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. CovarianceCorrelationANOVASimple Linear RegressionMultiple RegressionExample: Works well with a categorical dependent variable.NA(no dependent variable)NA(no dependent variable)N(should be continuous dependent variable)N(logistic regression)N(logistic regression)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

The table as required is given in step:2Covariance Correlation ANOVA Simple Linear Multiple Linear Regression Regression NA NA N N N N Y N Y Y Y Y NA (only the dependent variable must be contimuous) Y Y Y N NA N N N Y Y (using factorial ANOVA) N Y 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.

Answered: There are many statistical tools used…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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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.

	Covariance	Correlation	ANOVA	Simple Linear Regression	Multiple Regression
Example: Works well with a categorical dependent variable.	NA (no dependent variable)	NA (no dependent variable)	N (should be continuous dependent variable)	N (logistic regression)	N (logistic regression)
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