Deck 15: Advanced Statistical Techniques

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Question
If you have count data where the variance is smaller than the mean, however you have an excess of zero counts, which model should you choose?

A) Poisson regression
B) Zero-inflated Poisson regression
C) Negative binomial regression
D) Zero-inflated binomial regression
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Question
What is skewness?

A) A pointy distribution
B) A flat distribution
C) Lack of symmetry of a distribution
D) A platykurtic distribution
Question
What happens if you raise a variable to a power (p)< 1

A) We will reduce negative skew
B) The data will stay the same
C) We will reduce positive skew
D) We will make a flat distribution pointy
Question
Which one of the following methods is recommended to use when running an analysis containing missing data?

A) Pairwise deletion
B) Multiple imputation
C) Single imputation
D) Dummy variable adjustment
Question
What does multiple imputation do?

A) Removes any observation that has missing one or more of the variables in the model
B) Uses information from all values from other variables to predict values on variable(s) with low N
C) Calculations are based on all available data pairwise for all pairs of variables
D) Inserts a new value for all missing observations in a variable (e.g., 0 or the mean), as well as including a dummy variable coded 1 if the original data is missing
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Deck 15: Advanced Statistical Techniques
1
If you have count data where the variance is smaller than the mean, however you have an excess of zero counts, which model should you choose?

A) Poisson regression
B) Zero-inflated Poisson regression
C) Negative binomial regression
D) Zero-inflated binomial regression
B
2
What is skewness?

A) A pointy distribution
B) A flat distribution
C) Lack of symmetry of a distribution
D) A platykurtic distribution
C
3
What happens if you raise a variable to a power (p)< 1

A) We will reduce negative skew
B) The data will stay the same
C) We will reduce positive skew
D) We will make a flat distribution pointy
C
4
Which one of the following methods is recommended to use when running an analysis containing missing data?

A) Pairwise deletion
B) Multiple imputation
C) Single imputation
D) Dummy variable adjustment
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5
What does multiple imputation do?

A) Removes any observation that has missing one or more of the variables in the model
B) Uses information from all values from other variables to predict values on variable(s) with low N
C) Calculations are based on all available data pairwise for all pairs of variables
D) Inserts a new value for all missing observations in a variable (e.g., 0 or the mean), as well as including a dummy variable coded 1 if the original data is missing
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Unlock for access to all 5 flashcards in this deck.