![anova minitab anova minitab](http://www.wikihow.com/images/2/24/Perform-a-Normality-Test-on-Minitab-Step-12.jpg)
The resulting p-value is smaller than common significance levels. While the coefficient for South is closer to 0 than the coefficient for North, the standard error of the coefficient for South is also smaller.
![anova minitab anova minitab](https://i.ytimg.com/vi/DseGljsZJtE/hqdefault.jpg)
The resulting p-value is greater than common levels of the significance level, so you cannot conclude that the coefficient for North differs from 0. The standard error of the North coefficient is nearly as large as the value of the coefficient itself. Therefore, the model is able to estimate the coefficient for South with greater precision. The standard error of the coefficient for South is smaller than the standard error of the coefficient for North. The coefficients for North and South are similar in magnitude. In this model, North and South measure the position of a focal point in inches. Subtract a specified value, then divide by another The effect and interpretation of this method depends on the values that you enter. Each coefficient represents the expected change in the response given a change of one standard deviation in the variable. Divide by the standard deviation This method scales the variables. When you subtract the mean, the constant coefficient is estimating the mean response when all the predictors are at their mean values. Each coefficient represents the expected change in the response given a one unit change in the variable, using the original measurement scale. Subtract the mean This method centers the variables.
![anova minitab anova minitab](https://support.minitab.com/es-mx/minitab/20/media/generated-content/images/gage_expanded_refraction.png)
Subtract the mean, then divide by the standard deviation This method both centers and scales the variables. The coefficients represent the mean change in the response associated with the high and low values that you specified.
![anova minitab anova minitab](https://statistics.laerd.com/minitab-tutorials/img/owa/anova-transferred.png)
Minitab uses this method in design of experiments (DOE).
Anova minitab code#
The coding method that Minitab uses affects both the estimation and the interpretation of the coded coefficients as follows: Specify low and high levels to code as -1 and +1 This method both centers and scales the variables. Using the (0, 1) coding scheme, the coefficient for the categorical variable of mentoring indicates that employees with mentors have scores that are an average of 10.1 points greater than employees without mentors. The coefficient for the continuous variable of training hours, is 4.3, which indicates that, for every hour of training, the mean test score increases by 4.3 points. The variable x 2 is a categorical variable that equals 1 if the employee has a mentor and 0 if the employee does not have a mentor. In the equation, x 1 is the hours of in-house training (from 0 to 20). With the (−1, 0,+1) coding scheme, each coefficient represents the difference between each level mean and the overall mean.įor example, a manager determines that an employee's score on a job skills test can be predicted using the regression model, y = 130 + 4.3x 1 + 10.1x 2.The coefficient for the reference level is not displayed in the Coefficients table. With the (0, 1) coding scheme, each coefficient represents the difference between each level mean and the reference level mean.The coding scheme can be changed in the Coding sub-dialog box. The interpretation of the coefficient for a categorical variable depends on the coding scheme that you choose for categorical variables. The coefficient for one level of the categorical variable must be set to zero so that the model can be fit. A coefficient is listed for each level of the categorical variable except for one (unless you choose to show coefficients for all levels in the Results sub-dialog box).