2) A total predicted logit of 0 can be transformed to a probability of? 0 5 1 75 (3) Which of the following criteria is the most optimal for assessing the goodness of the fit of a multiple linear regression model? Adjusted R2 R2 The intercept The coefficient (4) When there are two predictor variables in a multiple regression model, what does the following expression (H0: β1 = β2 = 0) mean? One of the independent variables is useful in predicting the dependent variable Both of the independent variables are useful in predicting the dependent variable None of the independent variables is useful in predicting the dependent variable There is a third independent variable predicting the dependent variable
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.
Please answer the following three multiple choice question:
(2) A total predicted logit of 0 can be transformed to a
- 0
- 5
- 1
- 75
(3) Which of the following criteria is the most optimal for assessing the goodness of the fit of a multiple linear regression model?
- Adjusted R2
- R2
- The intercept
- The coefficient
(4) When there are two predictor variables in a multiple regression model, what does the following expression (H0: β1 = β2 = 0) mean?
- One of the independent variables is useful in predicting the dependent variable
- Both of the independent variables are useful in predicting the dependent variable
- None of the independent variables is useful in predicting the dependent variable
- There is a third independent variable predicting the dependent variable
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