Question 1

A high standard error of the estimate indicates that the:

Accepted Answer

A)  mean is not a good representation of the sample data. 
B)  observed Ys will vary greatly from the predicted Ys. 
C)  sample mean is not a good representation of the population mean. 
D)  observed Ys will cluster closely around the regression line. 
A)  mean is not a good representation of the sample data. 
B)  observed Ys will vary greatly from the predicted Ys. 
C)  sample mean is not a good representation of the population mean. 
D)  observed Ys will cluster closely around the regression line.

Question 2

The slope is the amount that Y is predicted to increase for an increase of 1 in X.

Accepted Answer

A) True 
 B)False

Question 3

The correlation between the number of ounces of candy a child consumes weekly and the number of cavities the child has at the age of 13 is 0.78.If a child has a z score of -0.85 on the candy-consumed variable, the z score on the variable number of cavities would be predicted to be:

Accepted Answer

A)  -0.66. 
B)  -0.92. 
C)  0.66. 
D)  0.92. 
A)  -0.66. 
B)  -0.92. 
C)  0.66. 
D)  0.92.

Question 4

The regression line is the line that:

Accepted Answer

A)  minimizes error in predicting scores on the dependent variable. 
B)  is the mean of the dependent variable. 
C)  minimizes error in predicting scores on the independent variable. 
D)  minimizes the correlation coefficient. 
A)  minimizes error in predicting scores on the dependent variable. 
B)  is the mean of the dependent variable. 
C)  minimizes error in predicting scores on the independent variable. 
D)  minimizes the correlation coefficient.

Question 5

Dr.Garoule is trying to determine which of his patients has the highest likelihood of depression.He calculates a linear regression equation with the scores on an anxiety measure, which are positively correlated with scores on a scale measuring depression.Dr.Garoule converts patient D's anxiety score to a z score and predicts the z score for the depression scale to be -0.35.Is patient D's raw score for depression above or below the mean and why?

Accepted Answer

A)  It is below the mean because the z score is negative. 
B)  It is above the mean because the z score is negative. 
C)  It is below the mean because the z score is positive. 
D)  It is above the mean because the z score is positive. 
A)  It is below the mean because the z score is negative. 
B)  It is above the mean because the z score is negative. 
C)  It is below the mean because the z score is positive. 
D)  It is above the mean because the z score is positive.

Question 6

The intercept is the predicted value for Y when X is equal to 1.

Accepted Answer

A) True 
 B)False

Question 7

The arrows in a structural equation model indicate the:

Accepted Answer

A)  causal direction of a relation. 
B)  strength and direction of a predictive relation. 
C)  number of variables that determine the prediction equation. 
D)  latent variables. 
A)  causal direction of a relation. 
B)  strength and direction of a predictive relation. 
C)  number of variables that determine the prediction equation. 
D)  latent variables.

Question 8

If the standard error of the estimate is zero, the relation between two variables is:

Accepted Answer

A)  curvilinear. 
B)  imperfect. 
C)  perfect. 
D)  unknown. 
A)  curvilinear. 
B)  imperfect. 
C)  perfect. 
D)  unknown.

Question 9

Regression capitalizes on correlation by using what is known about the relation between two variables to make predictions beyond those variables.

Accepted Answer

A) True 
 B)False

Question 10

When a regression equation includes just one independent variable, the value of the standardized regression coefficient is:

Accepted Answer

A)  equal to the slope of the regression equation. 
B)  the inverse of the correlation coefficient. 
C)  the same as the Pearson correlation coefficient. 
D)  equal to the y-intercept of the regression equation. 
A)  equal to the slope of the regression equation. 
B)  the inverse of the correlation coefficient. 
C)  the same as the Pearson correlation coefficient. 
D)  equal to the y-intercept of the regression equation.

Question 11

A multiple regression analysis revealed the following equation relating the time (in hours) it takes to complete a puzzle based on the number and size of pieces: Ŷ = 1.5 + 0.014 (Xnumber of pieces) - 1.2 (Ysize of pieces).If a puzzle has 500 pieces, with a size value of 0.5 inches, how long will it take to complete?

