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Basic Statistics







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A series of links below are listed to teach basic statistics used in Six Sigma projects. A Green Belt, Black Belt, or Master Black Belt should at a minimum be comfortable with these basic statistics. There are primarily two branches in which statistics are studied:

Descriptive Statistics

Applied to describe the data using numbers, charts, and graphs. Terms such as mean, median, mode, variance, standard deviation are values that summarize data. Descriptive statistics describe the entire group for which the numbers were obtained. These are the actual values for the entire group.

Inferential Statistics

Uses sample statistics to infer relationships of the population parameters.This is most often done in the Analyze and Improve phases using hypothesis testing, correlation analysis, regression analysis, and design of experiments (DOE). Rarely is it possible to be able to analyze the population (such as the average weight of all sharks in the ocean) so a sampling strategy is employed. The analysis of the sample is used to apply inferences to the entire population. The values may or may not be the same values for the entire population, but they are often used with a confidence interval applied.

Inferential Statistics



A comparison (of means, variance, proportions) is initiated with a hypothesis statement about a population or populations. The sample statistic(s) are studied and analyzed to determine with a certain level of confidence and power that the hypothesis (null hypothesis) is to be proven false or not false (but not necessarily true), see Hypothesis Testing for more information.




Histograms

Box Plots

Comparing Samples vs. Population

Data Classification

P-Value

Confidence Intervals

Correlation

Measures of Central Tendency: Mean, Median, Mode

Measures of Dispersion: Range, Standard Deviation, Variance

Alpha and Beta Risks (Type I Error and Type II Error)

Hypothesis Testing

Analysis of Variance (ANOVA)

DPU - Defects Per Unit

DPO - Defects Per Opportunity

DPMO - Defects Per Million Opportunities

Process Yield Metrics

FY - Final Yield

TPY - Throughput Yield

RTY - Rolled Throughput Yield

NY - Normalized Yield

The meaning and formula around sigma scores

Z-score

Yield to Sigma Relationships

Process Capability Indices

Pp

Ppk

Cp

Cpk

Cpm

DISCRETE DISTRIBUTIONS:

1) Binomial Distribution

2) Poisson Distribution

3) Hypergeometric Distribution

CONTINUOUS DISTRIBUTIONS:

1) Uniform Distribution

2) Normal Distribution

3) Exponential Distribution

4) t Distribution

5) Chi-square Distribution

6) F Distribution









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