Minitab Statistical Software Tutorial

[Fernando Capelle]

Thistutorial covers many aspects of regression analysis including: choosing the type of regression analysis to use, specifying the model, interpreting the results, determining how well the model fits, making predictions, and checking the assumptions. At the end, I include examples of different types of regression analyses.

Why should we choose regression at all? What are the common mistakes that even experts make when it comes to regression analysis? And, how do you distinguish a good regression analysis from a less rigorous regression analysis? Read these posts to find out:

Choosing the correct type of regression analysis is just the first step in this regression tutorial. Next, you need to specify the model. Model specification consists of determining which predictor variables to include in the model and whether you need to model curvature and interactions between predictor variables.

In addition to determining how the response variable changes when you change the values of the predictor variables, the other key benefit of regression is the ability to make predictions. In this part of the regression tutorial, I cover how to do just this.

Like any statistical test, regression analysis has assumptions that you should satisfy, or the results can be invalid. In regression analysis, the main way to check the assumptions is to assess the residual plots. The following posts in the tutorial show you how to do this and offer suggestions for how to fix problems.

The final part of the regression tutorial contains examples of the different types of regression analysis that Minitab can perform. Many of these regression examples include the data sets so you can try it yourself!

This series of five videos provide guided tutorials on using Minitab, a statistical analysis software that automates calculations and the creation of graphs. The videos were created by Dr Jill MacKay and are aimed at students of the BVM&S Student Research Course.

Minitab tutorial defines the detailed information of Minitab software. Minitab is the statistical software package used for quality improvement and data analysis. It provides the tool for data visualization, regression analysis, hypothesis testing, process control, and experiment design. Minitab software is commonly used in academia, industry, and research to analyze data to make decisions. Minitab provides online resources and tutorials which help users to learn the software.

The Minitab tutorial provides a step-by-step guide on how we can use Minitab software for the analysis of data. Minitab tutorial covers multiple topics such as the definition of the Minitab tutorial, overviews of the Minitab tutorial, why we need to learn the Minitab tutorial, Minitab applications, Minitab example, prerequisite, and target audience. This tutorial includes a practical example that helps us to understand how we can use the software effectively.

The Minitab tutorial is suitable for beginners and advanced users who want to learn Minitab for statistical analysis. Minitab tutorial has multiple uses in various applications. Minitab software is used to manipulate the data in a worksheet and then we can use the same data for computations. In this article, we have used multiple topics to describe Minitab.

As we know that Minitab is a statistical software package used for quality improvement in multiple industries such as healthcare, finance, manufacturing, and education. Below are the applications of Minitab as follows.

To follow the tutorial of Minitab we required a basic understanding of statistics such as the concept of the confidence interval, statistical significance, and hypothesis testing. We required a basic understanding of data analysis techniques and data types. Additionally, we have required a basic understanding of how we can use the statistical software package as well as we required some familiarity with the user interface of Minitab.

If suppose we are new to Minitab then we need to start with the introductory tutorials provides by online resources. To follow this tutorial we have also required a basic understanding of application software. A basic understanding of programing language is added advantage.

The target audience of this tutorial varies as per the levels of the specific applications. Specifically, this tutorial is helpful for individuals who want to learn how we can use Minitab for data analysis and modeling. Below is the target audience of the Minitab tutorial as follows.

This tutorial includes a practical example that helps us to understand how we can use the software effectively. The Minitab tutorial is suitable for beginners and advanced users who want to learn Minitab for statistical analysis. Minitab software is commonly used in academia, industry, and research to analyze data to make decisions. Minitab provides online resources and tutorials which help users to learn the software.

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Easy to use statistical software has become a essential tool in almost all businesses as more companies seek to have their employees basing decisions on data. Comprehensive statistical analyses, building of sophisticated descriptive and predictive models, and mining data to discover insights are no longer just done by statisticians but by those across many functional areas within an organization. Academia is experiencing a similar need. There has been a substantial increase in the number of programs and courses across a wide range of disciplines exposing students to the tools and techniques of the statistical sciences. Interactive point-and-click software can greatly reduce the barrier for students to develop the necessary skills and enjoy the data exploration process without the need for programming.

Minitab is a graphical user interface-based (GUI) statistical software program first developed in 1978 as a computing tool to support introductory statistics courses. Minitab v19, released in June 2020, is the version featured in this white paper.

JMP and JMP Pro are also GUI-based software, first developed by SAS in 1989 to support engineers, scientists and researchers in applying the tools of the statistical sciences in a nonprogramming, highly interactive environment. JMP and JMP Pro are used across a wide range of industries and academic disciplines. JMP 16 and JMP Pro 16, released in March 2021, are the versions featured in this paper.

As this is a transition guide rather than a comprehensive coverage of their individual capabilities, we are unable to cover all the differences between the two in this white paper. Visit minitab.com and jmp.com to learn more.

Easy to use statistical software has become a essential tool in almost all businesses as more companies seek to have their employees basing decisions on data. Comprehensive statistical analyses, building of sophisticated descriptive and predictive models, and mining data to discover insights are no longer just done by statisticians but by those across many functional areas within an organization. Academia is experiencing a similar need. There has been a substantial increase in the number of programs and courses across a wide range of disciplines exposing students to the tools and techniques of the statistical sciences. Interactive point-and-click software can greatly reduce the barrior for students to develop the necessary skills and enjoy the data exploration process without the need for programming.

As this is a transition guide rather than a comprehensive coverage of their individual capabilities, we are unable to cover all the differences between the two in this white paper. Visit www.minitab.com and www.jmp.com to learn more.

2. Contextual menus. Each analysis output performed in JMP is connected with access to a set of tools related to that analysis. In this way, the approach to analyzing data is more exploratory and interactive in nature, with the results of one analysis guiding the user to a set of tools that are designed as next steps in the analysis workflow.

Minitab runs analyses in a more traditional batch mode, with the user often having to decide upon which analysis to perform entirely separate from seeing features in the data. When a tool is selected, a dialog box is open. Variables are assigned roles and options are chosen. Running the analysis produces a set of output that is typically static. The next step in a typical analysis workflow often requires a new tool to be launched requiring another round of specifying variable roles and options.

3. Defining data to be used for analysis. The typical approach in Minitab is to create a worksheet that contains all the data that a particular analysis will be based on. If it is desired, for example, to perform an analysis on a subset of that data, it is usually required to perform an operation (e.g., Subset, Split Worksheet, etc.) to create a new worksheet. If outliers are discovered an the analyst wishes to see the results without those data points, another new worksheet would typically need to be created and the analysis run again. This requires the need to manage many worksheets in a typical analysis session.

Though one is also able to work with multiple data tables in JMP, it can often be easiest to use Data Filters and Row State operations to define the data to base a specific analysis on. These data filters and row states can be saved and accessed at any time, which allows the analyst to have a single data table upon which all analyses on various subsets of that data can be performed.

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