Accepted Answer

A)  6.3 hours 
B)  7.1 hours 
C)  7.9 hours 
D)  9.3 hours 
A)  6.3 hours 
B)  7.1 hours 
C)  7.9 hours 
D)  9.3 hours

Question 12

As the correlation coefficient becomes stronger, proportionate reduction in error:

Accepted Answer

A)  becomes smaller. 
B)  becomes larger. 
C)  is unaffected. 
D)  becomes more variable. 
A)  becomes smaller. 
B)  becomes larger. 
C)  is unaffected. 
D)  becomes more variable.

Question 13

Correlation involves relating variables, whereas regression involves prediction.

Accepted Answer

A) True 
 B)False

Question 14

The standardized regression coefficient expresses a predicted change in the dependent variable in terms of:

Accepted Answer

A)  standard deviation units. 
B)  slope. 
C)  a one-unit change in the independent variable. 
D)  error units. 
A)  standard deviation units. 
B)  slope. 
C)  a one-unit change in the independent variable. 
D)  error units.

Question 15

The proportionate reduction in error is a measure of the:

Accepted Answer

A)  amount of variance in the dependent variable explained by the independent variable. 
B)  correlation between two variables. 
C)  amount of variance in the independent variable explained by the dependent variable. 
D)  slope of a regression line. 
A)  amount of variance in the dependent variable explained by the independent variable. 
B)  correlation between two variables. 
C)  amount of variance in the independent variable explained by the dependent variable. 
D)  slope of a regression line.

Question 16

In the social sciences, there are numerous variables that can be discussed and considered as important phenomena, but they cannot be observed directly.These are called _____ variables.However, _____ variables, which can be observed and measured, are used to assess the intangible variables.

Accepted Answer

A)  intangible; tangible 
B)  latent; manifest 
C)  tangible; intangible 
D)  manifest; latent 
A)  intangible; tangible 
B)  latent; manifest 
C)  tangible; intangible 
D)  manifest; latent

Question 17

Dr.Kim thinks his regression equation is very accurate, but he wonders if perhaps the mean is just as good at predicting scores as the regression equation.He knows that the correlation coefficient (r) for the two variables is quite high, -0.82.Should Dr.Kim use the mean or the regression equation to predict scores?

Accepted Answer

A)  He should use the regression equation because the proportionate reduction in error (r2) is low. 
B)  He should use the regression equation because the proportionate reduction in error (r2) is high. 
C)  He should use the mean because the proportionate reduction in error (r2) is low. 
D)  He should use the mean because the proportionate reduction in error (r2) is high. 
A)  He should use the regression equation because the proportionate reduction in error (r2) is low. 
B)  He should use the regression equation because the proportionate reduction in error (r2) is high. 
C)  He should use the mean because the proportionate reduction in error (r2) is low. 
D)  He should use the mean because the proportionate reduction in error (r2) is high.

Question 18

In a 2008 article by Hsiu-Ling Lee, data from 147 colleges from 1995 to 2005 were used to predict endowments to a college from the average SAT score of students attending the college, among other variables.The resulting regression equation for just these variables was Ŷ = -20.46 + 4.06(X).Using the regression equation, what would be predicted to be the endowments (in billions) to a college whose students' average SAT score is 1020?

Accepted Answer

A)  3.6 billion 
B)  4,120.74 billion 
C)  4,161.66 billion 
D)  20,865.14 billion 
A)  3.6 billion 
B)  4,120.74 billion 
C)  4,161.66 billion 
D)  20,865.14 billion

Question 19

In the equation for a line in statistics, the _____ is the predicted amount of increase for Y when X is increased by 1, and the _____ is the predicted value for Y when X crosses the y-axis (X = 0).

Accepted Answer

A)  intercept; slope 
B)  intercept; standard error 
C)  slope; standard error 
D)  slope; intercept 
A)  intercept; slope 
B)  intercept; standard error 
C)  slope; standard error 
D)  slope; intercept

Question 20

The standardized regression coefficient, which is equal to the Pearson correlation coefficient in a simply linear regression, is also called:

Accepted Answer

A)  alpha. 
B)  standardized deviation prediction. 
C)  beta weight. 
D)  slope. 
A)  alpha. 
B)  standardized deviation prediction. 
C)  beta weight. 
D)  slope.

A high standard error of the estimate indicates that the:

The slope is the amount that Y is predicted to increase for an increase of 1 in X.

