Howell 9e_TBChapter 10

Chapter 10—Regression MULTIPLE CHOICE QUESTIONS 10.1 + 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. 10.2 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 10.3 + 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. 10.4 + 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 10.5 + The equation for a straight line is an equation of the form a) Y = bX a b) Y = bX c) Y = bX 2 + a d) * Y = bX + a 10.6 In the equation for a straight line used in the text, the intercept is represented by a) * a b) b c) X d) Y

Test Bank 10.7 In the equation for a straight line used in the text, the slope is represented by a) a b) * b c) X d) Y 10.8 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. 10.9 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. 10.10 + 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 10.11 + 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. 10.12 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. 10.13 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. 283

Chapter 10 10.14 The notation ( Y - ˆ Y ) represents a) our best prediction. b) the regression line. c) the predicted value. d) *error in prediction. 10.15 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. 10.16 The symbols a and b are frequently referred to as a) *regression coefficients. b) constants. c) slopes. d) regression correlations. 10.17 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. 10.18 + 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 . 10.19 When we have standardized data, the slope will be denoted as a) b b) *  c) 1.0 d) r 10.20 + 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. 284