tailed test with alpha = .05, how large does the sample have to be to reject the null hypothesis?

A. at least n = 8
B. at least n = 9
C. at least n =10
D. at least n = 11

15. _____ occurs when the sample results lead to the rejection of the null hypothesis that is in fact true.

a. Type I error
b. Two-tailed error
c. Type II error
d. One -tailed error

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