Question 1

As the standard error of estimate becomes larger, predictions become:

Accepted Answer

A)  less accurate. 
B)  more accurate. 
C)  larger. 
D)  smaller. 
A)  less accurate. 
B)  more accurate. 
C)  larger. 
D)  smaller.

Question 2

Regression is to _____ as correlation is to _____.

Accepted Answer

A)  association; causation 
B)  causation; association 
C)  relation; prediction 
D)  prediction; relation 
A)  association; causation 
B)  causation; association 
C)  relation; prediction 
D)  prediction; relation

Question 3

_____ is a useful statistical analysis for predicting behavior, and _____ is a useful technique for finding the direction and strength of a relation between two variables.

Accepted Answer

A)  Psychometrics; correlation 
B)  Correlation; regression 
C)  Regression; correlation 
D)  Psychometrics; regression 
A)  Psychometrics; correlation 
B)  Correlation; regression 
C)  Regression; correlation 
D)  Psychometrics; regression

Question 4

The standardized regression coefficient expresses the:

Accepted Answer

A)  relation between the independent and dependent variable in terms of squared units. 
B)  strength of the correlation between the two variables that are now incorporated into a regression analysis. 
C)  predicted change in the dependent variable in terms of standard deviations for an increase of 1 standard deviation in the independent variable. 
D)  likelihood of rejecting the null hypothesis with a regression analysis. 
A)  relation between the independent and dependent variable in terms of squared units. 
B)  strength of the correlation between the two variables that are now incorporated into a regression analysis. 
C)  predicted change in the dependent variable in terms of standard deviations for an increase of 1 standard deviation in the independent variable. 
D)  likelihood of rejecting the null hypothesis with a regression analysis.

Question 5

Like correlation, regression cannot prove causal direction of a relation between two variables.

Accepted Answer

A) True 
 B)False

Question 6

Regression is a type of statistical analysis that is most useful for:

Accepted Answer

A)  calculating z scores. 
B)  predicting behavior. 
C)  determining standard deviations. 
D)  finding the direction and strength of a relation between two variables. 
A)  calculating z scores. 
B)  predicting behavior. 
C)  determining standard deviations. 
D)  finding the direction and strength of a relation between two variables.

Question 7

Simple linear regression is a statistical technique that includes two or more predictor variables in a prediction equation.

Accepted Answer

A) True 
 B)False

Question 8

If the points on a scatterplot are all close to 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 9

Latent variables are what we want to research but cannot directly measure.

Accepted Answer

A) True 
 B)False

Question 10

The standard error of estimate is a measure of how accurately we predict using the regression equation or line of best fit.

Accepted Answer

A) True 
 B)False

Question 11

Predicting an individual's IQ score from two variables, for example, socioeconomic status and education level, would involve the use of:

Accepted Answer

A)  bivariate regression. 
B)  simple linear regression. 
C)  multiple regression. 
D)  nonlinear correlation. 
A)  bivariate regression. 
B)  simple linear regression. 
C)  multiple regression. 
D)  nonlinear correlation.

Question 12

Desmond thinks his new tutoring methods are highly effective compared to commercially available methods.He selects the worst students in his statistics class and tries his new tutoring strategy.Which statement describes a threat to the validity of his hypothesis even if the students do very well after the tutoring sessions?

Accepted Answer

A)  Instrumentation errors will skew the results. 
B)  Regression to the mean is likely to occur. 
C)  Confirmation bias is likely to occur. 
D)  The testing sequence is a confounding factor. 
A)  Instrumentation errors will skew the results. 
B)  Regression to the mean is likely to occur. 
C)  Confirmation bias is likely to occur. 
D)  The testing sequence is a confounding factor.

Question 13

As r2 increases, the standard error of the estimate:

Accepted Answer

A)  increases. 
B)  decreases. 
C)  stays the same. 
D)  gets closer to 1. 
A)  increases. 
B)  decreases. 
C)  stays the same. 
D)  gets closer to 1.

Question 14

In the equation Ŷ = 130 + 5(X1) + 3(X2), what is the y intercept?

Accepted Answer

A)  130 
B)  8 
C)  5 
D)  3 
A)  130 
B)  8 
C)  5 
D)  3

Question 15

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

Accepted Answer

A) True 
 B)False

Question 16

_____ refers to the accuracy of a prediction based on the regression equation or the amount of error that is eliminated compared to predictions based on the mean of the dependent variable.

Accepted Answer

A)  Predictive validity 
B)  Orthogonal regression coefficient 
C)  Reliability 
D)  Proportionate reduction in error 
A)  Predictive validity 
B)  Orthogonal regression coefficient 
C)  Reliability 
D)  Proportionate reduction in error

Question 17

According to the text, a good statistician examines the data points before proceeding and questions causality after the statistical analysis.What would that statistician be doing during each of these two phases of a regression analysis?

Accepted Answer

A)  Before the test, examine for linearity, and after the test, consider confounding variables that might help to understand cause. 
B)  Before the test, examine for errors in the data, and after the test, conjecture about possible causes using the ABC model. 
C)  Before the test, check for outliers, and after the test, run an experiment to reveal causal relations. 
D)  Before the test, create appealing visual displays of data, and after the test, create theories about causation. 
A)  Before the test, examine for linearity, and after the test, consider confounding variables that might help to understand cause. 
B)  Before the test, examine for errors in the data, and after the test, conjecture about possible causes using the ABC model. 
C)  Before the test, check for outliers, and after the test, run an experiment to reveal causal relations. 
D)  Before the test, create appealing visual displays of data, and after the test, create theories about causation.

