Question 1

Structural equation modeling involves:

Accepted Answer

A)  quantifying how well data fit a specified theory. 
B)  using a single independent variable to predict multiple dependent variables. 
C)  using multiple independent variables to predict a single dependent variable. 
D)  determining the amount of error present in a regression. 
A)  quantifying how well data fit a specified theory. 
B)  using a single independent variable to predict multiple dependent variables. 
C)  using multiple independent variables to predict a single dependent variable. 
D)  determining the amount of error present in a regression.

Question 2

The simple linear regression equation uses the following formula: Ŷ = a + b(X).

Accepted Answer

A) True 
 B)False

Question 3

Which statement about the slope of the simple linear regression line is true?

Accepted Answer

A)  It has the same sign as the correlation. 
B)  It is expressed as a standardized value. 
C)  The square of the slope is the proportion of variation in Y explained by X. 
D)  It is the value of Y when X is zero. 
A)  It has the same sign as the correlation. 
B)  It is expressed as a standardized value. 
C)  The square of the slope is the proportion of variation in Y explained by X. 
D)  It is the value of Y when X is zero.

Question 4

Structural equation modeling graphs depict a _____ among several variables, demonstrating how all of the variables combine to create a _____.

Accepted Answer

A)  network of relations; statistical model 
B)  causal chain; correlation 
C)  box plot; theory 
D)  scatterplot; theoretical model 
A)  network of relations; statistical model 
B)  causal chain; correlation 
C)  box plot; theory 
D)  scatterplot; theoretical model

Question 5

Good physical fitness has been correlated with many medical benefits, particularly in relation to blood pressure.A regression equation predicting an individual's blood pressure based on physical fitness level results in a negatively sloped line of best fit.What does this statement mean?

Accepted Answer

A)  People who are physically fit are predicted to have low blood pressure, while people who have low fitness levels are predicted to have high blood pressure. 
B)  People who have low fitness levels have low blood pressure, while people who are highly active probably have high blood pressure. 
C)  There is no correlation between blood pressure and physical fitness. 
D)  Low fitness levels cause high blood pressure; high fitness levels cause low blood pressure. 
A)  People who are physically fit are predicted to have low blood pressure, while people who have low fitness levels are predicted to have high blood pressure. 
B)  People who have low fitness levels have low blood pressure, while people who are highly active probably have high blood pressure. 
C)  There is no correlation between blood pressure and physical fitness. 
D)  Low fitness levels cause high blood pressure; high fitness levels cause low blood pressure.

Question 6

The predicted z score on the dependent variable will always be closer to the z score for the independent variable than its mean.

Accepted Answer

A) True 
 B)False

Question 7

Assume a positive correlation is found between the number of hours students spend studying for an exam and their grade on the exam.If the regression equation for these data is calculated and the y intercept is 65, what conclusion can be drawn?

Accepted Answer

A)  The standard error of the estimate is low. 
B)  The regression line crosses the x-axis at a score of 65. 
C)  The slope of the regression line is 65 when students do not study at all. 
D)  When students do not study at all, we would predict a score of 65 on the exam. 
A)  The standard error of the estimate is low. 
B)  The regression line crosses the x-axis at a score of 65. 
C)  The slope of the regression line is 65 when students do not study at all. 
D)  When students do not study at all, we would predict a score of 65 on the exam.

Question 8

The intercept is the predicted value for X when Y is equal to 0.

Accepted Answer

A) True 
 B)False

Question 9

Body weight can be predicted based on the amount of calories consumed by an individual due to the positive correlation between the two variables.When looking at the line of best fit for the linear regression, the data points are clustered close together.Predicted shyness based on the number of friendships a person has is also correlated, but the data points are more scattered around the line of best fit, showing a general negative correlation.Which has the higher predictive power and why?

