Statistics & Minitab for Lean Six Sigma Certification -BB&GB

After completing this module you will clearly understand

1. Measures of central tendencies - Mean , Median and Mode

2. Where to apply Mean , Median Mode

3. How to calculate all of this with a click of a button using Minitab

After completing this module you will clearly understand

1. What is shape and distribution ?

2. How will median and mean be affected by shape

After completing this module you will clearly understand

1. Measures of variation - 

2. What is range and how to calculate it

3. What is variance  and how to calculate it

4. What is standard deviation and how to calculate it

After completing this module you will clearly understand

1. What are skewness and kurtosis

2. What are quartiles

3. How to calculate all of these in Minitab

After completing this module you will clearly understand

1. How to calculate descriptive statistics in Minitab

2. How to build a graphical summary of basic statistics in Minitab

3. How to interpret the results of descriptive summary and graphical summary in Minitab

4. How to create a subset of a worksheet

After completing this module you will clearly understand

1. What is a box plot

2. How to interpret your data using a box plot

After completing this module you will clearly understand

1. How to build a box plot in Minitab

2. How to interpret the results

   
   

After completing this module you will clearly understand

1. What is a  pareto chart

2. Uses of pareto chart in quality control

3. How to construct a pareto chart in Minitab and how to interpret the same

After completing this module you will clearly understand

1. What is a time series

2. How to build time series in Minitab

3. How to interpret time series chart

After completing this module you will clearly understand

1. How to build a dot plot

2. How to interpret a dot plot

   
   

After completing this module you will clearly understand

1. What is a run chart

2. Uses of run chart

3. How to interpret a run chart

After completing this module you will clearly understand

1. What is a distribution

2. Importance of distribution in statistics

3. How to identify the distribution family of your data set in Minitab

4. Benefits of knowing the distribution family

After completing this module you will clearly understand

1. What is a normal distribution

2. Properties of a normal distribution

3. Benefits of normal distribution

After completing this module you will clearly understand

1. What is PDF and CDF

2. How to calculate PDF and CDF in Minitab

3. How to predict the probability or values using PDF and CDF calculator

After completing this module you will clearly understand

1. What is PDF and CDF

2. How to calculate PDF and CDF in Minitab  for non normal data

3. How to predict the probability or values using PDF and CDF calculator for non normal data

After completing this module you will clearly understand

1. How to check whether your data follows a normal distribution

2. Anderson darling test of normality check

3. How to interpret Anderson darling normality test results

After completing this module you will clearly understand

1. What do you mean by box cox transformation 

2. How to convert a non normal distribution to a normal distribution

3. How to perform box cox transformation using Minitab

After completing this module you will clearly understand

1. What do you mean by Measurement system analysis (MSA)

2. What are the various components of MSA

3. What is the importance of MSA in data analysis

This section will be very useful for participants from industries where physical inspection of products or services are performed by operators. Participants from industries where inspection/quality inspections/product inspections  are 100% automated without inspection operators or appraisers taking decisions may  not apply this technique.

After completing this module you will clearly understand

1. What do you mean by Gauge R&R for discrete data

2. What is the objective of Gauge R&R for discrete data

3. How can you identify multiple issues with Gauge operators (Understanding and training issues)

4. How to interpret results of Gauge R&R for discrete data in Minitab

This lecture will be useful for participants from physical product industry where product inspections are performed manually using a physical gauge , gauge operator and physical products. People from industries where measurements  are through  Automated  measurement equipment system may not apply this technique.

