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SPSS can be used to conduct both of these tests. P-value > α: Cannot conclude the data do not follow the specified distribution (Fail to reject H 0) If the p-value is larger than the significance level, the decision is to fail to reject the null hypothesis because you do not have enough evidence to conclude that your data do not follow the specified distribution. Interpret Regression Output Multiple linear regressions and multiple logistic regressions are similar in that they allow you to adjust for potential confounders when testing the relationship between your dependent and independent variables. The most common models are simple linear and multiple linear. In this case, the PLS predictions can be interpreted as contrasts between broad bands of frequencies. Key output includes the p-value, R 2, and residual plots. Regression analysis includes several variations, such as linear, multiple linear, and nonlinear. Instead, PLS prediction is a function of all of the input factors.
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If the errors are not normal, then the results presented in this chapter will hold approximately if the sample is large. In this chapter we adopt assumptions MR1-MR6, including normality, listed on page 150. Complete the following steps to interpret a regression analysis. these topics we discuss model specification for the multiple regression model and the construction of prediction intervals. P-value ≤ α: The data do not follow the specified distribution (Reject H 0) If the p-value is less than or equal to the significance level, the decision is to reject the null hypothesis and conclude that your data do not follow the specified distribution. Interpret the key results for Multiple Regression. A significance level of 0.05 indicates that the risk of concluding the data do not follow the specified distribution-when, actually, the data do follow the specified distribution-is 5%. Usually, a significance level (denoted as α or alpha) of 0.05 works well. To determine whether the data do not follow the specified distribution, compare the p-value to the significance level.