Accepted Answer

A)  calories consumed and body weight, because it is a positive correlation 
B)  calories consumed and body weight, because the variance is lower 
C)  shyness and number of friendships, because it is a negative correlation 
D)  shyness and number of friendships, because the variance is lower 
A)  calories consumed and body weight, because it is a positive correlation 
B)  calories consumed and body weight, because the variance is lower 
C)  shyness and number of friendships, because it is a negative correlation 
D)  shyness and number of friendships, because the variance is lower

Question 10

Multiple regression differs from simple linear regression because it:

Accepted Answer

A)  repeats a linear regression several times, which can improve the results by averaging. 
B)  uses more than one independent variable to make predictions. 
C)  uses higher order polynomials to make predictions. 
D)  employs the mathematical framework of calculus. 
A)  repeats a linear regression several times, which can improve the results by averaging. 
B)  uses more than one independent variable to make predictions. 
C)  uses higher order polynomials to make predictions. 
D)  employs the mathematical framework of calculus.

Question 11

The predicted z score on the dependent variable will always be closer to its mean than the z score for the independent variable.

Accepted Answer

A) True 
 B)False

Question 12

If the points on a scatterplot are all far away from the regression line:

Accepted Answer

A)  the standard error of the estimate is small. 
B)  r is a positive number. 
C)  r is close to 0. 
D)  the standard error of the estimate is large. 
A)  the standard error of the estimate is small. 
B)  r is a positive number. 
C)  r is close to 0. 
D)  the standard error of the estimate is large.

Question 13

Use the following to answer questions 
The scatterplot and regression line on the left depict the relation between a state's expenditure per student and the average SAT scores for students in the state.The scatterplot and regression line on the right depict the relation between students' SAT Verbal and SAT Quantitative scores.
Figure: Standard Error Comparisons    
-(Figure: Standard Error Comparisons) Based on the scatterplots, for which prediction is the r2 greater?

Accepted Answer

A)  state expenditure per student from composite SAT scores 
B)  composite SAT scores from the state's expenditure per student 
C)  SAT Quantitative scores from SAT Verbal scores 
D)  SAT Verbal scores from SAT Quantitative scores 
A)  state expenditure per student from composite SAT scores 
B)  composite SAT scores from the state's expenditure per student 
C)  SAT Quantitative scores from SAT Verbal scores 
D)  SAT Verbal scores from SAT Quantitative scores

Question 14

Simple linear regression allows one to:

Accepted Answer

A)  determine the relation among four or more variables. 
B)  predict an individual's score on a dependent variable from her score on multiple independent variables. 
C)  predict an individual's score on the dependent variable from her score on the independent variable. 
D)  infer the direction of causal relations. 
A)  determine the relation among four or more variables. 
B)  predict an individual's score on a dependent variable from her score on multiple independent variables. 
C)  predict an individual's score on the dependent variable from her score on the independent variable. 
D)  infer the direction of causal relations.

Question 15

In the computer printouts presented in the text, information for the multiple regression equation can be found in the column labeled:

Accepted Answer

A)  standardized coefficients. 
B)  B under unstandardized coefficients. 
C)  beta under standardized coefficients. 
D)  sig. 
A)  standardized coefficients. 
B)  B under unstandardized coefficients. 
C)  beta under standardized coefficients. 
D)  sig.

Question 16

The statistic that describes the variability of a set of data points to the line of best fit in a linear regression is the standard:

Accepted Answer

A)  deviation. 
B)  deviation of the estimate. 
C)  error of the estimate. 
D)  error. 
A)  deviation. 
B)  deviation of the estimate. 
C)  error of the estimate. 
D)  error.

Question 17

Data from 147 colleges from 1995 to 2005 (Lee, 2008) were used to predict the endowments (in billions) to a college from the average SAT score of students attending the college.The resulting regression equation was Ŷ = -20.46 + 4.06(X).This regression indicates that:

Accepted Answer

A)  most colleges have very high endowments. 
B)  for every one-point increase in SAT scores, a college can expect 4.06 billion more in endowments. 
C)  for every one-dollar increase in endowments, the college can expect a half-point increase in SAT scores. 
D)  for every one-point increase in SAT scores, a college can expect 20.46 billion fewer in endowments. 
A)  most colleges have very high endowments. 
B)  for every one-point increase in SAT scores, a college can expect 4.06 billion more in endowments. 
C)  for every one-dollar increase in endowments, the college can expect a half-point increase in SAT scores. 
D)  for every one-point increase in SAT scores, a college can expect 20.46 billion fewer in endowments.

