Minitab Course for Beginners

Lesson 1 • What Is Statistical Analysis

Defines statistics, its role in decision-making, and types of data. Establishes the conceptual framework that all subsequent Minitab tools will apply.

Lesson 2 • Entering and Managing Data

Covers data entry, column naming, and basic worksheet operations. Proper data structure is prerequisite to accurate analysis throughout the course.

Lesson 3 • Importing External Data Sources

Demonstrates importing data from spreadsheets and text files into Minitab. Connects real-world data collection to the Minitab analysis environment.

Lesson 1 • Measures of Central Tendency

Calculates mean, median, and mode using Minitab's Display Descriptive Statistics command. These measures form the baseline for comparing groups and detecting shifts.

Lesson 2 • Data Subsetting and Filtering

Applies Minitab's data subsetting tools to isolate relevant observations. Filtering enables focused analysis of subgroups identified in descriptive exploration.

Lesson 3 • Frequency Tables and Distributions

Creates frequency tables and histograms to reveal data distribution shape. Distribution shape informs the choice of parametric or nonparametric tests later.

Lesson 4 • Measures of Variability

Quantifies spread using range, variance, and standard deviation in Minitab. Understanding variability is essential for process control and hypothesis testing.

Lesson 1 • Core Probability Concepts

Reviews probability rules, events, and distributions at an applied level. Provides the theoretical grounding needed to interpret p-values and confidence intervals.

Lesson 2 • Other Common Distributions

Introduces binomial, Poisson, and t-distributions using Minitab's distribution tools. Recognising the correct distribution ensures accurate probability calculations.

Lesson 3 • The Normal Distribution in Minitab

Uses Minitab's Calc > Probability Distributions menu to compute normal probabilities. Builds intuition for the bell curve that underpins most parametric tests.

Lesson 1 • One-Sample Tests in Minitab

Runs one-sample t-tests and z-tests for the mean using Minitab's Stat menu. Provides the first hands-on experience linking hypothesis logic to software output.

Lesson 2 • Hypothesis Testing Framework

Establishes null and alternative hypotheses, Type I and Type II errors, and p-values. This framework governs every statistical test performed in subsequent chapters.

Lesson 3 • Confidence Interval Fundamentals

Explains confidence level, margin of error, and interval width. Confidence intervals quantify estimation uncertainty before formal hypothesis tests are introduced.

Lesson 4 • Two-Sample and Paired Tests

Compares two groups or paired observations using Minitab's two-sample t-test and paired t-test. Extends hypothesis testing to comparative business and process scenarios.

Lesson 1 • One-Way ANOVA Concepts

Explains the logic of partitioning variance into between-group and within-group components. Connects ANOVA to the hypothesis testing framework established in Chapter 4.

Lesson 2 • ANOVA Residual Diagnostics

Validates ANOVA assumptions using residual plots, normality tests, and equal variance checks. Diagnostic failures guide corrective actions such as transformations.

Lesson 3 • Running One-Way ANOVA in Minitab

Executes one-way ANOVA using Minitab's Stat > ANOVA menu and interprets the output table. Hands-on practice reinforces the connection between F-ratio and p-value decisions.

Lesson 4 • Post-Hoc Multiple Comparisons

Applies Tukey, Fisher, and Dunnett comparisons to identify which group pairs differ. Post-hoc tests prevent inflated Type I error when multiple pairs are compared.

Lesson 1 • Correlation Analysis in Minitab

Calculates Pearson and Spearman correlation coefficients and tests their significance. Correlation quantifies linear association before regression models are constructed.

Lesson 2 • Multiple Linear Regression

Extends regression to multiple predictors and applies variable selection methods in Minitab. Multiple regression addresses complex real-world relationships with several influencing factors.

Lesson 3 • Simple Linear Regression

Fits a straight-line model to one predictor and one response using Minitab's Regression menu. Students interpret slope, intercept, and R-squared for practical prediction.

Lesson 4 • Regression Diagnostics and Assumptions

Evaluates linearity, independence, homoscedasticity, and normality using Minitab's residual plots. Assumption violations identified here guide model corrections in the next section.

Lesson 1 • Variables Control Charts

Constructs Xbar-R, Xbar-S, and I-MR charts for continuous data in Minitab. Variables charts track process mean and spread simultaneously for complete process monitoring.

Lesson 2 • Attributes Control Charts

Builds p, np, c, and u charts for count and proportion data in Minitab. Attributes charts extend process monitoring to defect rates and nonconforming unit counts.

Lesson 3 • Control Chart Fundamentals

Introduces common cause vs. special cause variation and the logic of control limits. This conceptual foundation determines when a process requires intervention.

Lesson 4 • Process Capability Analysis

Calculates Cp, Cpk, Pp, and Ppk indices using Minitab's Capability Analysis command. Capability indices link process variation to specification limits for quality reporting.

Lesson 1 • Creating Professional Statistical Reports

Compiles graphs, tables, and narrative into polished Minitab reports and exports. Professional output communicates findings clearly to technical and non-technical audiences.

Lesson 2 • Chi-Square Tests in Minitab

Applies chi-square goodness-of-fit and tests of independence to categorical data. Chi-square tests extend hypothesis testing to frequency and association questions.

Lesson 3 • Measurement System Analysis

Conducts Gage R&R studies to quantify measurement system variation in Minitab. Reliable measurement is prerequisite to valid process control and regression conclusions.

Lesson 4 • Automating Analysis with Macros

Records and runs Minitab macros to automate repetitive analytical tasks. Automation reduces errors and accelerates reporting in high-volume analytical environments.