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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
(This browser is so useless that it does not properly support characters from other althabets and not even subscripts!)
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.
What is the difference betwene simple linear regression and multiple regression?
Simple linear regression is performed between one independent variable and one dependent variable. Multiple regression is performed between more than one independent variable and one dependent variable. Multiple regression returns results for the combined influence of all IVs on the DV as well as the individual influence of each IV while controlling for the other IVs. It is therefore a far more accurate test than running separate simple regressions for each IV. Multiple regression should not be confused with multivariate regression, which is a much more complex procedure involving more than one DV.
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
In software testing regression testing must consist of fixed set of test to create a base line is it true or false?
Regression are classified as - Full / Complete Regression -- Entire application is regressed - Regional regression -- Tests performed around defect fixes or code changes
What is the difference between multivariate regression and multiple regression?
Although not everyone follows this naming convention, multiple regression typically refers to regression models with a single dependent variable and two or more predictor variables. In multivariate regression, by contrast, there are multiple dependent variables, and any number of predictors. Using this naming convention, some people further distinguish "multivariate multiple regression," a term which makes explicit that there are two or more dependent variables as well as two or more independent variables.In short, multiple regression is by far the more familiar form, although logically and computationally the two forms are extremely similar.Multivariate regression is most useful for more special problems such as compound tests of coefficients. For example, you might want to know if SAT scores have the same predictive power for a student's grades in the second semester of college as they do in the first. One option would be to run two separate simple regressions and eyeball the results to see if the coefficients look similar. But if you want a formal probability test of whether the relationship differs, you could run it instead as a multivariate regression analysis. The coefficient estimates will be the same, but you will be able to directly test for their equality or other properties of interest.In practical terms, the way you produce a multivariate analysis using statistical software is always at least a little different from multiple regression. In some packages you can use the same commands for both but with different options; but in a number of packages you use completely different commands to obtain a multivariate analysis.A final note is that the term "multivariate regression" is sometimes confused with nonlinear regression; in other words, the regression flavors besides Ordinary Least Squares (OLS) linear regression. Those forms are more accurately called nonlinear or generalized linear models because there is nothing distinctively "multivariate" about them in the sense described above. Some of them have commonly used multivariate forms, too, but these are often called "multinomial" regressions in the case of models for categorical dependent variables.
How do you do regression testing and can we give one example on that in the same application?
regression testing is normally a set of automated scripts written by the test/automation team to test all previously existing functionality to make sure nothing has been broken.
When shall you do your pregnancy test?
You shall do your pregnancy test after you have sex and get some synthoms that you're pregnant
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.
Prerequisites of Regression testing and system testing?
system testing is a kind of retesting where we can test whole system after integration. while regression testing is a process where we do the rerunning the test cases and check whether that re run doesnot affects the real environment.
What does a large 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.
