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It gives a measure of the extent to which values of the dependent variable move with values of the independent variables. This will enable you to decide whether or not the model has any useful predictive properties (significance). It also gives a measure of the expected changes in the value of the dependent variable which would accompany changes in the independent variable.
A regression model cannot offer an explanation. The fact that two variables move together does not mean that changes in one cause changes in the other. Furthermore it is possible to have very closely related variables which, because of a wrongly specified model, can show no correlation. For example, a LINEAR model fitted to y=x2 over a symmetric range for x will show zero correlation!
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What is the difference between the logistic regression and regular regression?
in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.
What is Mincer- zarnowitz regression?
A mincer Zarrowitz is a regression of the actual variable (dependent variable, y) against its fitted counterpart. At times, it may be used to assess the forecast accuracy of a model.
In the regression model the predictor variable is useful in predicting the response variable provided that B1 equals 0?
Not useful
Can independent variables be changed?
Yes. In fact, in multiple regression, that is often part of the analysis. You can add or remove independent variables to the model so as to get the best fit between what values are observed for the dependent variable and what the model predicts for the given set of independent variables.
What is multiple and partial correlation?
multiple correlation: Suppose you calculate the linear regression of a single dependent variable on more than one independent variable and that you include a mean in the linear model. The multiple correlation is analogous to the statistic that is obtainable from a linear model that includes just one independent variable. It measures the degree to which the linear model given by the linear regression is valuable as a predictor of the independent variable. For calculation details you might wish to see the wikipedia article for this statistic. partial correlation: Let's say you have a dependent variable Y and a collection of independent variables X1, X2, X3. You might for some reason be interested in the partial correlation of Y and X3. Then you would calculate the linear regression of Y on just X1 and X2. Knowing the coefficients of this linear model you would calculate the so-called residuals which would be the parts of Y unaccounted for by the model or, in other words, the differences between the Y's and the values given by b1X1 + b2X2 where b1 and b2 are the model coefficients from the regression. Now you would calculate the correlation between these residuals and the X3 values to obtain the partial correlation of X3 with Y given X1 and X2. Intuitively, we use the first regression and residual calculation to account for the explanatory power of X1 and X2. Having done that we calculate the correlation coefficient to learn whether any more explanatory power is left for X3 to 'mop up'.