The correlation between the number of ounces of candy a child consumes weekly and the number of cavities the child has at the age of 13 is 0.78.If a child has a z score of -0.85 on the candy-consumed variable, the z score on the variable number of cavities would be predicted to be:

The regression line is the line that:

The intercept is the predicted value for Y when X is equal to 1.

The arrows in a structural equation model indicate the:

If the standard error of the estimate is zero, the relation between two variables is:

Regression capitalizes on correlation by using what is known about the relation between two variables to make predictions beyond those variables.

When a regression equation includes just one independent variable, the value of the standardized regression coefficient is:

As the correlation coefficient becomes stronger, proportionate reduction in error:

Correlation involves relating variables, whereas regression involves prediction.

The standardized regression coefficient expresses a predicted change in the dependent variable in terms of:

The proportionate reduction in error is a measure of the:

In the social sciences, there are numerous variables that can be discussed and considered as important phenomena, but they cannot be observed directly.These are called _ variables.However, _ variables, which can be observed and measured, are used to assess the intangible variables.

In the equation for a line in statistics, the _ is the predicted amount of increase for Y when X is increased by 1, and the _ is the predicted value for Y when X crosses the y-axis (X = 0).

The standardized regression coefficient, which is equal to the Pearson correlation coefficient in a simply linear regression, is also called:

An Introduction to Statistics and Research Design

Frequency Distributions

Visual Displays of Data

Central Tendency and Variability

Sampling and Probability

The Normal Curve, Standardization and Z Scores

Hypothesis Testing With Z Tests

Confidence Intervals, Effect Size, and Statistical Power

The Single-Sample T Test

The Paired-Samples T Test

The Independent-Samples T Test

One-Way Between-Groups Anova

One-Way Within-Groups Anova

Two-Way Between-Groups Anova

Correlation

Chi-Square Tess

Choosing and Reporting Statistics

Filters

Exam 16: Regression

A high standard error of the estimate indicates that the:

The slope is the amount that Y is predicted to increase for an increase of 1 in X.

The correlation between the number of ounces of candy a child consumes weekly and the number of cavities the child has at the age of 13 is 0.78.If a child has a z score of -0.85 on the candy-consumed variable, the z score on the variable number of cavities would be predicted to be:

The regression line is the line that:

The intercept is the predicted value for Y when X is equal to 1.

The arrows in a structural equation model indicate the:

If the standard error of the estimate is zero, the relation between two variables is:

Regression capitalizes on correlation by using what is known about the relation between two variables to make predictions beyond those variables.

When a regression equation includes just one independent variable, the value of the standardized regression coefficient is:

As the correlation coefficient becomes stronger, proportionate reduction in error:

Correlation involves relating variables, whereas regression involves prediction.

The standardized regression coefficient expresses a predicted change in the dependent variable in terms of:

The proportionate reduction in error is a measure of the:

In the social sciences, there are numerous variables that can be discussed and considered as important phenomena, but they cannot be observed directly.These are called _____ variables.However, _____ variables, which can be observed and measured, are used to assess the intangible variables.

In the equation for a line in statistics, the _____ is the predicted amount of increase for Y when X is increased by 1, and the _____ is the predicted value for Y when X crosses the y-axis (X = 0).

The standardized regression coefficient, which is equal to the Pearson correlation coefficient in a simply linear regression, is also called:

An Introduction to Statistics and Research Design

Frequency Distributions

Visual Displays of Data

Central Tendency and Variability

Sampling and Probability

The Normal Curve, Standardization and Z Scores

Hypothesis Testing With Z Tests

Confidence Intervals, Effect Size, and Statistical Power

The Single-Sample T Test

The Paired-Samples T Test

The Independent-Samples T Test

One-Way Between-Groups Anova

One-Way Within-Groups Anova

Two-Way Between-Groups Anova

Correlation

Chi-Square Tess

Choosing and Reporting Statistics

Filters

In the social sciences, there are numerous variables that can be discussed and considered as important phenomena, but they cannot be observed directly.These are called _ variables.However, _ variables, which can be observed and measured, are used to assess the intangible variables.

In the equation for a line in statistics, the _ is the predicted amount of increase for Y when X is increased by 1, and the _ is the predicted value for Y when X crosses the y-axis (X = 0).