A random sample consisting of 200 business executives working in the Downtown area is drawn and they were asked to answer a set of questions. One question in the survey asked about their annual salary and another about their annual expenses on luxury goods. We regress the spending on salary, and the regression has R2 = 0.884. Interpret the result of the R2 value in this context. “Despite the high value of R2, a linear regression model may not be appropriate.” Comment.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
. A random sample consisting of 200 business executives working in the Downtown area is drawn and they were asked to answer a set of questions. One question in the survey asked about their annual salary and another about their annual expenses on luxury goods. We regress the spending on salary, and the regression has R2 = 0.884.
- Interpret the result of the R2 value in this context.
- “Despite the high value of R2, a linear regression model may not be appropriate.” Comment.
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