Question 1

What is suggested by very high standard errors in parameter estimates in logistic regression?

Accepted Answer

A)  Shrinkage 
B)  Multicollinearity 
C)  An outlier 
D)  A dummy variable 
A)  Shrinkage 
B)  Multicollinearity 
C)  An outlier 
D)  A dummy variable

Question 2

In determining the odds that a specific type of antibiotic preparation will cure otitis media in children, the odds ratio value was calculated to be greater than 1. With increases in the predictor, what will this mean for the outcome of the result?

Accepted Answer

A)  A greater chance of a cure being achieved than if not treated. 
B)  A lesser chance of a cure being achieved. 
C)  A lesser chance of treatment being effective. 
D)  None of the above. 
A)  A greater chance of a cure being achieved than if not treated. 
B)  A lesser chance of a cure being achieved. 
C)  A lesser chance of treatment being effective. 
D)  None of the above.

Question 3

A researcher was interested in predicting whether a person would attempt to commit suicide (score = 1) or not (score = 0) from their depression scores. They found that the value of exp b was 2.56. How should this value be interpreted?

Accepted Answer

A)  If two people have depression scores that differ by 1 unit, then the odds of the person with the higher score attempting suicide are 2.56 higher than for the other person. 
B)  2.56 times more people who attempted suicide scored highly on depression. 
C)  If two people have depression scores that differ by 1 unit, then the odds of the person with the higher score attempting suicide are 2.56 lower than for the other person. 
D)  Suicide rates are 2.56 times as high in depressed people. 
A)  If two people have depression scores that differ by 1 unit, then the odds of the person with the higher score attempting suicide are 2.56 higher than for the other person. 
B)  2.56 times more people who attempted suicide scored highly on depression. 
C)  If two people have depression scores that differ by 1 unit, then the odds of the person with the higher score attempting suicide are 2.56 lower than for the other person. 
D)  Suicide rates are 2.56 times as high in depressed people.

What is suggested by very high standard errors in parameter estimates in logistic regression?

In determining the odds that a specific type of antibiotic preparation will cure otitis media in children, the odds ratio value was calculated to be greater than 1. With increases in the predictor, what will this mean for the outcome of the result?

A researcher was interested in predicting whether a person would attempt to commit suicide (score = 1) or not (score = 0) from their depression scores. They found that the value of exp b was 2.56. How should this value be interpreted?

Why Is My Evil Lecturer Forcing Me to Learn Statistics

The Spine of Statistics

The Phoenix of Statistics

The Ibm Spss Statistics Environment

Exploring Data With Graphs

The Beast of Bias

Non-Parametric Models

Correlation

Regression

Comparing Two Means

Moderation,mediation and More Regression

Glm 1: Comparing Several Independent Means

Glm 2: Comparing Means Adjusted for Other Predictors Analysis of Covariance

Glm 3: Factorial Designs

Glm 4: Repeated-Measures Designs

Glm 5: Mixed Designs

Multivariate Analysis of Variance Manova

Exploratory Factor Analysis

Categorical Outcomes: Chi-Square and Loglinear Analysis

Multilevel Linear Models

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Exam 20: Categorical Outcomes: Logistic Regression

What is suggested by very high standard errors in parameter estimates in logistic regression?

In determining the odds that a specific type of antibiotic preparation will cure otitis media in children, the odds ratio value was calculated to be greater than 1. With increases in the predictor, what will this mean for the outcome of the result?

A researcher was interested in predicting whether a person would attempt to commit suicide (score = 1) or not (score = 0) from their depression scores. They found that the value of exp b was 2.56. How should this value be interpreted?

Why Is My Evil Lecturer Forcing Me to Learn Statistics

The Spine of Statistics

The Phoenix of Statistics

The Ibm Spss Statistics Environment

Exploring Data With Graphs

The Beast of Bias

Non-Parametric Models

Correlation

Regression

Comparing Two Means

Moderation,mediation and More Regression

Glm 1: Comparing Several Independent Means

Glm 2: Comparing Means Adjusted for Other Predictors Analysis of Covariance

Glm 3: Factorial Designs

Glm 4: Repeated-Measures Designs

Glm 5: Mixed Designs

Multivariate Analysis of Variance Manova

Exploratory Factor Analysis

Categorical Outcomes: Chi-Square and Loglinear Analysis

Multilevel Linear Models

Filters