"Six Sigma Statistics with EXCEL and MINITAB" Table of Contents

The following is the Table Of Contents for the book "Six Sigma Statistics with EXCEL and MINITAB" by Issa Bass.

Chapter 1. Introduction
1.1 Six Sigma Methodology
1.1.1 Define the Organization
A. Mission statement
B. What questions should an organization ask?
What does the organization produce?
How does it produce its goods or services?
Who are the customers?
Who are its suppliers?
Who are the organization's competitors?
Who are the competitors' customers and suppliers
1.1.2 Measure the Organization
Measuring the organization through Balanced Scorecards
1.1.3 Analyze the Organization
What metrics should be used and what is the standard formula to calculate metrics?
How to analyze the organization?
1.1.4 Improve the Organization
DMAIC Roadmap
1.2 Statistics, Quality Control and Six Sigma
1.2.1 Poor quality defined as a deviation from engineered standards
1.2.2 Sampling and Quality Control
1.3 Statistical Definition of Six Sigma
1.3.1 Variability, the source of defects
1.3.2 Evaluation of the process performance
1.3.3. Normal distribution and process capability
Chapter 2. Introduction to Minitab and Excel
2.1 Starting with Minitab
2.1.2 Minitab’s menus
A. File Menu
B. Edit Menu
C. Data Menu
D. Calc Menu
E. Basic functionalities of Minitab Calculator
F. Help Menu
2.2 An overview of Data Analysis with Excel
2.2.1 Graphical display of data
2.2.3 Data Analysis Add-in
Chapter 3. Basic tool for data collection, organization and description
3.1 The Measures of Central Tendency give a first perception of your data
3.1.1 Arithmetic mean
A. Arithmetic mean for raw data
B. Arithmetic mean of grouped data
3.1.2 Geometric mean
3.1.3 Mode
3.1.3 Median
3.2 How Much Variability Do Your Data Exhibit? Measures of dispersion
3.2.1 Range
3.2.2 Mean Deviation
3.2.3 Mean Deviation
3.2.4 Variance
3.2.5 Standard Deviation
3.2.6 Chebycheff's theorem
3.2.7 Coefficient of variation
3.3 The Measures of Association quantify the level of relatedness between factors
3.3.1 Covariance
3.3.2 Correlation coefficient
3.3.3 Coefficient of determination
3.4 Graphical Representation of Data.
3.4.1 Histograms
3.4.2 Stem-and-Leaf
3.4.4 Box Plots
3.5 Descriptive Statistics: Minitab and Excel Summaries
Chapter 4. What do your data tell you? Chances are…… - Introduction to Probability
4.1 Discrete and continuous distributions
4.2 Discrete Distributions
4.2.1 Binomial Distribution
4.2.2 Hypergeometric Distribution
4.2.3 Poisson Distribution
4.2.4 Geometric Distribution
4.2.5 Excel Examples
4.2.6 Minitab Examples
4.3 Continuous Distributions
4.3.1 Uniform Distribution
4.3.2 Normal Distribution
4.3.3 Lognormal Distribution
4.3.4 Exponential Distribution
Chapter 5. How to determine, analyze and interpret your samples?
5.1 Central Limit Theorem
5.2 Sampling Distribution for the Mean
5.3 Sampling Distribution for Proportions
5.4 Estimating Populations with Large Sample sizes
5.5 Estimating Populations with small sample sizes and sigma unknown
5.5.1 The t – distribution
5.5.2 The Chi square distribution
5.6 Estimating Sample sizes
5.6.1 Sample Size when estimating the mean
5.6.2 Sample size when estimating proportions
5.7 Minitab and Excel exercises
Chapter 6. Comparing two samples means and variances to make an inference about the populations – Hypothesis Testing
6.1 How to conduct a hypothesis testing
6.1.1 Null hypothesis
6.1.2 Alternate hypothesis
6.1.3 Test statistic
6.1.4 Level of significance or level of risk
6.1.5 Decision rule determination
6.1.6 Decision making
6.2 Testing for a population mean
6.2.1 Large sample with known sigma
6.2.2 What is the p-value and how to interpret it?
6.2.3 Small samples with unknown sigma
6.3 Hypothesis Testing about proportions
6.4 Hypothesis Testing about the variance
6.5 Statistical Inference about two populations
6.5.1 Inference about the difference between two means
6.5.2 Small independent samples with equal variances
6.5.3 Testing the hypothesis about two variances
6.6 Testing for Normality of data
Chapter 7. Know what your process is producing and keeping it under control - Statistical Process Control
7.1 How to build a control chart
7.2 The Western Electric (WECO) Rules
