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R-squared measures the proportion of the variation in your dependent variable Y explained by your independent variables X for a linear regression model. Adjusted R-squared adjusts the statistic based on the number of independent variables in the model. R2 shows how well terms data points fit a curve or line. Adjusted R2 also indicates how well terms fit a curve or line, but adjusts for the number of terms in a model. If you add more and more useless variables to a model, adjusted r-squared will decrease. If you add more useful variables, adjusted r-squared will increase. Adjusted R2 will always be less than or equal to R 2. You only need R2 when working with samples. In other words, R2 isnt necessary when you have data from an entire population.

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R-squared measures the proportion of the variation in your dependent variable Y explained by your independent variables X for a linear regression model. Adjusted R-squared adjusts the statistic based on the number of independent variables in the model. R2 shows how well terms data points fit a curve or line. Adjusted R2 also indicates how well terms fit a curve or line, but adjusts for the number of terms in a model. If you add more and more useless variables to a model, adjusted r-squared will decrease. If you add more useful variables, adjusted r-squared will increase. Adjusted R2 will always be less than or equal to R 2. You only need R2 when working with samples. In other words, R2 isnt necessary when you have data from an entire population.

span >Which of the following is not correct regarding adjusted R2?

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