You run a multiple regression, and the p-value turns out be very small, about 0.001. Which of the following can you definitely conclude? a. The R-square value is not guaranteed to be large; it could be a value like 0.34. b. The p-values for the regression coefficients of all X variables will indicate significance at the 1% level. c. The p-value for the regression coefficient of at least one X variable will indicate significance at the 1% level. d. All of these choices are true.
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.
You run a multiple regression, and the p-value turns out be very small, about 0.001. Which of the following can you definitely conclude?
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