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1) Estimating Relationships - There are many statistical tools used to estimate relationships between variables. These include covariance, correlation, ANOVA, simple linear regression, and multiple regression. In the table below, enter a Yes (or Y) or No (or N) in each box to indicate the best response. If a box does not apply, enter NA. Covariance Example: Works well with a categorical dependent variable. Example: Can be used to estimation direction of relation between variables Works well with one or more categorical variables Can be used to measure the magnitude of relationship between variables Works well with mostly continuous variables Estimated relationship depends on scale Can be used with multiple independent variables ΝΑ (no dependent variable) Correlation ΝΑ (no dependent variable) ANOVA N (should be continuous dependent variable) Simple Linear Regression N (logistic regression) Multiple Regression N (logistic regression)

1) Estimating Relationships - There are many statistical tools used to estimate relationships between variables. These include covariance, correlation, ANOVA, simple linear regression, and multiple regression. In the table below, enter a Yes (or Y) or No (or N) in each box to indicate the best response. If a box does not apply, enter NA. Covariance Example: Works well with a categorical dependent variable. Example: Can be used to estimation direction of relation between variables Works well with one or more categorical variables Can be used to measure the magnitude of relationship between variables Works well with mostly continuous variables Estimated relationship depends on scale Can be used with multiple independent variables ΝΑ (no dependent variable) Correlation ΝΑ (no dependent variable) ANOVA N (should be continuous dependent variable) Simple Linear Regression N (logistic regression) Multiple Regression N (logistic regression)

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter4: Calculating The Derivative
Section4.CR: Chapter 4 Review
Problem 88CR
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  • Estimating Relationships – There are many statistical tools used to estimate relationships between variables. These include covariance, correlation, ANOVA, simple linear regression, and multiple regression. In the table below, enter a Yes (or Y) or No (or N) in each box to indicate the best response. If a box does not apply, enter NA.
1) Estimating Relationships - There are many statistical tools used to estimate relationships between variables. These include covariance, correlation, ANOVA, simple linear regression, and multiple regression. In the table below, enter a Yes (or Y) or No (or N) in each box to indicate the best response. If a box does not apply, enter NA. Covariance Example: Works well with a categorical dependent variable. Example: Can be used to estimation direction of relation between variables Works well with one or more categorical variables Can be used to measure the magnitude of relationship between variables Works well with mostly continuous variables Estimated relationship depends on scale Can be used with multiple independent variables ΝΑ (no dependent variable) Correlation ΝΑ (no dependent variable) ANOVA N (should be continuous dependent variable) Simple Linear Regression N (logistic regression) Multiple Regression N (logistic regression)
Transcribed Image Text:1) Estimating Relationships - There are many statistical tools used to estimate relationships between variables. These include covariance, correlation, ANOVA, simple linear regression, and multiple regression. In the table below, enter a Yes (or Y) or No (or N) in each box to indicate the best response. If a box does not apply, enter NA. Covariance Example: Works well with a categorical dependent variable. Example: Can be used to estimation direction of relation between variables Works well with one or more categorical variables Can be used to measure the magnitude of relationship between variables Works well with mostly continuous variables Estimated relationship depends on scale Can be used with multiple independent variables ΝΑ (no dependent variable) Correlation ΝΑ (no dependent variable) ANOVA N (should be continuous dependent variable) Simple Linear Regression N (logistic regression) Multiple Regression N (logistic regression)
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