Duration
2 days
Course Outline
In this 2-days foundation course, you will learn to decrease the time required for statistical analysis by quickly learning how to navigate Minitab’s user-friendly and customizable environment. Learn how to easily import data and export output. Develop sound statistical approaches to data analysis by learning how to create and interpret a wide variety of graphs. Augment your graphical analysis by learning the foundation for important statistical concepts such as hypothesis testing and confidence intervals. By analyzing a variety of real world data sets, learn how to match the appropriate statistical tools to your own applications and how to correctly interpret statistical output to reveal problems with a process or to show evidence of an improvement. Learn how to explore your processes through statistical modeling tools that help to uncover and describe relationships between variables. A strong emphasis is placed on making good business decisions based upon the practical application of statistical techniques commonly found in manufacturing, engineering, and research and development endeavors.
Topics covered in the training material include: ODBC, Bar Charts, Histograms, Boxplots, Pareto Charts, Scatterplots, Tables and Chi-Square Analysis, Measures of Location and Variation, t-Tests, Proportion Tests, Tests for Equal Variances, Power and Sample Size, Correlation, Simple Linear and Multiple Regression, ANOVA and GLM
Prerequisite: None. This course is a prerequisite for all other general Minitab courses.
Title
Objectives
Overview of MINITAB
- Understand the Minitab file structure.
- Navigate the Minitab environment using the Project Manager.
Introduction to Minitab
- Import data from other software programs.
- Create and run Exec files to re-create analysis steps.
- Display charts and plots to represent data.
- Use Minitab tools to recode data and create new variables.
- Restructure data for analysis in Minitab.
Hypothesis Tests: Continuous Data
- Evaluate the difference between a process (population) mean and a target value using a 1-sample t-test and confidence intervals.
- Assess the power of a hypothesis test using power analysis.
- Test for a difference between two population means using a 2-sample t-test.
- Test for a difference between paired observations using a paired t-test.
Hypothesis Tests: Attribute Data
- Determine the appropriate sample size for a 1-proportion test.
- Determine whether a defect rate is different from the target value using the 1-proportion test.
- Determine whether defect rates are different from one another using the 2-proportions test.
- Determine whether two categorical variables are associated.
Equivalence Testing (Optional)
- Assess the power of a hypothesis test using power analysis.
- Test for a similarity between two population means using a 2-sample Equivalence test.
- Test for a similarity between paired observations using a Paired Equivalence test.
Analysis of Variance
- Assess the power of an analysis of variance using power analysis.
- Compare group variances using a variance test.
- Compare means for samples collected at different levels using a general linear model.
- Perform ANOVA with more than one factor.
- Interpret interaction plots and multiple comparisons.
Correlation and Simple Regression
- Measure the degree of linear association between two or more variables using correlation.
- Model the relationship between a continuous response variable and a predictor variable.
Multiple Correlation and Regression
- Measure the degree of linear association between two or more variables using correlation.
- Perform regression analysis with more than one predictor.
- Understand the causes and effects of multicollinearity in multiple regression analysis.
- Learn about automated procedures that can be used to help determine the final model.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.