Adjusted mean squares measure how much variation a term or a model explains, assuming that all other terms are in the model, regardless of the order they were entered. Unlike the adjusted sums of squares, the adjusted mean squares consider the degrees of freedom.
The adjusted mean square of the error (also called MSE or s^{2}) is the variance around the fitted values.
Minitab uses the adjusted mean squares to calculate the pvalue for a term. Minitab also uses the adjusted mean squares to calculate the adjusted R^{2} statistic. Usually, you interpret the pvalues and the adjusted R^{2} statistic instead of the adjusted mean squares.
Adjusted sums of squares are measures of variation for different components of the model. The order of the predictors in the model does not affect the calculation of the adjusted sum of squares. In the Analysis of Variance table, Minitab separates the sums of squares into different components that describe the variation due to different sources.
Minitab uses the adjusted sums of squares to calculate the pvalue for a term. Minitab also uses the sums of squares to calculate the R^{2} statistic. Usually, you interpret the pvalues and the R^{2} statistic instead of the sums of squares.
The total degrees of freedom (DF) are the amount of information in your data. The analysis uses that information to estimate the values of unknown population parameters. The total DF is determined by the number of observations in your sample. The DF for a term show how much information that term uses. Increasing your sample size provides more information about the population, which increases the total DF. Increasing the number of terms in your model uses more information, which decreases the DF available to estimate the variability of the parameter estimates.
If two conditions are met, then Minitab partitions the DF for error. The first condition is that there must be terms you can fit with the data that are not included in the current model. For example, if you have a continuous predictor with 3 or more distinct values, you can estimate a quadratic term for that predictor. If the model does not include the quadratic term, then a term that the data can fit is not included in the model and this condition is met.
The second condition is that the data contain replicates. Replicates are observations where each predictor has the same value. For example, if you have 3 observations where pressure is 5 and temperature is 25, then those 3 observations are replicates.
If the two conditions are met, then the two parts of the DF for error are lackoffit and pure error. The DF for lackoffit allow a test of whether the model form is adequate. The lackoffit test uses the degrees of freedom for lackoffit. The more DF for pure error, the greater the power of the lackoffit test.
Fitted values are also called fits or . The fitted values are point estimates of the mean response for given values of the factor levels.
Fitted values are calculated by entering the specific xvalues for each observation in the data set into the model equation.
Observations with fitted values that are very different from the observed value may be unusual or influential. If Minitab determines that your data include unusual values, your output includes the table of Fits and Diagnostics for Unusual Observations, which identifies the unusual observations. For more information on unusual values, go to Unusual observations.
Fitted means use the least squares estimation method to predict the mean response values of a balanced design for each group. Data means are the raw response variable means for each factor level combination.
Therefore, the two types of means are identical for balanced designs but can be different for unbalanced designs. Fitted means are useful for observing response differences that are caused by changes in factor levels rather than differences caused by the disproportionate influence of unbalanced experimental conditions.
The fitted means calculated from the sample are estimates of the population mean for each group.
Minitab uses the Fvalue to calculate the pvalue, which you use to make a decision about the statistical significance of the terms and model. The pvalue is a probability that measures the evidence against the null hypothesis. Lower probabilities provide stronger evidence against the null hypothesis.
A sufficiently large Fvalue indicates that the term or model is significant.
If you want to use the Fvalue to determine whether to reject the null hypothesis, compare the Fvalue to your critical value. You can calculate the critical value in Minitab or find the critical value from an Fdistribution table in most statistics books. For more information on using Minitab to calculate the critical value, go to Using the inverse cumulative distribution function (ICDF) and click "Use the ICDF to calculate critical values".
The histogram of the residuals shows the distribution of the residuals for all observations.
Pattern  What the pattern may indicate 

