After using the Pearson correlation is used to test hypothesis of the relationship between an independent and a dependent variable of which both variables are continues, Can I still go ahead to use the linear regression to test same hypothesis ?
Hello, if the data distribution is normal and the two variables are interval with relative, you can use Pearson's correlation coefficient. The variable is "explanatory". Regression is often used to discover the model of linear relationship between variables. In this case, it is assumed that one or more descriptive variables whose value is independent of the other variables or under the control of the researcher, can be effective in predicting the response variable whose value is not dependent on the descriptive variables and under the control of the researcher. The purpose of regression analysis is to identify the linear model of this relationship.
Hello Hardi,
If you have continuous scores for one IV and continuous scores for one DV from a set of cases, either the Pearson correlation or ordinary linear regression may be used to test the hypothesis that the linear relationship between the paired scores is zero. The standardized regression coefficient from the regression will be numerically equal to the Pearson correlation coefficient. As well, the computed probability associated with each test will be identical.
The linear regression, however, also yields estimates of the slope and intercept of the best-fitting linear relationship between the IV and DV (as noted in Omulbanin's reply). This may be useful.
Good luck with your work.
Yes, you can use Pearson correlation and linear regression to establish the relationship between an independent variable and a dependent variable at the same time.
Pearson correlation and linear regression are both useful statistical methods for examining the relationship between an independent variable and a dependent variable, and they can be used together to provide a more complete understanding of the relationship between the variables.
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