data analysis chapter 16 study guide

41. For a multiple regression analysis with 2 and 12 degrees of freedom, MS regression is 135 and MS residual is 15. What is the decision for this test? A) reject the null hypothesis; the predictive variability of two predictor factors are significant B) retain the null hypothesis; the predictive variability of two predictor factors are not significant C) reject the null hypothesis; the predictive variability of one predictor factor is significant D) retain the null hypothesis; the predictive variability of one predictor factor is significant

A) reject the null hypothesis; the predictive variability of two predictor factors are significant

21. The degrees of freedom associated with regression variation are equal to A) the number of predictor variables B) the number of predictor variables minus one C) n - 1 D) n - 2

A) the number of predictor variables

4. A researcher measures the extent to which the speed at which people eat (in minutes) predicts calorie intake (in kilocalories). Which factor is the predictor variable in this example? (pg 1215) A) the speed at which people eat B) calorie intake C) minutes and kilocalories D) all of the above

A) the speed at which people eat

42. The value of b1 and b2 are referred to as, A) unstandardized beta coefficients B) standardized beta coefficients C) regression variation D) residual variation

A) unstandardized beta coefficients

10. A researcher reports the following equation for a best-fitting straight line to a set of data points: Y^=0.48x+12.03. Which value is the slope? A)Y^hat B) 0.48 C) 12.03 D) The slope is not given in this equation.

B) 0.48

47. If F = 2.04 for the relative contribution of one factor, then what is this value when converted to a t statistic? A) 2.04 B) 1.43 C) 4.16 D) The conversion is not possible.

B) 1.43

***24. If the coefficient of determination is 0.32 and SSy= 150, then what is the sum of squares residual for an analysis of regression? A) 48 B) 102 C) 150 D) There is not enough information to answer this question.

B) 102

49. For a multiple regression, we typically report which value that is not often reported for a one factor linear regression analysis? A) effect size B) B coefficient C) test statistic D) p value

B) B coefficient

3. A researcher measures the extent to which time spent watching educational preschool television programming predicts success in school. Which factor is the criterion variable in this example? (pg 1215) A) educational preschool television B) type of television programming C) success in school D) time spent in school

C) success in school

45. In addition to evaluating the significance of a multiple regression equation, we also should consider: A) the significance of the residual variability B) the complexity of the correlation coefficient C) the relative contribution of each factor D) the significant of each individual data point

C) the relative contribution of each factor

9. A researcher reports the following equation for a best-fitting straight line to a set of data points: Y=-1.01x+3.24 . Which value is the y-intercept? A)Y^hat B) X C) -1.01x D) 3.24

D) 3.24

12. If b = -0.57, My= 2.75, and Mx= 5.25 for a set of data points, then what is the value of the y-intercept for the best-fitting linear equation? A) 0.24 B) 11.68 C) -0.24 D) 5.74

D) 5.74

26. If the coefficient of determination is 0.30 and the sum of squares regression for an analysis of regression is 210, then what is the value of SSy? A) 210 B) 300 C) 490 D) 700

D) 700

46. Which of the following is a step to evaluate the significance for the relative contribution of each factor: (slide 33) A) Find r2 for the "other" predictor variable B) Complete the F table and make a decision C) Identify SS accounted for by the predictor variable of interest D) All of the above

D) All of the above

5. Linear regression describes the extent to which _______ predicts ________. (pg 1215) A) X; Y B) the predictor variable; the criterion variable C) the known variable; the to-be-predicted variable D) all of the above

D) all of the above

48. To summarize any type of regression analysis using APA format, we report each of the following except the, (ppt 37) A) test statistic B) degrees of freedom C) p value D) critical values

D) critical values

6. Which of the following is used to determine the linear equation that best fits a set of data points? (pg 1217) A) correlational analysis B) analysis of variance C) analysis of regression D) method of least squares

D) method of least squares

37. A statistical method that includes two or more predictor variables in the equation of a regression line to predict changes in a criterion variable is called A) analysis of variance B) standard error of estimate C) residual regression D) multiple regression

D) multiple regression

22. The degrees of freedom associated with residual variation are equal to A) the number of predictor variables B) the number of predictor variables minus one C) n - 1 D) n - 2

D) n - 2

36. Multiple regression is a statistical method that includes ____ predictor variable(s) in the equation of the regression line. A) zero B) one C) two D) two or more

D) two or more

11. If SSxy= -16.32 and SSx= 40.00 for a set of data points, then what is the value of the slope for the best-fitting linear equation? A) -0.41 B) -2.45 C) positive D) There is not enough information; you would also need to know the value of

A) -0.41

15. A researcher reports the following regression equation for two variables, X and Y: Y^=5.10x-1.50 . If X = 2.30, then what is the value of ? A) 10.23 B) 11.73 C) 13.23

A) 10.23

35. A researcher computes the following analysis of regression table. Based on the data given, what is the value of the standard error of estimate? (Note: Complete the table first.) Source of Variation SS.--df.--MS.--F Regression-----------28---1.---------5.60 Residual Total------------------118---19 A) 2.24 B) 5.00 C) 5.74 D) 8.49

A) 2.24

****23. If the coefficient of determination is 0.12 and SSy= 225, then what is the sum of squares regression for an analysis of regression? A) 27 B) 198 C) 225 D) There is not enough information to answer this question.

