There are primarily two branches in which statistics are studied:
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.
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 analyze the entire population (such as the average weight of all sharks in the ocean). For this reason a sampling strategy is applied. 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 so they are applied with a confidence interval.
A comparison (of means, variance, proportions) is initiated with a hypothesis statement about a population or populations. The sample statistics are studied 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.
The links below cover other topics within basic statistics:
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
Yield to Sigma Relationships
Process Capability Indices
A few other continuous distributions (but are not covered in this website) are Beta, Cauchy, Gamma, Lognormal, Weibull, Double Exponential, Power Normal, Bivariate Normal, Power Log Normal, Triangular, and Tukey-Lambda.
Six Sigma Certification
Six Sigma Modules
Green Belt Program (1,000+ Slides)
Cause & Effect Matrix
Central Limit Theorem
1-Way Anova Test
Correlation and Regression