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Q:
19 Suppose that in the previous question another subject had a predicted score of 10.3, and actually obtained a score of 12.4. For this subject the residual score would be
a) 2.1
b) -0.7
c) 12.4
d) 0.0
Q:
18+ If our regression equation is = 0.75 age + 0.50 experience - 0.10 grade point average " 2.0, and if our first subject had scores of 16, 4, and 3.0 on those three variables, respectively, then that subject's predicted score would be
a) 11.7
b) 10
c) 16
d) -3
Q:
17+ If two variables taken together account for 65% of the variability in Y, and a third variable has a simple squared correlation with Y of .10, then adding that variable to the equation will allow us to account for
a) 65% of the variability in Y.
b) 75% of the variability in Y.
c) 10% of the variability in Y.
d) at least 65% of the variability in Y.
Q:
16 In simple correlation a squared correlation coefficient tells us the percentage of variability in Y associated with variability in X. In multiple regression, the squared multiple correlation coefficient
a) has the same kind of meaning.
b) has no meaning.
c) overestimates the degree of variance accounted for.
d) underestimates the degree of variance accounted for.
Q:
15 The multiple correlation of several variables with a dependent variable is
a) less than the largest individual correlation.
b) equal to the correlation of the dependent variable to the values predicted by the regression equation.
c) noticeably less than the correlation of the dependent variable to the values predicted by the regression equation.
d) It could take on any value.
Q:
14+ If two variables are each correlated significantly with the dependent variable, then the multiple correlation will be
a) the sum of the two correlations.
b) the sum of the two correlations squared.
c) no less than the larger of the two individual correlations.
d) It could take on any value.
Q:
13 If we have three predictors and they are all individually correlated with the dependent variable, we know that
a) each of them will play a significant role in the regression equation.
b) each of them must be correlated with each other.
c) each regression coefficient will be significantly different from zero.
d) none of the above
Q:
12+ If you have a number of scores that are outliers you should
a) throw them out.
b) run the analysis with and without them, to see what difference they make.
c) try to identify what is causing those scores to be outliers.
d) both b and c
Q:
11 In multiple regression an outlier is one that
a) is reasonably close to the regression surface.
b) is far from the regression surface.
c) is extreme on at least one variable.
d) will necessarily influence the final result in an important way.
Q:
10 Before running a multiple regression, it is smart to look at the distribution of each variable. We do this because
a) we want to see that the distributions are not very badly skewed.
b) we want to look for extreme scores.
c) we want to pick up obvious coding errors.
d) all of the above
Q:
9+ When we speak of the correlations among the independent variables, we are speaking of
a) homoscedasticity.
b) multicollinearity.
c) independence.
d) multiple correlation.
Q:
8 If we want to compare the contribution of several predictors to the prediction of a dependent variable, we can get at least a rough idea by comparing
a) the regression coefficients.
b) the standardized regression coefficients.
c) the variances of the several variables.
d) the simple Pearson correlations of each variable with the dependent variable.
Q:
7 If one independent variable has a larger coefficient than another, this means
a) that the variable with the larger coefficient is a more important predictor.
b) that the variable with the larger coefficient is a more statistically significant predictor.
c) that the variable with the larger coefficient contributes more to predicting the variability in the criterion.
d) We can"t say anything about relative importance or significance from what is given here.
Q:
6+ In the previous question, a student who scored 0 on both X1 and X2 would be expected to have a dependent variable score of
a) 0.
b) 3.5.
c) 12.
d) the mean of Y.
Q:
5+ Given the following regression equation (= 3.5 X1 + 2X2 + 12), the coefficient for X1 would mean that
a) two people who differ by one point on X1 would differ by 3.5 points on .
b) two people who differ by one point on X1 would differ by 3.5 points on , assuming that they did not differ on X2.
c) X1 causes a 3.5 unit change in the dependent variable.
d) X1 is more important than X2.
Q:
4 In multiple regression the intercept is usually denoted as
a) a
b) b1
c) b0
d) 0
Q:
3+ In the previous question the intercept would be
a) 1.0
b) 0.0
c) 3.0
d) There would be no way to know.
Q:
2+ Assume that we generated a prediction just by adding together the number of stressful events you report experiencing over the last month, the number of close friends you have, and your score on a measure assessing how much control you feel you have over events in your life (i.e., prediction = stress + friends + control). The regression coefficient for stressful events would be
a) 1.0
b) 4.0
c) 0.0
d) There is no way to know.
Q:
1 The difference between multiple regression and simple regression is that
a) multiple regression can have more than one dependent variable.
b) multiple regression can have more than one independent variable.
c) multiple regression does not produce a correlation coefficient.
d) both b and c
Q:
20+ When we think in terms of standardized data, the slope represents
a) the change in X for a one unit change in Y.
b) the number of standard deviations will differ for a one standard deviation difference in X.
c) the height of the regression line.
d) 0.
