Advanced Lean Six Sigma: Practical Data Analysis with Excel

Probability and distribution types drive data-driven decisions in Lean Six Sigma, highlighting the normal and binomial distributions for continuous and discrete outcomes, to predict performance and improve quality.

Attribute agreement analysis measures inspection accuracy and consistency in pass/fail parts, exposing bias, false rejects, and false accepts, and guides training and tool improvements, operator consistency and agreement between operators.

Apply statistical process control (SPC) and control charts to monitor process stability, differentiating random common cause variation from assignable special cause variation using rational subgroups, mean, range, and standard deviation.

Master descriptive statistics for continuous data, including central tendency, spread, and distribution shape, to support data-driven decisions in Lean Six Sigma.

Explore z scores, standard scores that show how far a value lies from the mean in standard deviations, and connect them to the standard normal distribution and alpha.

Master hypothesis testing to decide if sample evidence reflects the population, using null hypothesis H0, alternative hypothesis Ha, p values, alpha, and two-tailed, left-tailed, or right-tailed tests to compare means.

Examine how sum of squares, degrees of freedom, and mean square reveal variation sources in ANOVA. Interpret SS rows, SS columns, and SS error for a small non-replicated data set.

Compute the mean square by dividing the sum of squares by degrees of freedom. Use mean squares to compare variation and form the F statistic for significance.

Apply linear regression to predict a dependent variable from an independent variable, distinguish correlation from causation, account for outliers, and evaluate model fit with r squared using Excel.

Apply the Dmaic framework to a Lean Six Sigma project on a 500ml bottle line. Define, measure, analyze, improve, and control quality with SIPOC, control charts, and hypothesis testing.

Measure how efficiently assets like machinery and inventory generate profit. Compute ROA by dividing net income by total assets, highlighting a 20% return on assets.