Six Sigma Statistics Using Minitab 17

Compare descriptive statistics with inferential statistics and learn sampling from a population to draw conclusions. Explore central tendency, dispersion, range, and tests like t test and analysis of variance.

Explore Minitab 17 basics, including data types (text, number, date and time), and row and column statistics, with simple operations like ranking and sorting. Try a 30-day trial to practice.

Navigate Minitab's four core areas—menu bar, toolbar, session window, and worksheet—plus the project manager, with data entry in columns and rows and easy copy-paste from Excel.

Explore the three data types in Minitab 17—numeric, text, and date/time—how to identify them by alignment, and how to convert between types using the data menu, with Excel import considerations.

Calculate the average score for each student using Minitab 17 row statistics, employing two approaches: the row statistics calculator and a stored expression, then round to one decimal.

Explore column statistics in Minitab 17 by using display descriptive statistics to compute the mean and count for each test, highlighting test three as the tougher exam.

Rank 24 students by their average scores from lowest to highest in Minitab 17, handle ties with average ranks, then sort columns by rank in decreasing order to reveal performers.

Explore graphs in Minitab, including box plots, potato charts, individual value plots, bar charts, and pie charts, and use the graphical assistant to select the right graph for your data.

Explore box plots with shipping-distance data, learn to compute mean, median, quartiles and interquartile range, and interpret the box plot with whiskers and outliers in Minitab.

Apply Pareto chart analysis to identify the 20% of defects driving 80% of problems using Minitab 17, and draw Pareto charts from single-column data or defect-frequency pairs, including by shift.

Draw an individual value plot in minitab 17 to compare distance across eastern, western, and central centers. Split the data by center to compare variation, noting western shows less variation.

Explore bar charts for defect analysis in paint floors, including plain, cluster, and stacked bar charts, and learn when to avoid continuous data and how Pareto charts differ.

Create a pie chart in Minitab to display shipment status by percentages—on time, late, and back orders—and tailor slice labels for clear, presentation-ready results.

Utilize the Minitab graphical analysis assistant to choose appropriate charts like Pareto, box plots, and histograms based on data type, including continuous data and defect analysis, revealing normal distribution.

Explore descriptive statistics, contrast with inferential statistics, and learn to summarize data using central tendency measures (mean, mode, median) and dispersion (range, standard deviation).

Explore descriptive statistics in Six Sigma with Minitab 17, focusing on central tendency and dispersion, including mean, mode, median, range, and standard deviation from discrete and continuous data.

Compute descriptive statistics for distance in Minitab, including mean, standard deviation, median, four modes, and range, and assess normality with a histogram and fit.

learn basic probability concepts through coin flips and dice, building a foundation that leads to distributions such as normal, binomial, and Poisson distributions, then hypothesis testing and ANOVA.

Learn why probability matters in quality and risk management. Explore sampling from a lot, defect likelihood, and contingency buffers, using dice, coin flips, and real-world scenarios like insurance.

The lecture defines probability as favorable outcomes over total outcomes, using a dice example to show sample space and simple probability, and explains classical, relative frequency, and subjective definitions.

Explore experiments, events, elementary events, and the sample space using dice rolls and sampling examples. Identify how outcomes become events within a defined sample space.

Visualize the sample space and events with a Wayne diagram, then illustrate union and intersection of A and B to reveal A or B and A and B outcomes.

Identify mutually exclusive events and independent events using Venn diagrams and practical dice and ball examples, and explain complementary events and their probabilities.

Understand marginal, union, and joint probabilities with real-world examples, and note that conditional probability is covered separately.

Apply the multiplication law to compute joint probabilities for independent events as P(A∩B)=P(A)P(B), and use conditional probability for dependent events. Use coin toss and sampling examples to illustrate these concepts.

Explore factorials, including zero factorial equals one, and master permutations and combinations with formulas n p r and n c r, illustrated by practical counting examples and probabilities.

Explore probability distributions in six sigma statistics using Minitab 17: learn normal, binomial, and Poisson distributions and distinguish between continuous and discrete variables.

Explore continuous versus discrete probability distributions, highlighting how discrete variables have step values while continuous distributions use the normal curve and area under the curve for ranges.

Explore the normal distribution’s symmetry, bell shape, and long tails, and see how mu and sigma define its curve and spread, where mean equals mode equals median.