Q:
19 When we have standardized data, the slope will be denoted as
Q:
18+ When we standardize paired data we
a) divide everything by the standard deviation of X.
b) convert X to a T score.
c) convert both X and Y to z scores.
d) subtract the mean from each value of X and Y.
Q:
17 In the equation = 12.6 X + 5
a) a difference of one unit in X will lead to a 5 point difference in the prediction.
b) will decrease as X increases.
c) the correlation is certain to be significant.
d) a difference of one unit in X will lead to a 12.6 point difference in the prediction.
Q:
16 The symbols a and b are frequently referred to as
a) regression coefficients.
b) constants.
c) slopes.
d) regression correlations.
Q:
15 In calculating the regression coefficients we square the errors of prediction because
a) statisticians square everything.
b) the sum of the errors would always be 0 for a great many lines we could draw.
c) squaring makes the errors more striking.
d) little errors are more important than big errors.
Q:
14 The notation (Y - ) represents
a) our best prediction.
b) the regression line.
c) the predicted value.
d) error in prediction.
Q:
13 The "best fitting line" is that regression line that
a) minimizes the errors of prediction.
b) minimizes each squared error of prediction.
c) minimizes the sum of squared errors of prediction.
d) hits the most points as it goes through the scatterplot.
Q:
12 The notation is used instead of Y
a) to indicate that the answer is only approximate.
b) to indicate that we have an equation for a straight line.
c) to indicate that the result is a prediction.
d) because this is a mathematical equation.
Q:
11+ In the previous problem your best estimate of the intercept relating the total earning from the hours worked is
a) -10.
b) 0.
c) 10.
d) We have no idea.
Q:
10+ Suppose that you sell ice cream from a cart on the street. After you pay the ice cream supplier, the regression line that predicts your ice cream profits from the number of hours you work has a slope of 15. But the man who owns the cart charges you $5 per hour in rent. How much money will you earn per hour?
a) $15
b) $10
c) $5
d) nothing
Q:
9 If we have a regression line predicting the amount of improvement in your performance as a function of the amount of tutoring you receive, an intercept of 12 would mean that
a) you need to have 12 hours of tutoring to get an A.
b) if you don"t have any tutoring, the best you can do is a grade of 12.
c) even without tutoring you will improve.
d) tutoring helps.
Q:
8 When the slope of the regression line is positive, the line goes from
a) upper left to lower right.
b) lower left to upper right.
c) the line is flat.
d) It depends on the intercept.
Q:
7 In the equation for a straight line used in the text, the slope is represented by
a) a
b) b
c) X
d) Y
Q:
6 In the equation for a straight line used in the text, the intercept is represented by
a) a
b) b
c) X
d) Y
Q:
5+ The equation for a straight line is an equation of the form
a) Y = bX a
b) Y = bX
c) Y = bX2 + a
d) Y = bX + a
Q:
4+ When we have considerable spread of the points about the regression line, the slope of that line will be _______ the slope of a similar line when there is less scatter.
a) less than
b) more than
c) the same as
d) more extreme than
Q:
3+ If the correlation between X and Y is negative, the slope of the regression equation must be
a) negative.
b) positive.
c) non-significant.
d) It could be either a or b.
Q:
2 When we make a prediction using a regression equation, our prediction is _______ on X.
a) dependent
b) conditional
c) correlated
d) both a and b
Q:
1+ When I want to make a prediction but don"t have the value of X on which to base that prediction, my best estimate is
a) the value that I calculate with a regression equation.
b) the smallest value of Y.
c) the mean of Y.
d) There is no good prediction.
Q:
66 Briefly describe the difference between the standardized beta coefficient and the unstandardized b
Q:
65 Given the following data, do you believe the regression equation would be a reliable way to predict values of Y. Explain your answer.
Q:
64 Given the data in the previous table:
a) What is the slope of the regression line?
b) What does the value of the slope mean here?
c) Is the slope significantly different from 0?
Q:
63 Answer the following questions based on the regression data in the previous table.a) What percent of variability in behavior problems is accounted for by anger?b) What percent of variability in behavior problems independent of anger?
Q:
62 Write a sentence interpreting the regression data in the following table.Dependent variable: Child Behavior Problem Score
Q:
61 Given this regression equation, = .3 X + 25, estimate the values of X given the following values of Y.a) Y = 0b) Y = 25c) Y = -30
Q:
60 Calculate SSerror for the previous data. Explain how you did it.
Q:
59 Calculate the residuals for the previous data. Explain how you did it.
Q:
58 Given the following values, calculate the regression equation.Age of car (years) Mileage 1.00 40.00 1.00 25.00 2.00 37.00 2.00 35.00 3.00 36.00 3.00 35.00 4.00 32.00 5.00 30.00 6.00 25.00 10.00 20.00
Q:
57 Given this regression equation, = .75 X + 5, estimate Y for the following values of X.a) X = 0b) X = 1c) X = -3d) X = 75
Q:
56 Residual refers to the error of prediction.
Q:
55 Using a regression equation to predict a value will always lead to highly accurate predictions.
Q:
54 When there is only one predictor variable in a regression, beta (regression coefficient) = r (correlation coefficient).