After completing this module you will clearly understand

1. What do you mean by Gauge R&R for continuous data

2. What is the objective of Gauge R&R for continuous data

3. How can you identify issues with Gauge

4. How can you identify issues with Gauge operators (Understanding and training issues)

5. How to interpret results of Gauge R&R for continuous data in Minitab

After completing this module you will clearly understand

1. What do you mean by process sigma

2. What do you mean by process capability and process capability index

3. The difference between short term and long term process sigma and capability

4. How to calculate Cp , Cpk , Pp . Ppk and process sigma

5. Relationship between Cpk , Ppk and Z values

6. What do you mean by Six Sigma

After completing this module you will clearly understand

1. What is Z usl , Z lsl , Z bench

2. What does it mean when z value becomes negative

3. How to interpret different z values correlate with PPM and % outside limits

4. What do you mean by overall capability and Within capability

5. What do you mean by observed performance

6. What do you mean by expected performance

7. What do you mean by PPM

After completing this module you will clearly understand

1. How to run process capability and process capability analysis in Minitab

2. What is Cpm and how to calculate Cpm

3. What is PPM - Parts per million

4. What are the acceptable values of Cp , Cpk , Pp , Ppk

After completing this module you will clearly understand

1. How to calculate process capability index for non normal data

2. How to calculate process capability if you cannot find a matching distribution in Minitab

3. How Z value is calculated for non normal distributions

4. How to calculate Z value from Ppk

5. Why Cpk is not calculated for non normal data

After completing this module you will clearly understand

1. What is discrete data

2. What is binomial distribution

3. What is Poisson distribution

4. How to identify whether your data follows Binomial distribution

5. What are the conditions for Binomial distribution

6. How to identify whether your data follows Poisson distribution

7. What are the conditions for Poisson distribution

8. What is % Defective and DPU

9. How to calculate process capability of process with discrete data using Minitab

After completing this module you will clearly understand

1. What do you mean by hypothesis testing

2. Null hypothesis and alternate hypothesis

3. How to construct null hypothesis and alternate hypothesis

4. Different types of Hypothesis testing

5. When to use and where to use hypothesis testing

6. Importance of p value

7. How to interpret and use P value

After completing this module you will clearly understand

1. What are the different types of risks handled in Hypothesis testing

2. Type 1 error , Type 2 error , alpha risk , beta risk , consumers risk and producers risk

3. What is P value and what is alpha value

4. When to reject null hypothesis and when to accept null hypothesis using p value

5. Critical statistics and test statistics

6. How to calculate and set alpha value for different confidence levels

7. How to take decision on rejecting or accepting null hypothesis using critical statistics and test statistics

8. Difference between single tail and double tail hypothesis testing (Single side or Double side)

After completing this module you will clearly understand

1. What is 1 sample t test

2. When to use 1 sample t test

3. What is a T distribution

4. Formula to calculate T statistics

5. Method to find out T critical value

6. Pre conditions to run 1 sample t test

7. Practical application of 1 sample t test

After completing this module you will clearly understand

1. What do you mean by 2 sample t test

2. Where to apply 2 sample t test in your business areas

3. What are the expected results after using 2 sample t test

4. Practical applications of the results from 2 sample t test

5. How to interpret the results of 2 sample t test

After completing this module you will clearly understand

1. What do you mean by paired t test

2. Business situations where you can apply paired t test

3. When you should not apply it

4. How to interpret the results of paired t test

After completing this module you will clearly understand

1. What do you mean by ANOVA ?

2. Why is called as ANOVA when it is testing the means

3. Pre requisites for running ANOVA

4. Requirements to be met post ANOVA model completion (Before using for prediction)

5. What is Tuckey pairwise comparison

6. What can you infer from  Tuckey pairwise comparison

   
   