7.3 Types of control charts
7.3.1 Attribute control charts
7.3.1.1 The p – chart
7.3.1.2 np – chart
7.3.1.3 The c – chart
7.3.1.4 u – chart
7.3.2 Variable Control Charts
7.3.2.1 X and R charts
7.3.2.2 Standard error based X chart
7.3.2.3 Mean Range based X control charts
A. R chart
7.3.2.4 X and S control Charts
7.3.2.5 Moving Range
Chapter 8. Is your process meeting your customers’ expectations? - Process Capability Analysis
8.1 Process Capability With Normal Data.
8.1.1 Potential Capabilities Vs. Actual Capabilities
A. Short Term Potential Capabilities, Cp and Cr
B. Process Performance, Long Term Potential Process Capabilities
8.1.2 Actual Process Capability Indices
8.2 Taguchi’s Capability indices CPM and PPM
8.3 Process Capability and PPM
8.4 Capability Sixpack for Normally Distributed Data
8.5 Process Capability Analysis with non-normal data
8.5.1 Normality Assumption and Box – Cox Transformation
8.5.2 Process Capability Using Box – Cox Transformation
8.5.3 Process Capability Using Non-normal distribution
Chapter 9. Making an inference about more than two population means - Analysis Of Variance
9.1 ANOVA and Hypothesis testing
9.2 Completely Randomized Experimental Design (One-Way ANOVA)
9.2.1 Degree of Freedom
9.2.2 Multiple comparison tests
A. Tukey's Honestly Significant Difference (HSD) Test
9.3 The Randomized Block Design
9.4 Analysis Of Means -ANOM
Chapter 10. Building a Model to predict Variations – Regression Analysis
10.1 Building a model with only two variables - Simple Linear Regression
10.1.1 Plotting the combination of X and Y to visualize the relationship - Scatter Plot
A. Using Minitab
B. Scatter plot using Excel
10.1.2 The regression equation
10.1.3 Least Square Method
A. Using Minitab to find the Regression equation
B. Using Excel to conduct a regression analysis
10.1.4 How far are the results of our analysis from the true values -Residual Analysis
10.1.5 Standard Error of Estimate
10.1.6 How strong is the relationship between X and Y- Coefficient of Correlation
A. Using Excel
B. Using Minitab
10.1.7 Coefficient of Determination or what proportion in the variation of Y is explained by the changes in X
10.1.8 Testing the validity of the regression line - Hypothesis test for the slope of the regression model
10.1.9 Confidence Interval to estimate the mean
10.1.10 Fitted Line Plot
10.2 Building a Model with more than Two Variables - Multiple Regression Analysis
10.2.1 Hypothesis testing for the coefficients
A. Using Excel
B. Building a Multiple Regression using Minitab
10.2.2 Interpretation of the results
A. P – Values for the coefficients
B. Adjusted Coefficient of Determination
C. Multicollinearity
10.2.3 Stepwise Regression
A. Standard Stepwise
B. Forward Selection
C. Backward elimination
Chapter 11. What combination of factor is optimal for producing high quality at a lower cost?
11.1 Introduction to Design Of Experiment
11.1.1 Factorial Design with Two Factors
11.1.2 Factorial Design with More than Two Factors
11.1.3 Fractional Factorial Design
Chapter 12. Taguchi Method or Murphy’s Law. “Anything that can go wrong will go wrong!”
12.1 Assessing the cost of Quality
12.1.1 Cost of conformance
12.1.2 Cost of non conformance
12.2 Taguchi's Loss Function
12.3 Variability Reduction
12.3.1 Concept Design
12.3.2 Parameter Design
A. The Bigger-The-Better
B. The Smaller-The-Better
C. The Nominal-The-Best
12.3.4 Tolerance Design
Chapter 13. Is your measurement process lying to you? -Measurement System Analysis
13.1 Variation due to precision – Assessing the spread of the measurement
13.1.1 Gage R&R Crossed
A. X R-chart
B. Repeatability and Reproducibility
13.1.2 Gage R&R Nested
13.2 Gage Run Chart
13.3 Variations due to Accuracy
13.3.1 Gage Bias
13.3.2 Gage Linearity
Chapter 14. How to test ordinal or nominal data? - Nonparametric Statistics
14.1 The Mann-Whitney U test
14.1.1 The Mann-Whitney U test for small samples
14.1.2 The Mann-Whitney U test for large samples
14.2 The Chi Square tests
14.2.1 The Chi Square Goodness-Of-Fit test
14.2.2 Contingency Analysis – Chi Square Test of Independence
Chapter 15. Basic Quality Tools
15.1 Pareto Analysis
15.2 Cause and Effect Diagram


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