A) 27

28. In a sample of 28 participants, suppose we conduct an analysis of regression with one predictor variable. If F= 4.28, then what is the decision for this test at a .05 level of significance? A) X significantly predicts Y. B) X does not significantly predict Y. C) There is not enough information to answer this question.

A) X significantly predicts Y.

29. A researcher computes the following analysis of regression table. Based on the data given, what is the decision for this test at a .05 level of significance? (Note: Complete the table first.) Source of Variation SS df MS F Regression.--------------1.--28 Residual Total----------------118--19 A) X significantly predicts Y. B) X does not significantly predict Y. C) There is not enough information to answer this question.

A) X significantly predicts Y.

50. The scores or data points for a regression analysis are typically reported in, (ppt 37) A) a scatter plot B) a bar chart C) a pie chart D) all of the above

A) a scatter plot

8. The method of least squares is used to determine the ________ straight line to a set of data points. (pg 1217) A) best-fitting B) straightest C) most linear D) approximate

A) best-fitting

***1. The regression equation measures A) how far the sample mean deviates from the population mean B) how far each data point deviates from the line that most closely fits the data C) how significant mean differences are between groups D) how often scores regress from deviations in the data

A) how far the sample mean deviates from the population mean

40. The predictions made using multiple regression are often more ________ than the predictions made using linear regression with one predictor variable. A) informative B) convoluted C) confounded D) realistic

A) informative

16. Using an analysis of regression, the variability in Y that is predicted by X is measured by the (ppt 18) A) regression variation B) residual variation C) correlation coefficient D) coefficient of determination

A) regression variation

20. Which of the following statements is true regarding the sources of variation present in an analysis of regression? A) Regression variation measures variability in X, whereas residual variation measures variability in Y. B) The closer that data points fall to the regression line, the more the variance in Y will be attributed to regression variation. C) There are three sources of variation in an analysis of regression: regression variance, residual variance, and error variance. D) When most of the variability in Y is associated with residual variation, then X predicts Y.

B) The closer that data points fall to the regression line, the more the variance in Y will be attributed to regression variation.

27. In a sample of 22 participants, suppose we conduct an analysis of regression with one predictor variable. If F= 4.07, then what is the decision for this test at a .05 level of significance? A) X significantly predicts Y. B) X does not significantly predict Y. C) There is not enough information to answer this question.

B) X does not significantly predict Y.

38. One key advantage for including multiple predictor variables in the equation of a regression line is that it allows you to A) detect mean differences between groups for each criterion variable B) detect the extent to which two or more predictor variables interact C) show cause-and-effect because many predictor variables are added D) all of the above

B) detect the extent to which two or more predictor variables interact

2. A researcher measures the extent to which years of marriage predict perceptions of forgiveness. Which factor is the criterion variable in this example? (1215) A) years of marriage B) perceptions of forgiveness C) both years of marriage and perceptions of forgiveness D) none of the above

B) perceptions of forgiveness

17. Using an analysis of regression, the variability in Y that is associated with error is measured by the A) regression variation B) residual variation C) correlation coefficient D) coefficient of determination

B) residual variation

30. An estimate of the standard deviation or distance that data points fall from the regression line is measured by the (ppt 23) A) sum of squares B) standard error of estimate C) criterion variable D) predictor variable

B) standard error of estimate

32. What is the computation for the standard error of estimate? A) the square root of the mean square regression B) the square root of the mean square residual C) the mean square regression, squared D) the mean square residual, squared

B) the square root of the mean square residual

34. A researcher computes an analysis of regression in which MSe= 0.82. What is the value of Se in this example? A) 0.67 B) 0.82 C) 0.91 D) There is not enough information to answer this question.

C) 0.91

***25. If the coefficient of determination is 0.25 and the sum of squares residual is 180, then what is the value of SSy? A) 60 B) 180 C) 240 D) 800

C) 240

19. The more that the variability in ____ is associated with regression variation, the more likely it is that X predicts Y. A) XY B) X C) Y D) all of the above

C) Y

18. Both sources of variation in an analysis of regression measure the variability in A) X and Y B) X only C) Y only

C) Y only

31. The standard error of estimate is used as a measure of the ________ in predictions using the equation of a regression line. A) linearity B) appropriateness C) accuracy D) certainty

C) accuracy

7. Which of the following is used to determine the significance of predictions made by a best fitting linear equation? (pg 1217) A) correlational analysis B) analysis of variance C) analysis of regression D) method of least squares

C) analysis of regression

43. To standardize the beta coefficients, we first, A) analyze the significance of each data point B) analyze the residual variation C) convert the original data to standardized z scores D) compute the standard error of estimate

C) convert the original data to standardized z scores

33. A researcher computes a perfect negative correlation, in which each data point falls exactly on the regression line. In this example, the value of the standard error of estimate will be A) less than 0 B) greater than 0 C) equal to 0 D) There is not enough information to answer this question.

C) equal to 0