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Assuming that the data satisfy the requirements of errors that are independent and identically distributed, the regression model that is often fitted takes the form of
fitted(y) = b0 + b1*x
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If there is a linear relationship between the independent variable,x , and the dependent variable, y, then b1 must be non-zero. You would therefore test the null hypothesis that b1 is equal to 0 against the alternative hypothesis, which may be that
* b1 is not 0, or
* b1 is greater than 0, or
* b1 is less than 0.
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What can you conclude if the global test of regression does not reject the null hypothesis?
You can conclude that there is not enough evidence to reject the null hypothesis. Or that your model was incorrectly specified. Consider the exact equation y = x2. A regression of y against x (for -a < x < a) will give a regression coefficient of 0. Not because there is no relationship between y and x but because the relationship is not linear: the model is wrong! Do a regression of y against x2 and you will get a perfect regression!
What is difference between regression testing and retesting?
A regression test is a test where a previously known bug is tested for after a change. A retest is simply repeating a test.
Example of regression test case?
This question needs to be more specific. Are you asking for an example of statistical regression?
What is the geometric basis for the math behind correlation and regression?
Z Test
What does f statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