Q:
53 In a regression using standardized data, to predict health symptoms from stress, the beta = .5. This means that for every 1 point increase in stress there is half a point increase in symptoms.
Q:
52 The regression equation can be used to estimate the value of the criterion variable based on knowing the value of the predictor variable.
Q:
51 Regression is typically used to test cause-effect relationships.
Q:
50 If the association between warm parenting practices and self-esteem is .50, then 75% of the variability in self-esteem is independent of warm parenting practices.
Q:
49 If the correlation between smoking and lung cancer is .50, smoking accounts for 50% of the variability in lung cancer.
Q:
48 Regression can be used to examine both linear and curvilinear relationships.
Q:
47 Regression is only appropriate for predicting a criterion variable from one predictor variable.
Q:
46 The intercept of a regression line is
a) the value of when X=0.
b) always greater than 0.
c) significant when the correlation is significant.
d) never informative.
Q:
45 A regression line is
a) a line of covariance.
b) a correlation matrix.
c) the best fit straight line.
d) the equal to a correlation line.
Q:
44 When one refers to the degree that variable A changes as variable B changes they are referring to
a) variance.
b) regression.
c) covariance.
d) habituation.
Q:
43 A regression analysis of hours spent exercising and ounces of weight loss had a slope of 3. We would predict that
a) for every 1 hour of exercise, a person would lose 3 ounces of weight.
b) for every ounce lost, a person has to exercise for 7 hours.
c) every hour of exercise would have no effect on weight.
d) The slope cannot be used to make predictions, you need the intercept.
Q:
42+ An example in the text hypothesized that 4% of the variability in life expectancy was accounted for by variability in smoking behavior. The values of r and r2, respectively, are equal to
a) .20 and .04.
b) .04 and .16.
c) .04 and .20.
d) More information is needed.
Q:
41 In a scatterplot, an outlier is one that
a) is far to the left of the display.
b) is in the center of the display.
c) is far from the regression line.
d) is the largest value of Y.
Q:
40 If you drop a pencil randomly on a scatterplot, what aspect are you changing as you rotate the pencil about the point where it crosses the Y axis?
a) the slope.
b) the intercept.
c) the correlation.
d) the residual.
Q:
39 If you drop a pencil randomly on a scatterplot, what aspect are you changing as you move the pencil vertically on the page without rotating it?
a) the slope.
b) the intercept.
c) the correlation.
d) the residual.
Q:
38 If the slope is significant we know that
a) the intercept is not significant.
b) the intercept is significant.
c) there is a strong relationship between the two variables.
d) none of the above
Q:
37 A significant slope means that
a) the slope is positive.
b) there is a significant relationship between X and Y in the population.
c) the slope is not equal to 0 in the population.
d) both b and c
Q:
36+ If the correlation between X and Y is significant, that tells us
a) that the slope is significant.
b) that the intercept is significant.
c) that X causes Y.
d) nothing about the regression equation.
Q:
35+ Which of the following does NOT belong with the rest?
a) variance attributable to
b) variance associated with
c) variance predictable from
d) variance caused by
Q:
34+ An important thing about r2 is that it represents a measure of
a) causal relationships.
b) accountable variability.
c) the correlation.
d) statistical significance.
Q:
33 If we want to specify the percentage of the overall variability in life expectancy attributable to variability in smoking behavior, the statistic we want to look at is
Q:
32 The notation SS stands for
a) simply sensational.
b) statistical significance.
c) squared sums.
d) sum of squares.
Q:
31+ If the correlation between a body image measure and an eating disorders measure is .50, we can conclude that
a) body image has very little to do with eating disorders.
b) 50% of the variability in the eating disorders scales is associated with variability in body image.
c) one quarter of the variability in the eating disorders scores is associated with variability in body image.
d) overweight people eat too much.
Q:
30 When we use a regression equation to make a prediction, the errors that we make are often referred to as
a) residuals.
b) predictions.
c) .
d) standard errors.
Q:
29 We can think of the standard error of estimate as
a) the standard deviation of the errors that we make when using the regression equation.
b) the standard deviation of Y.
c) the variance of the errors that we would make when using the regression equation.
d) the variance of X.
Q:
28 The standard error of estimate is denoted by
a) b) c) d) none of the above
Q:
27+ The standard error of estimate is given by
a) b) c) d) none of the above
Q:
26 If we do know X, our measure of error isa) the standard deviation of Y.b) the standard deviation of X.c) the standard error of estimate.d) the standardized residual.