Move beyond describing data — learn to draw conclusions about populations from samples using hypothesis tests, confidence intervals, and regression at GreatLakes Manufacturing.
Work through each chapter in order. Each one builds on the last.
The logic of hypothesis testing, null and alternative hypotheses, Type I and II errors, and Z-tests for a population mean.
🕑 ~50 min readOne-sample t-tests when population standard deviation is unknown, Student’s t-distribution, and degrees of freedom.
🕑 ~50 min readIndependent and paired two-sample t-tests for comparing means across groups at GreatLakes Manufacturing.
🕑 ~45 min readConstructing and interpreting confidence intervals for means and proportions in manufacturing quality control.
🕑 ~45 min readMeasuring and testing the strength of linear relationships between manufacturing variables.
🕑 ~40 min readFitting a line to data, interpreting slope and intercept, R-squared, and prediction at GreatLakes Manufacturing.
🕑 ~55 min readChi-square goodness-of-fit and tests of independence for categorical data in manufacturing quality.
🕑 ~50 min readBy the end of this course, you will be able to:
Formulate and test hypotheses using Z-tests and t-tests, selecting the right test for each business scenario.
Construct and interpret confidence intervals for population means and proportions with correct margin-of-error reasoning.
Compare two groups using independent and paired t-tests and draw valid conclusions about manufacturing processes.
Measure correlation between variables, fit simple linear regression models, and interpret slope, intercept, and R-squared.
Apply chi-square tests to categorical data to assess goodness-of-fit and independence in quality control contexts.
What you need before starting this course.
This course assumes you are comfortable with means, standard deviations, and basic data visualization. Complete STATS100 first if you need a refresher on descriptive statistics fundamentals.
Practice what you learn with these interactive stats games.
Given a scenario, decide whether to use a Z-test or a t-test. Build decision-making speed.
Play Now →Estimate p-values from test statistics and make reject/fail-to-reject decisions under pressure.
Play Now →Build confidence intervals from sample data and see how often they capture the true parameter.
Play Now →Look at scatter plots and estimate the correlation coefficient. Train your visual intuition for r.
Play Now →Compute expected frequencies, chi-square statistics, and decide whether to reject independence.
Play Now →