Computing the test statistic and p-value in minitab express

#Computing the test statistic and p value in minitab express how to#
#Computing the test statistic and p value in minitab express software#

(Use a 10% significance level.) Group Stress No stress Sample mean 26 Sample Standard Dev. Test the claim that stress effects weight. The table below summarizes the data on weight gain. After 21 days, the change in weight (in grams) was determined for each rat. In a study of the effects of stress on weight in rats, 71 rats were randomly assigned to either a stressful environment or a control (nonstressful) environment. (Do not just say 'Reject Ho' or 'Do not reject Ho.') If the question requires a confidence interval, be sure to make a statement about the confidence interval in context of the problem.ДЈ. Step 4: State your conclusion in the context of the problem. (Use 'mu' or the symbol u.) Step 2: Calculate the test statistic. Step 1: State the null and alternative hypothesis. In addition, you should show that you have checked that the conditions for inference are met. If the question requires a test of significance, your solution should clearly show the four steps to the test.

#Computing the test statistic and p value in minitab express software#

Keep in mind that the results obtained from software are not meaningful unless the correct null and alternative hypotheses are determined. If you input the hypotheses and the summary statistics into Minitab Express, Minitab Express will output the test statistic and p-value and you will not have to do any complex calculations. To see how the F-test works using concepts and graphs, see my post about understanding the F-test.Transcribed image text: Instructions The key steps to tests of significance are to state Ho and Hp, calculate the test statistic, compute the p- value, and make a conclusion based on the given significance level. If the P value for the overall F-test is less than your significance level, you can conclude that the R-squared value is significantly different from zero. The overall F-test determines whether this relationship is statistically significant. While R-squared provides an estimate of the strength of the relationship between your model and the response variable, it does not provide a formal hypothesis test for this relationship. Therefore, if the P value of the overall F-test is significant, your regression model predicts the response variable better than the mean of the response.

In the intercept-only model, all of the fitted values equal the mean of the response variable. There are a couple of additional conclusions you can draw from a significant overall F-test. For example, a significant overall F-test could determine that the coefficients are jointly not all equal to zero while the tests for individual coefficients could determine that all of them are individually equal to zero. However, in a few cases, the tests could yield different results. Typically, if you don't have any significant P values for the individual coefficients in your model, the overall F-test won't be significant either. Great! That set of terms you included in your model improved the fit! If the P value for the F-test of overall significance test is less than your significance level, you can reject the null-hypothesis and conclude that your model provides a better fit than the intercept-only model. In Minitab statistical software, you'll find the F-test for overall significance in the Analysis of Variance table.

Alternative hypothesis: The fit of the intercept-only model is significantly reduced compared to your model.

Null hypothesis: The fit of the intercept-only model and your model are equal.

The hypotheses for the F-test of the overall significance are as follows: A regression model that contains no predictors is also known as an intercept-only model. It compares a model with no predictors to the model that you specify. The F-test of the overall significance is a specific form of the F-test. Unlike t-tests that can assess only one regression coefficient at a time, the F-test can assess multiple coefficients simultaneously. In general, an F-test in regression compares the fits of different linear models. Recently I've been asked, how does the F-test of the overall significance and its P value fit in with these other statistics? ThatвЂ™s the topic of this post!

#Computing the test statistic and p value in minitab express how to#

IвЂ™ve also written about how to interpret R-squared to assess the strength of the relationship between your model and the response variable. Previously, IвЂ™ve written about how to interpret regression coefficients and their individual P values.

Computing the test statistic and p-value in minitab express