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Please rearrange the following steps of Multiple Linear Regression so that they are in order from the first step to the last step. (You may repeat this question if you don't get it all correct the first time.) Drag and drop options into correct order and submit. For keyboard navigation... SHOW MORE ✓ = Create scatterplots of each x-variable with the y-variable to assess the assumption linearity and confirm by checking correlations. III III = Review the Variance Inflation Factor values and use a backward-elimination process to remove the variable with the largest VIF above 10 and refit the model with the remaining x-variables and reassess the new VIF values. Use the backward-elimination procedure to remove any unneeded x-variables from the model to obtain the reduced model. III Perform a 6-step ANOVA test to determine if at least one x-variable in the full model is useful for predicting the y-variable. Check the residuals for the final model to determine if they are normally distributed, have random scatter, and have constant variance.

Please rearrange the following steps of Multiple Linear Regression so that they are in order from the first step to the last step. (You may repeat this question if you don't get it all correct the first time.) Drag and drop options into correct order and submit. For keyboard navigation... SHOW MORE ✓ = Create scatterplots of each x-variable with the y-variable to assess the assumption linearity and confirm by checking correlations. III III = Review the Variance Inflation Factor values and use a backward-elimination process to remove the variable with the largest VIF above 10 and refit the model with the remaining x-variables and reassess the new VIF values. Use the backward-elimination procedure to remove any unneeded x-variables from the model to obtain the reduced model. III Perform a 6-step ANOVA test to determine if at least one x-variable in the full model is useful for predicting the y-variable. Check the residuals for the final model to determine if they are normally distributed, have random scatter, and have constant variance.

MATLAB: An Introduction with Applications
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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Please rearrange the following steps of Multiple Linear Regression so that they are in order from the first step to the last step. (You may repeat this question if you don't get it all correct the first time.) Drag and drop options into correct order and submit. For keyboard navigation... SHOW MORE ✓ = Create scatterplots of each x-variable with the y-variable to assess the assumption linearity and confirm by checking correlations. III III = Review the Variance Inflation Factor values and use a backward-elimination process to remove the variable with the largest VIF above 10 and refit the model with the remaining x-variables and reassess the new VIF values. Use the backward-elimination procedure to remove any unneeded x-variables from the model to obtain the reduced model. III Perform a 6-step ANOVA test to determine if at least one x-variable in the full model is useful for predicting the y-variable. Check the residuals for the final model to determine if they are normally distributed, have random scatter, and have constant variance.
Transcribed Image Text:Please rearrange the following steps of Multiple Linear Regression so that they are in order from the first step to the last step. (You may repeat this question if you don't get it all correct the first time.) Drag and drop options into correct order and submit. For keyboard navigation... SHOW MORE ✓ = Create scatterplots of each x-variable with the y-variable to assess the assumption linearity and confirm by checking correlations. III III = Review the Variance Inflation Factor values and use a backward-elimination process to remove the variable with the largest VIF above 10 and refit the model with the remaining x-variables and reassess the new VIF values. Use the backward-elimination procedure to remove any unneeded x-variables from the model to obtain the reduced model. III Perform a 6-step ANOVA test to determine if at least one x-variable in the full model is useful for predicting the y-variable. Check the residuals for the final model to determine if they are normally distributed, have random scatter, and have constant variance.
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