Leverage and Influence – Basic Idea Basic idea: ![]() Testing Parameters and CI’s for Parameters:Ĭonfidence Intervals for the Mean of Y and Prediction of a New YĢ) Analyze > Fit Model approach – more information and allows for multiple regressionīasic output from Fit Model approach is shown below.Ĭonfidence Intervals and Prediction Intervalsįor adding a new patient who is 65 years of age gives… 8124 or 81.24% of the variation in the patient satisfaction responses can be explained by the simple linear regression on patients age. To obtain the plots of the residuals shown to the left, select Plot Residuals from the Linear Fit pull-down menu. The output from fitting the simple linear regression model to these data is shown below: Comments: We first examine the simple linear regression model for satisfaction using patient age as the sole predictor, i.e. We can see both age and severity of illness are negatively correlated with the response satisfaction, and are positively correlated with each other as would be expected. I have added the following options to the display: Show Correlations and Horizontal from the Show Histograms option. The scatterplot matrix for these variables is shown on the next page. The diagonals are all 1.000 because the correlation of any variable with itself is 1. It gives all pairwise correlations between these variables. ![]() The output below gives a correlation matrix for Age, Severity, and Satisfaction. To do this in JMP select Analyze > Multivariate Methods > Multivariate as shown below. 79 – 83) HospSatisfaction (Table 3.2).JMP Before fitting any regression model it is good idea to plot the response variable and the potential predictors in a scatterplot matrix. – Regression Analysis – Computing in JMP Example 1: Patient Satisfaction Survey (pgs.
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