A long tail in one direction  Skewness 
A bar that is far away from the other bars  An outlier 
Because the appearance of a histogram depends on the number of intervals used to group the data, don't use a histogram to assess the normality of the residuals. Instead, use a normal probability plot.
A histogram is most effective when you have approximately 20 or more data points. If the sample is too small, then each bar on the histogram does not contain enough data points to reliably show skewness or outliers.
The normal plot of the residuals displays the residuals versus their expected values when the distribution is normal.
Use the normal probability plot of residuals to verify the assumption that the residuals are normally distributed. The normal probability plot of the residuals should approximately follow a straight line.
If you see a nonnormal pattern, use the other residual plots to check for other problems with the model, such as missing terms or a time order effect. If the residuals do not follow a normal distribution, prediction intervals can be inaccurate. If the residuals do not follow a normal distribution and the data have fewer than 15 observations, then confidence intervals for predictions, confidence intervals for coefficients, and pvalues for coefficients can be inaccurate.
The pvalue is a probability that measures the evidence against the null hypothesis. Lower probabilities provide stronger evidence against the null hypothesis.
The following shows how to interpret significant main effects and interaction effects.
The pvalue is a probability that measures the evidence against the null hypothesis. Lower probabilities provide stronger evidence against the null hypothesis.
If the pvalue is larger than the significance level, the test does not detect any lackoffit.
A residual (e_{i}) is the difference between an observed value (y) and the corresponding fitted value, (), which is the value predicted by the model.
Plot the residuals to determine whether your model is adequate and meets the assumptions of regression. Examining the residuals can provide useful information about how well the model fits the data. In general, the residuals should be randomly distributed with no obvious patterns and no unusual values. If Minitab determines that your data include unusual observations, it identifies those observations in the Fits and Diagnostics for Unusual Observations table in the output. The observations that Minitab labels as unusual do not follow the proposed regression equation well. However, it is expected that you will have some unusual observations. For example, based on the criteria for large residuals, you would expect roughly 5% of your observations to be flagged as having a large residual. For more information on unusual values, go to Unusual observations.
The residuals versus fits graph plots the residuals on the yaxis and the fitted values on the xaxis.
Use the residuals versus fits plot to verify the assumption that the residuals are randomly distributed and have constant variance. Ideally, the points should fall randomly on both sides of 0, with no recognizable patterns in the points.
Pattern  What the pattern may indicate 

Fanning or uneven spreading of residuals across fitted values  Nonconstant variance 
Curvilinear  A missing higherorder term 
A point that is far away from zero  An outlier 
A point that is far away from the other points in the xdirection  An influential point 
If you identify any patterns or outliers in your residual versus fits plot, consider the following solutions:
Issue  Possible solution 

Nonconstant variance  Consider using Options. Under BoxCox transformation, select Optimal λ.  in Minitab Statistical Software. Click
An outlier or influential point 

The residual versus order plot displays the residuals in the order that the data were collected.
The residuals versus variables plot displays the residuals versus another variable. The variable could already be included in your model. Or, the variable may not be in the model, but you suspect it affects the response.
If you see a nonrandom pattern in the residuals, it indicates that the variable affects the response in a systematic way. Use
in Minitab Statistical Software to refit the model with a term for this variable.R^{2} is the percentage of variation in the response that is explained by the model. It is calculated as 1 minus the ratio of the error sum of squares (which is the variation that is not explained by model) to the total sum of squares (which is the total variation in the model).
Use R^{2} to determine how well the model fits your data. The higher the R^{2} value, the better the model fits your data. R^{2} is always between 0% and 100%.
R^{2} always increases when you add additional predictors to a model. For example, the best fivepredictor model will always have an R^{2} that is at least as high the best fourpredictor model. Therefore, R^{2} is most useful when you compare models of the same size.
Small samples do not provide a precise estimate of the strength of the relationship between the response and predictors. If you need R^{2} to be more precise, you should use a larger sample (typically, 40 or more).
R^{2} is just one measure of how well the model fits the data. Even when a model has a high R^{2}, you should check the residual plots to verify that the model meets the model assumptions.
Adjusted R^{2} is the percentage of the variation in the response that is explained by the model, adjusted for the number of predictors in the model relative to the number of observations. Adjusted R^{2} is calculated as 1 minus the ratio of the mean square error (MSE) to the mean square total (MS Total).
Use adjusted R^{2} when you want to compare models that have different numbers of predictors. R^{2} always increases when you add a predictor to the model, even when there is no real improvement to the model. The adjusted R^{2} value incorporates the number of predictors in the model to help you choose the correct model.
Step  % Potato  Cooling rate  Cooking temp  R^{2}  Adjusted R^{2}  Pvalue 

