Select the analysis options for One-Way ANOVA

Stat > ANOVA > One-Way > Options

Assume equal variances

Select Assume equal variances to assume that all populations have equal variances. If you do not assume equal variances, Minitab performs Welch's ANOVA test. One-way ANOVA with equal variances is slightly more powerful than Welch's ANOVA, but serious error can result if the variances are not equal. Use the Test for Equal Variances to determine whether or not the populations have equal variances.

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 predicted mean concentration of dissolved solids in water is 13.2 mg/L. The 95% confidence interval for the mean of multiple future observations is 12.8 mg/L to 13.6 mg/L. The 95% upper bound for the mean of multiple future observations is 13.5 mg/L, 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 response.
Lower bound
  • Use a lower confidence bound to estimate a likely lower value for the mean response.
Upper bound
  • Use an upper confidence bound to estimate a likely higher value for the mean response.
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