How to do Multiple Regression Analysis in Minitab 16

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Priss80

#1
I am using Minitab 16, i can't find the multiple regression tab althought I click into "assistant"/ "Regresion". Anyone know and help.
 
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Miner

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#2
You can perform multiple regression in Minitab 16 using either Stat > Regression > Regression or Stat > Regression > General Regression.

I recommend using General Regression as it is easier to specify your model.
 

reynald

Quite Involved in Discussions
#3
Happy New Year!

Minitab shares the command with simple regression and multiple regression.
As Miner said go to Stat > Regression > Regression. Then input as many predictors as you need. If you want interactions between 2 numerical columns create a third column containing the product of those two columns and add that as a predictor.

Regards,
Reynald
 

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Priss80

#4
What is the different between Regression>Regression and Regression>Regression General in Minitab?
 
S

sajid7

#5
Re: AS91X0 Third Party Auditor Cycling and Changing Auditors

Thus far everything is going as planned, and i'm almost there. But i'm stuck on the Regression analysis. The standard error of estimates to be precise.
 

Miner

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#6
You will obtain the same results from each. Regression is an older version that retains a lot of manual control for those that are very adept at regression analysis. However, as reynald indicated, to include interactions (and quadratics) you must manually create these factors in their own columns.

General Regression is a recent addition that is more user-friendly. To create an interaction, you simply add A*B to the model with no need to create the factor in a column.

In Minitab 17, both were consolidated into a single option.
 
P

Priss80

#7
What is the purpose of categorical predictors (optional) in general regression? What to select fill in this area? Is it related to the type of coding? In the worksheet, what variable to be fill in for the category predictor, example speed high, low or 1, 0
 

reynald

Quite Involved in Discussions
#8
Since this is general regression, predictors can either be continuous (i.e. simple linear regression) or ordinal/nominal variables (logistic regression) or both (general linear regression).

On that slot you put in the categorical predictors. Otherwise it will be read as a continuous variable and can lead to error.
 

Miner

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#9
The effect of using a categorical predictor is the you get a separate regression equation for each level of the categorical predictor.

For example: You want to perform a regression using weight as the Dependent Variable (DV) using height and gender as the Independent Variables (IV). Gender is categorical (M/F) and height is continuous. You will get two regression equations (e.g., weight F = B0 + B1*height F and weight M = B2 + B3*height M ).
 
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