Minitab express and minitab 18 comparison

#Minitab express and minitab 18 comparison code
#Minitab express and minitab 18 comparison download

So again, we're failing to reject, meaning that there is no interaction between the two factors in this case, age and gender. And then this last one is interaction that's even at higher P value. So whether you're 15, 16, 17, 18, the means are about the same. There is no difference in means for mean gps for H. So this is a very very big P value, meaning that again, we fail to reject H. So that's looking at the ages 15, 16, 17 18 year olds. That means there is no difference in means of male and female. So that means that we're failing to reject. And that's significantly greater than The alpha value of. So the source of variation whenever it's a sample, then we're looking at the row titles in this case that's gender. So I can I don't need to worry about the F statistic or the F critical value or anything. So I'm looking at the innova table and I'm gonna use the P value methods.

#Minitab express and minitab 18 comparison code

generation of with Minitab 17- 18 Yates's Correction for Continuity 162 code 44-5,47,50, 75. The only thing that we care about is this analysis of variance down here. Index (Entries in bold are Minitab commands and key words.). And this gives us a lot of information that really isn't important to us. But just put it anywhere over here where there's some space and this should be good to go. And then for the output range I put it in G. And then the alpha value book says use 30.10. So there are three of each that for male and female. And then so that's for me it's from a one to E seven and then rose per sample. This is a two way innova test and for the input range just make sure everything is highlighted here, including the not just the data values but including the titles of them rows and columns. There's a lot of cool things you can do and I'm gonna choose the Unova to factor with replication.

#Minitab express and minitab 18 comparison download

Now if you don't have the data analysis, you can download it assuming you have Excel and it works really, really well. So if you punch in those uh data values and you press data or click on data and go to data analysis. So the book says that you you know use technology and I think Excel works great. And then the third is that there's no interaction between age and gender in this case. So 15, 16, 17, 18, that means there's no uh we're assuming that there are no difference in the means of of of that. The second is that there's no difference in means of factor b. So the first uh, null hypothesis is that there is no difference in means of factor A and that's gonna be the myth agenda of the students are male or female. And we never really usually write the alternative hypothesis, but the alternative is just the opposite of these. And here you have, you know, average gps and the null hypothesis are three null hypotheses. And this is obviously high school students of 15, 16, 17 and 18 year olds and then male and female. And this study is the gps of males and females and they're different ages for. The following is a solution for a two way in over test for number 17.