Assess normality of the distance data in C6 using the Anderson-Darling test in Minitab, interpret the p-value and histogram to conclude the data is normally distributed.

apply normal distribution concepts using Minitab 17 to a bulb life example, calculating the failure probability below 360 hours and the 99% life guarantee around 283 hours.

explains the binomial distribution for discrete two-outcome trials, with n, p, and X, using five coin tosses to find exactly one head and derive mean and variance.

Explore binomial distribution in Minitab to compute exact and cumulative probabilities for coin flips and defect counts, including a 500-bolt lot with 1% defect rate.

Explains Poisson distribution and its components, including mu and lambda, and shows probability of one accident during peak time when the mean is three and the variance equals mu.

Learn to compute Poisson probabilities with Minitab, using a mean of 3 per day and 42 per ten minutes to assess exact and cumulative chances, including the 80 customers case.

Learn how tolerances define acceptable production and service limits and how to use Minitab to calculate Cp and Cpk, with notes on Pp and Ppk.

Define process capability and differentiate specification limits from process control limits using shaft examples, and outline conditions for capability studies: normal distribution, statistical control, representative samples, and sufficient sample size.

Compute CP and CPK from six sigma concepts, compare control and specification limits, and interpret process capability, including CP>1, CP=1 findings, and rejection rates in ppm.

Analyze process performance indices P, PK, and CPK; distinguish short-term from long-term sigma. Outline Taguchi's CPM concept using the target and USL and LSL.

Calculate process capability in minitab from a wire diameter data set with limits 0.5–0.6, reporting CP around 0.86, CPP ~0.79, CPU near 1, and left-shifted mean to 0.545.

Explore measurement system analysis to ensure reliable measurements in Six Sigma projects, distinguishing accuracy from precision using bias, linearity, stability, and gauge R&R, with Minitab exercises.

Explore measurement system analysis and its four components—gauge, procedures, operator, and training—to improve data-driven decisions and ensure measurements reflect true process performance, with Minitab guidance.

Learn how resolution drives measurement system analysis by applying the rule of ten, making instrument resolution one tenth of tolerance, illustrated with a 50 mm diameter example.

Explore accuracy versus precision in measurement system analysis, defining accuracy as the average near the true value and precision as the consistency of gauge readings.

Shows accuracy as nearness to the center and precision as consistency, and reveals four patterns—accurate and precise, accurate but imprecise, precise but inaccurate, neither.

Explore measurement system analysis by distinguishing accuracy from precision, and examining bias, linearity, and stability, while understanding how mean and variation shape the true value.

Explore how precision reflects measurement variation through pressure gauge readings, distinguishing repeatability (gauge variation) from reproducibility (operator variation) and showing how their sum forms total gauge variation.

Examine gauge R and R studies to quantify repeatability and reproducibility, compare measurement system variability to process variability, and ensure your gauge can discriminate between parts.

Explore the differences between crossed and nested Gage R&R studies, when each applies, and learn how to set up a sample Gage R&R in Minitab, for destructive tests.

Conduct a gauge R&R study with Minitab to perform measurement system analysis across multiple operators and parts. Interpret repeatability and reproducibility to evaluate measurement system suitability, noting 27.9% variation.

Assess gauge R&R in Minitab using the x bar r method to evaluate repeatability, revealing 26% variation—above the 10% limit and suggesting the gauge is not suitable for critical measurements.

Explore how correlation and regression reveal relationships between two variables, visualize with a scatter plot, compute the correlation coefficient, and derive a regression equation using Minitab.

Explore how hours studied relate to marks using scatter diagrams, correlation, and linear regression to derive the equation y = a + b.

Illustrates a scatter diagram of hours studied versus test scores from ten students, created in Excel, revealing a positive relationship and a roughly fitted line.

Draw a scatter diagram in Minitab to examine temperature versus strength, with a regression line and grouping by manufacturer A and B, revealing a negative correlation and differing slopes.

Calculate the Pearson correlation coefficient to quantify the relationship between study hours (X) and test scores (Y) using data, and interpret r from -1 to 1 to gauge strength.

Using Minitab, calculate the Pearson correlation coefficient to assess the strength and direction of the relationship between temperature and strength, revealing a strong negative correlation of -0.769.

Explore how hours studied and test scores show a strong correlation (r = 0.87) and how regression yields a best-fit line y = 15.79 + 0.9759 x to predict scores.

Compute the regression equation from a fitted line plot in Minitab to relate temperature to strength, and interpret R square about 0.59 with a standard error of 70 units.