1  X  52%  51%  0.000  
2  X  X  63%  62%  0.000  
3  X  X  X  65%  62%  0.000 
The first step yields a statistically significant regression model. The second step adds cooling rate to the model. Adjusted R^{2} increases, which indicates that cooling rate improves the model. The third step, which adds cooking temperature to the model, increases the R^{2} but not the adjusted R^{2}. These results indicate that cooking temperature does not improve the model. Based on these results, you consider removing cooking temperature from the model.
Predicted R^{2} is calculated with a formula that is equivalent to systematically removing each observation from the data set, estimating the regression equation, and determining how well the model predicts the removed observation. The value of predicted R^{2} ranges between 0% and 100%.
Use predicted R^{2} to determine how well your model predicts the response for new observations. Models that have larger predicted R^{2} values have better predictive ability.
A predicted R^{2} that is substantially less than R^{2} may indicate that the model is overfit. An overfit model occurs when you add terms for effects that are not important in the population, although they may appear important in the sample data. The model becomes tailored to the sample data and therefore, may not be useful for making predictions about the population.
Predicted R^{2} can also be more useful than adjusted R^{2} for comparing models because it is calculated with observations that are not included in the model calculation.
For example, an analyst at a financial consulting company develops a model to predict future market conditions. The model looks promising because it has an R^{2} of 87%. However, the predicted R^{2} is only to 52%, which indicates that the model may be overfit.
S represents the standard deviation of how far the data values fall from the fitted values. S is measured in the units of the response.
Use S to assess how well the model describes the response. S is measured in the units of the response variable and represents the standard deviation of how far the data values fall from the fitted values. The lower the value of S, the better the model describes the response. However, a low S value by itself does not indicate that the model meets the model assumptions. You should check the residual plots to verify the assumptions.
For example, you work for a potato chip company that examines the factors that affect the percentage of crumbled potato chips per container. You reduce the model to the significant predictors, and S is calculated as 1.79. This result indicates that the standard deviation of the data points around the fitted values is 1.79. If you are comparing models, values that are lower than 1.79 indicate a better fit, and higher values indicate a worse fit.
The standard error of the mean (SE Mean) estimates the variability between sample means that you would obtain if you took samples from the same population again and again. Whereas the standard error of the mean estimates the variability between samples, the standard deviation measures the variability within a single sample.
For example, you have a mean delivery time of 3.80 days, with a standard deviation of 1.43 days, from a random sample of 312 delivery times. These numbers yield a standard error of the mean of 0.08 days (1.43 divided by the square root of 312). If you took multiple random samples of the same size, from the same population, the standard deviation of those different sample means would be around 0.08 days.
Use the standard error of the mean to determine how precisely the sample mean estimates the population mean.
A smaller value of the standard error of the mean indicates a more precise estimate of the population mean. Usually, a larger standard deviation results in a larger standard error of the mean and a less precise estimate of the population mean. A larger sample size results in a smaller standard error of the mean and a more precise estimate of the population mean.
Minitab uses the standard error of the mean to calculate the confidence interval, which is a range of values likely to include the population mean.
The standardized residual equals the value of a residual (e_{i}) divided by an estimate of its standard deviation.
Use the standardized residuals to help you detect outliers. Standardized residuals greater than 2 and less than −2 are usually considered large. The Fits and Diagnostics for Unusual Observations table identifies these observations with an 'R'. The observations that Minitab labels do not follow the proposed regression equation well. However, it is expected that you will have some unusual observations. For example, based on the criteria for large standardized residuals, you would expect roughly 5% of your observations to be flagged as having a large standardized residual. For more information, go to Unusual observations.
Standardized residuals are useful because raw residuals might not be good indicators of outliers. The variance of each raw residual can differ by the xvalues associated with it. This unequal variation causes it to be difficult to assess the magnitudes of the raw residuals. Standardizing the residuals solves this problem by converting the different variances to a common scale.
The terms provide the factor levels and factor level combinations for the fitted means in the means table.
For terms that represent main effects, the table displays the groups within each factor and their fitted means. For terms that represent interaction effects, the table displays all possible combinations of groups across both factors. Use the pvalues in the ANOVA table to determine whether these effects are statistically significant.