Select the options for Fit Poisson Model

Stat > Regression > Poisson Regression > Fit Poisson Model > Options

Weights

In Weights, enter a numeric column of weights to perform weighted regression. The weights must be greater than or equal to zero. The weights column must have the same number of rows as the response column. For more information about determining the appropriate weight, go to Weighted regression.

Confidence level for all intervals

Enter the level of confidence for the confidence intervals for the coefficients and the fitted values.

Usually, a confidence level of 95% works well. A 95% confidence level indicates that, if you took 100 random samples from the population, the confidence intervals for approximately 95 of the samples would contain the mean response. For a given set of data, a lower confidence level produces a narrower interval, and a higher confidence level produces a wider interval.

Note

To display the confidence intervals, you must go to the Results sub-dialog box, and from Display of results, select Expanded tables.

Type of confidence interval

You can select a two-sided interval or a one-sided bound. For the same confidence level, a bound is closer to the point estimate than the interval. The upper bound does not provide a likely lower value. The lower bound does not provide a likely upper value.

For example, the mean number of patients who come to a clinic in a given hour is 4.58. The 95% confidence interval for the mean number of events for multiple future observations is 2.7 to 6.5. The 95% upper bound for the mean is 6.2, which is more precise because the bound is closer to the predicted mean.

  • Two-sided: Use a two-sided confidence interval to estimate both likely lower and upper values for the mean number of events.
  • Lower bound: Use a lower confidence bound to estimate a likely lower value for the mean number of events.
  • Upper bound: Use an upper confidence bound to estimate a likely higher value for the mean number of events.

Residuals for diagnostics

The deviance and Pearson residuals help identify patterns in the residual plots and outliers. Observations that are poorly fit by the model have high deviance and Pearson residuals. Minitab calculates the residual values for each distinct factor/covariate pattern.
  • Deviance: Deviance residuals are a measure of how well the model predicts the observation. Deviance residuals are often preferred for a logistic regression that uses the logit link function because the distribution of the residuals is more like the distribution of residuals from least squares models. The logit link function is the most common link function.
  • Pearson: Pearson residuals are also a measure of how well the model predicts the observation. A common approach for identifying outliers is to plot the Pearson residuals by the order of the observations in the worksheet.

Deviances for Tests

Select a deviance for calculating the chi-square values and the p-values. It is most common to use the adjusted deviance. Use the sequential deviance to determine the significance of terms by the order that they enter the model.
  • Adjusted (Type III): Measures the reduction in the deviance for each term relative to a model that contains all the remaining terms.
  • Sequential (Type I): Measures the reduction in the deviance when a term is added to a model that contains only the terms before it.
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