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Basic Analysis and Graphing - SAS

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224 Performing Simple Logistic Regression Chapter 7<br />

Logistic Platform Options<br />

Table 7.2 Description of the Parameter Estimates Report (Continued)<br />

Chi-Square<br />

Prob>ChiSq<br />

Lists the Wald tests for the hypotheses that each of the parameters is zero. The Wald<br />

Chi-square is computed as (Estimate/Std Error) 2 .<br />

Lists the observed significance probabilities for the Chi-square tests.<br />

Covariance of Estimates<br />

Reports the estimated variances of the parameter estimates, <strong>and</strong> the estimated covariances between the<br />

parameter estimates. The square root of the variance estimates is the same as those given in the Std Error<br />

section.<br />

Logistic Platform Options<br />

The following table describes the options within the Logistic Fit red triangle menu.<br />

Odds Ratios<br />

Inverse Prediction<br />

Logistic Plot<br />

Plot Options<br />

Adds odds ratios to the Parameter Estimates report. For more details,<br />

see the Modeling <strong>and</strong> Multivariate Methods book.<br />

This option is available only for a response with two levels.<br />

Prediction of x values from given y values. For more information, see<br />

“Inverse Prediction” on page 226.<br />

This option is available only for a response with two levels.<br />

Shows or hides the logistic plot.<br />

The Plot Options menu includes the following options:<br />

Show Points<br />

Toggles the points on or off.<br />

Show Rate Curve Is useful only if you have several points for<br />

each x-value. In these cases, you get reasonable estimates of the<br />

rate at each value, <strong>and</strong> compare this rate with the fitted logistic<br />

curve. To prevent too many degenerate points, usually at zero or<br />

one, JMP only shows the rate value if there are at least three<br />

points at the x-value.<br />

Line Color<br />

Enables you to pick the color of the plot curves.<br />

ROC Curve<br />

A Receiver Operating Characteristic curve is a plot of sensitivity by<br />

(1 – specificity) for each value of x. See “ROC Curves” on page 225.

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