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Multiple Regression Analysis in Minitab - The Center for ...

Multiple Regression Analysis in Minitab 1. Suppose we are interested in how the exercise and body mass index affect the blood pressure. A. random sample of 10 males 50 years of age is selected and their height, weight, number of hours of exercise and the blood pressure are measured. Body mass index is calculated by the following ( ). formula: ( ) . Select Stat- Regression - Regression from the pull-down menu. Placing the variable we would like to predict, blood pressure, in the Response: and the variable we will use for prediction, exercise and body mass index in the Predictors: box. Click OK. This generates the following Minitab output. The Regression equation is BloodPressure = - Exercise + BMI. Predictor Coef SE Coef T P. Constant Exercise BMI S = R-Sq = R-Sq(adj) = Analysis of Variance Source DF SS MS F P. Regression 2 Residual Error 7 Total 9 The interpretation of R2 is same as before.

Multiple Regression Analysis in Minitab 2 The next part of the output is the statistical analysis (ANOVA-analysis of variance) for the regression model. The ANOVA represents a hypothesis test with where the null hypothesis is H o:E i 0 for all i (In simple regression, i = 1) H A:E i z 0 for at least 1 coefficient

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Logistic Regression on SPSS - The Center for Applied ...

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The dataset is available at U:\_MT Student File Area\hjkim\STAT380\SPSS tutorial\hypertension.sav. Note that the hypertension variable binary variable. 0 means no hypertension and 1 means hypertension. Predictor variables are age, gender and body mass index. Age and bmi is quantitative and gender is categorical variable.

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Introduction to Minitab 1 We begin by first starting Minitab. You may choose to either ... then from All Programs, then Minitab 16 Statistical Software 3. A new spreadsheet should appear on the screen. Below is a sample data set that we will be using for today’s exercise. It lists the heights & weights for 10 men and 12 women.

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