If in a regression of some actual data against the predictions from two models, if the coefficient for the intercept is greater than 2 The combined model would create a bias The combined model would not create a bias The combined model would be a better model The combined model would always have a forecast that is too high The number 2 has no relevance in this context
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.
If in a regression of some actual data against the predictions from two models, if the coefficient for the intercept is greater than 2
The combined model would create a bias |
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The combined model would not create a bias |
||
The combined model would be a better model |
||
The combined model would always have a forecast that is too high |
||
The number 2 has no relevance in this context |
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