Question 18

Which is the correct formula for the proportionate reduction in error?

Accepted Answer

A)  (b) $\frac { \sqrt { S S _ { x } } } { \sqrt { S S _ { y } } }$ 
B)  $\frac { X - M _ { n } } { S D _ { n } }$ 
C)  a + bX 
D)  $( \operatorname { SStotan } - \mathrm { SSen \sigma } )$
SNtral 
A)  (b) $\frac { \sqrt { S S _ { x } } } { \sqrt { S S _ { y } } }$ 
B)  $\frac { X - M _ { n } } { S D _ { n } }$ 
C)  a + bX 
D)  $( \operatorname { SStotan } - \mathrm { SSen \sigma } )$
SNtral

Question 19

Y is the symbol for a(n) _____, and Ŷ is the symbol for a(n) _____.

Accepted Answer

A)  predicted score on the independent variable; observed score on the independent variable 
B)  observed score on the dependent variable; predicted score on the dependent variable 
C)  observed score on the independent variable; predicted score on the dependent variable 
D)  predicted score on the dependent variable; observed score on the independent variable 
A)  predicted score on the independent variable; observed score on the independent variable 
B)  observed score on the dependent variable; predicted score on the dependent variable 
C)  observed score on the independent variable; predicted score on the dependent variable 
D)  predicted score on the dependent variable; observed score on the independent variable

Question 20

You want to predict your score on the statistics final exam using your grade point average for the semester.Which statistical technique is best for this type of analysis?

Accepted Answer

A)  bar graph 
B)  correlation 
C)  simple linear regression 
D)  standardized z scores 
A)  bar graph 
B)  correlation 
C)  simple linear regression 
D)  standardized z scores

As the standard error of estimate becomes larger, predictions become:

Regression is to _ as correlation is to _.

_ is a useful statistical analysis for predicting behavior, and _ is a useful technique for finding the direction and strength of a relation between two variables.

The standardized regression coefficient expresses the:

Like correlation, regression cannot prove causal direction of a relation between two variables.

Regression is a type of statistical analysis that is most useful for:

Simple linear regression is a statistical technique that includes two or more predictor variables in a prediction equation.

If the points on a scatterplot are all close to the regression line:

Latent variables are what we want to research but cannot directly measure.

The standard error of estimate is a measure of how accurately we predict using the regression equation or line of best fit.

Predicting an individual's IQ score from two variables, for example, socioeconomic status and education level, would involve the use of:

As r² increases, the standard error of the estimate:

In the equation Ŷ = 130 + 5(X₁) + 3(X₂), what is the y intercept?

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

_____ refers to the accuracy of a prediction based on the regression equation or the amount of error that is eliminated compared to predictions based on the mean of the dependent variable.

According to the text, a good statistician examines the data points before proceeding and questions causality after the statistical analysis.What would that statistician be doing during each of these two phases of a regression analysis?

Which is the correct formula for the proportionate reduction in error?

Y is the symbol for a(n) _, and Ŷ is the symbol for a(n) _.

You want to predict your score on the statistics final exam using your grade point average for the semester.Which statistical technique is best for this type of analysis?

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

As the standard error of estimate becomes larger, predictions become:

Regression is to _____ as correlation is to _____.

_____ is a useful statistical analysis for predicting behavior, and _____ is a useful technique for finding the direction and strength of a relation between two variables.

The standardized regression coefficient expresses the:

Like correlation, regression cannot prove causal direction of a relation between two variables.

Regression is a type of statistical analysis that is most useful for:

Simple linear regression is a statistical technique that includes two or more predictor variables in a prediction equation.

If the points on a scatterplot are all close to the regression line:

Latent variables are what we want to research but cannot directly measure.

The standard error of estimate is a measure of how accurately we predict using the regression equation or line of best fit.

Predicting an individual's IQ score from two variables, for example, socioeconomic status and education level, would involve the use of:

As r2 increases, the standard error of the estimate:

In the equation Ŷ = 130 + 5(X1) + 3(X2), what is the y intercept?

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

_____ refers to the accuracy of a prediction based on the regression equation or the amount of error that is eliminated compared to predictions based on the mean of the dependent variable.

According to the text, a good statistician examines the data points before proceeding and questions causality after the statistical analysis.What would that statistician be doing during each of these two phases of a regression analysis?

Which is the correct formula for the proportionate reduction in error?

Y is the symbol for a(n) _____, and Ŷ is the symbol for a(n) _____.

You want to predict your score on the statistics final exam using your grade point average for the semester.Which statistical technique is best for this type of analysis?

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

Regression is to _ as correlation is to _.

_ is a useful statistical analysis for predicting behavior, and _ is a useful technique for finding the direction and strength of a relation between two variables.

As r² increases, the standard error of the estimate:

In the equation Ŷ = 130 + 5(X₁) + 3(X₂), what is the y intercept?

Y is the symbol for a(n) _, and Ŷ is the symbol for a(n) _.