Minitab serves best for research as well as for teaching purposes.Minitab is easy to learn when compared with other statistical packages.It offers a quick graphical output which will help the users to have a quick look at their analysis.Easy to compute all the statistics problems.It has a flexible and affordable licensing.The instructions can be written in English in Minitab.Minitab serves as a reference for many textbooks and supplements.By learning Minitab the students are highly exposed to wide range of jobs in world class companies. Minitab is a statistical which is used by many of the large companies.Minitab is a complete package with a lot of statistical tools so there is no need for special purchase of any add-ons or modules.Minitab is more affordable as it offers discounts to students and academic institutions.The commands and menus in Minitab are structured in a way that matches with the textbooks and provides easy guidance to get the output.Minitab has a intuitive interface which makes it easy to use for students and instructors.Listed below are the five reasons why Minitab is so popular Minitab is more widely used by students and others for statistical analysis than other statistical. Minitab has four different types of ANOVAвЂ™s like One Way, Balanced and General Linear Model. Minitab provides a quick and effective solution for the level of analysis which is required in most of the Six Sigma projects. Minitab can make the statistical computations on your data more easy and quick. You can enter data into Minitab in the same way like doing in an excel. Minitab is a statistical analysis package which will enable you to enter the data quickly and then perform different type of analysis on the data. Now a dayвЂ™s ANOVA is also more commonly used in the field of Agriculture. Another main advantage of using ANOVA is that it can be used even if the number of observations in each group vary. In particular ANOVA helps to identify how a particular treatment works and how long it lasts. ANOVA is the most preferred model in the field of pharmaceuticals and medical research because it helps to find out how effective are the two different types of treatment and how durable they are. In such cases ANOVA becomes more reliable and effective. It is not reliable when there are more than two samples to be compared. The t-test compares the means between two samples and is also easy to conduct. There are only two tests which can yield the same results вЂ“ t-test and ANOVA. This is because it is easy to compute manually using simple algebra rather than complex calculations. Though there are different methods like hypothesis testing, partitioning of sum of squares and others ANOVA is given the first preference these days. The null hypothesis of ANOVA states that all means are equal and the alternative hypothesis states that at least one is different. ANOVA assesses the importance of one or more factors by comparing the variable means at different factor levels. It is used to test the specific differences between the means. It is a statistical method which is used to test the difference between two or more means. Thus, the engineer should be wary about using the model to make generalizations beyond the sample data.ANOVA stands for Analysis of Variance.

The imprecision may be due to the small size of the groups. The low predicted R 2 value indicates that the model generates imprecise predictions for new observations. The confidence intervals for the remaining pairs of means all include zero, which indicates that the differences are not significant. The engineer can use this estimate of the difference to determine whether the difference is practically significant. This range does not include zero, which indicates that the difference between these means is significant. The graph and the table that include the Tukey simultaneous confidence intervals show that the confidence interval for the difference between the means of Blend 2 and 4 is 3.114 to 15.886. The engineer uses the Tukey comparison results to formally test whether the difference between a pair of groups is statistically significant. The engineer knows that some of the group means are different. This result indicates that the mean differences between the hardness of the paint blends is statistically significant. The p-value for the paint hardness ANOVA is less than 0.05.

Minitab 16 anova