Learn to test for multicollinearity in R with data from the English Health Survey (teaching dataset) (2002) /
This dataset is designed for learning to test for multicollinearity in statistical analysis, specifically multiple linear regression analysis. The dataset is a subset of data derived from the 2002 English Health Survey (Teaching Dataset). Prior to running any multiple linear regression model, we nee...
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| Format: | eBook |
| Language: | English |
| Published: |
London :
SAGE Publications, Ltd.,
2019.
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| Online Access: | Connect to the full text of this electronic book |
| Summary: | This dataset is designed for learning to test for multicollinearity in statistical analysis, specifically multiple linear regression analysis. The dataset is a subset of data derived from the 2002 English Health Survey (Teaching Dataset). Prior to running any multiple linear regression model, we need to test our data for a number of assumptions, one of which is that there is no multicollinearity among the predictor (or independent) variables. Collinearity is a correlation between two predictor (or independent) variables in a regression model whereby they express a linear relationship. Multicollinearity is where more than two predictor (or independent) variables are associated. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for R. |
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| Physical Description: | 1 online resource : illustrations |
| ISBN: | 9781526498670 1526498677 |