Question 18

Proportionate reduction in error can be symbolized by:

Accepted Answer

A)  r2. 
B)  d. 
C)  r. 
D)  b. 
A)  r2. 
B)  d. 
C)  r. 
D)  b.

Question 19

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 1000 pieces, with a size value of 0.5 inches, how long will it take to complete?

Accepted Answer

A)  6.3 hours 
B)  7.9 hours 
C)  11.9 hours 
D)  14.9 hours 
A)  6.3 hours 
B)  7.9 hours 
C)  11.9 hours 
D)  14.9 hours

Question 20

The idea that patterns of extreme scores will balance out if sampling continues indefinitely or trends are looked at over the long run is known as:

Accepted Answer

A)  attrition. 
B)  regression to the mean. 
C)  history of the mean. 
D)  correlation. 
A)  attrition. 
B)  regression to the mean. 
C)  history of the mean. 
D)  correlation.

Structural equation modeling involves:

The simple linear regression equation uses the following formula: Ŷ = a + b(X).

Which statement about the slope of the simple linear regression line is true?

Structural equation modeling graphs depict a _ among several variables, demonstrating how all of the variables combine to create a _.

Good physical fitness has been correlated with many medical benefits, particularly in relation to blood pressure.A regression equation predicting an individual's blood pressure based on physical fitness level results in a negatively sloped line of best fit.What does this statement mean?

The predicted z score on the dependent variable will always be closer to the z score for the independent variable than its mean.

Assume a positive correlation is found between the number of hours students spend studying for an exam and their grade on the exam.If the regression equation for these data is calculated and the y intercept is 65, what conclusion can be drawn?

The intercept is the predicted value for X when Y is equal to 0.

Multiple regression differs from simple linear regression because it:

The predicted z score on the dependent variable will always be closer to its mean than the z score for the independent variable.

If the points on a scatterplot are all far away from the regression line:

Simple linear regression allows one to:

In the computer printouts presented in the text, information for the multiple regression equation can be found in the column labeled:

The statistic that describes the variability of a set of data points to the line of best fit in a linear regression is the standard:

Data from 147 colleges from 1995 to 2005 (Lee, 2008) were used to predict the endowments (in billions) to a college from the average SAT score of students attending the college.The resulting regression equation was Ŷ = -20.46 + 4.06(X).This regression indicates that:

Proportionate reduction in error can be symbolized by:

The idea that patterns of extreme scores will balance out if sampling continues indefinitely or trends are looked at over the long run is known as:

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

Structural equation modeling involves:

The simple linear regression equation uses the following formula: Ŷ = a + b(X).

Which statement about the slope of the simple linear regression line is true?

Structural equation modeling graphs depict a _____ among several variables, demonstrating how all of the variables combine to create a _____.

Good physical fitness has been correlated with many medical benefits, particularly in relation to blood pressure.A regression equation predicting an individual's blood pressure based on physical fitness level results in a negatively sloped line of best fit.What does this statement mean?

The predicted z score on the dependent variable will always be closer to the z score for the independent variable than its mean.

Assume a positive correlation is found between the number of hours students spend studying for an exam and their grade on the exam.If the regression equation for these data is calculated and the y intercept is 65, what conclusion can be drawn?

The intercept is the predicted value for X when Y is equal to 0.

Multiple regression differs from simple linear regression because it:

The predicted z score on the dependent variable will always be closer to its mean than the z score for the independent variable.

If the points on a scatterplot are all far away from the regression line:

Simple linear regression allows one to:

In the computer printouts presented in the text, information for the multiple regression equation can be found in the column labeled:

The statistic that describes the variability of a set of data points to the line of best fit in a linear regression is the standard:

Data from 147 colleges from 1995 to 2005 (Lee, 2008) were used to predict the endowments (in billions) to a college from the average SAT score of students attending the college.The resulting regression equation was Ŷ = -20.46 + 4.06(X).This regression indicates that:

Proportionate reduction in error can be symbolized by:

The idea that patterns of extreme scores will balance out if sampling continues indefinitely or trends are looked at over the long run is known as:

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

Structural equation modeling graphs depict a _ among several variables, demonstrating how all of the variables combine to create a _.