After completing this module you will clearly understand

1. What do you mean by 1 proportion test

2. When and where do you use 1 p test

3. Importance of Binomial distribution in 1 p test

4. Critical things to be considered while running the test in Minitab including

   1. Method to select

   2. How to handle raw data in Minitab to run 1 p test

5. The importance of z distribution in 1 p test

After completing this module you will clearly understand

1. What do you mean by 2  proportion test

2. When and where do you use 2 p test

3. Critical things to be considered while running the test in Minitab including

   1. Method to select

4. How to handle beta risk in Hypothesis testing

5. Things to be considered while accepting null hypothesis

6. What recommendations should you provide when accepting null hypothesis 

After completing this module you will clearly understand

1. What do you mean by chi square test

2. What is a chi square distribution

3. What is the relationship between chi squared distribution and normal distribution

4. When do you use Chi square test

5. How to calculate the chi square statistics

6. How to calculate expected value in chi square test

7. How to calculate degrees of freedom in a chi square test

8. Null hypothesis and alternate hypothesis for chi square test

9. Difference between Pearson's  chi square and Likelihood ratio test chi square  , when to use it

10. How to manually calculate p value for chi square test using minitab

After completing this module you will clearly understand

1. What do you mean by Non Parametric test

2. What do you mean by 1 Sample sign test

3. What is the parametric equivalent of 1 Sample sign test

4. When do we use 1 sample sing test

5. How is the P value calculated in 1 sample sing test

6. What is the importance of Binomial distribution in 1 sample sign test

7. How to frame null and alternate hypothesis for 1 sample sign test

After completing this module you will clearly understand

1. What do you mean by Mann Whitney test

2. What is the parametric equivalent of Mann Whitney test

3. When do we use Mann Whitney test and what are the conditions

4. How is the P value calculated in Mann Whitney test

5. What do you mean by normal approximation method

6. How do you calculate Z value in Mann Whitney test

7. How to frame null and alternate hypothesis for Mann Whitney test

After completing this module you will clearly understand

1. What do you mean by Moods Median  test

2. What is the parametric equivalent of Moods Median Test

3. Conditions to satisfy before using Moods Median test

4. What is the underlying distribution of Moods Median test

5. How do you calculate the P value for moods median test

6. What is the difference between Moods Median test and Kruskal Wallis test

After completing this module you will clearly understand

1. What do you mean by Kruskal Wallis Statistics

2. What is the parametric equivalent of Kruskal Wallis Statistics

3. Conditions to satisfy before using Kruskal Wallis Statistics

4. What is the underlying distribution of Kruskal Wallis Statistics

5. How do you calculate the P value for Kruskal Wallis Statistics

6. What is the difference between Moods Median test and Kruskal Wallis test

After completing this module you will clearly understand

1. What do you mean by Simple linear regression (SLR)

2. What is the equation straight line

3. What do you mean by slop and intercept

4. What are the pre conditions to be satisfied for running SLR

5. What do you mean by Error

6. What do you mean by sum of squares

7. What do you mean by normal equations

8. How do you derive normal equations using partial derivatives

9. Generalized equation for regression modelling : y = b0+b1x

10. How do you deduce an expression for b0 intercept and b1 slope

11. What are the post conditions to be met with respect to error /residuals to use the model.

After completing this module you will clearly understand

1. What do you mean by Scatter plot

2. What do you mean by Coefficient of correlation

3. What do you mean by Coefficient of determination R square

4. What do you mean by R square Adjusted

5. What can you infer from a scatter plot and limitations of scatter plots

6. When can you confirm whether 2 variables have linear relationship

After completing this module you will clearly understand

1. How to run SLR in Minitab

2. Interpret the results from Minitab

3. How to perform SLR manually

4. What do you mean  SSE , SSR , SST

5. How to calculate R-square manually and how Minitab calculates it

6. What do you mean by Standard error

7. How do calculate the standard error of slope and intercept

8. ANOVA table and F-test in SLR

9. How to calculate F statistics

10. How to calculate Adj Mean  Squares aka Variance

11. T test in Regression and how to calculate T statistics

12. How many hypothesis test are conducted in SLR

13. How to validate Pre conditions and post model conditions in SLR

14. How to check if errors are independent and contain 0 in the range of Mean

    
    

After completing this module you will clearly understand

1. What do you mean by a Parsimonious model

2. What do you mean by over fitting of regression model

3. How can we use R square adjusted to avoid over fitting

4. How to calculate  R square adjusted manually

5. What do you mean by Variance Inflation factor (VIF)

6. What do you mean by Multicollinearity

7. Risks associated with Multicollinearity

8. How to tackle Multicollinearity  through VIF

9. Steps taken to avoid  Multicollinearity

10. What do you mean by Stepwise regression

11. What do you mean by Forward elimination technique

12. What do you mean by Backward elimination technique

13. What do you mean by best subset regression

14. Why do we prefer stepwise regression

After completing this module you will clearly understand

1. How to run MLR in Minitab

2. How to interpret results from Minitab

3. How to run stepwise regression

4. How to run best subset regression

5. What is Mallow Cp value

6. How to decide on which model to select from best subset regression using Mallow Cp value and S

7. How to compare multiple models in Minitab and make the final selection of  regression model

8. How to run the residual analysis

9. How to interpret residual analysis results in Minitab

10. What do you mean by unusual observation

11. How to identify unusual observation or outliers in the Minitab error graph

12. Run graphical summary and validate whether the mean confidence interval of residuals contains 0

13. What is your final regression equation 

14. What to do when the residual analysis is not satisfactory especially when the errors are not random

15. How to preform Box Cox transformation in regression for your response variable aka Y

After completing this module you will clearly understand

1. What do you mean by process control

2. What do you mean by statistical process control

3. What is a control chart and uses of control charts

4. Statistic used in control charts

5. What do you mean by control limits

6. What do you mean by upper control limit and lower control limit

7. How to determine whether your process is under control or not using control charts

8. What do you mean by lagging indicators and leading indicators

9. What is the difference between special cause and common cause

10. How to identify special cause and common cause from the control charts

11. Why is it recommended to set the control limits at 3 sigma difference from the mean

12. Alpha error , type 1 error and beta error type 2 error in control charts and how to handle it

13. 8 western electric rules to determine special cause

After completing this module you will clearly understand

1. What are the different types of control charts used for continuous data

2. When should you use IMR chart

3. When should you use X bar R chart

4. When should you use X bas S chart

5. How are the control limits calculated by Minitab

6. How to interpret the control charts

7. Why is it important to check the R chart first before looking at the sample mean in X bar R chart

8. What should you do if your process is out of control 

After completing this module you will clearly understand

1. What are the different types of control charts used for Attribute data

2. When should you use NP chart

3. When should you use P chart

4. When should you use U  chart

5. When should you use C chart

6. How to manually caudate control limits for attribute control charts

7. How to interpret the control charts

8. What should you do if your process is out of control