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  1. Introduction
    1. What Is Statistics?
    2. Subjects in Modern Statistics
    3. Why Should I Learn Statistics? 0% developed
    4. What Do I Need to Know to Learn Statistics?
  2. Different Types of Data
    1. Primary and Secondary Data
    2. Quantitative and Qualitative Data
  3. Methods of Data Collection
    1. Experiments
    2. Sample Surveys
    3. Observational Studies
  4. Data Analysis
    1. Data Cleaning
    2. Moving Average
  5. Summary Statistics
    1. Measures of center
      1. Mean, Median, and Mode
      2. Geometric Mean
      3. Harmonic Mean
      4. Relationships among Arithmetic, Geometric, and Harmonic Mean
      5. Geometric Median
    2. Measures of dispersion
      1. Range of the Data
      2. Variance and Standard Deviation
      3. Quartiles and Quartile Range
      4. Quantiles
  6. Displaying Data
    1. Bar Charts
    2. Comparative Bar Charts
    3. Histograms
    4. Scatter Plots
    5. Box Plots
    6. Pie Charts
    7. Comparative Pie Charts
    8. Pictograms
    9. Line Graphs
    10. Frequency Polygon
  7. Probability
    1. Combinatorics
    2. Bernoulli Trials
    3. Introductory Bayesian Analysis
  8. Distributions
    1. Discrete Distributions
      1. Uniform Distribution
      2. Bernoulli Distribution
      3. Binomial Distribution
      4. Poisson Distribution
      5. Geometric Distribution
      6. Negative Binomial Distribution
      7. Hypergeometric Distribution
    2. Continuous Distributions
      1. Uniform Distribution
      2. Exponential Distribution
      3. Gamma Distribution
      4. Normal Distribution
      5. Chi-Square Distribution
      6. Student-t Distribution
      7. F Distribution
      8. Beta Distribution
      9. Weibull Distribution
  9. Testing Statistical Hypothesis
    1. Purpose of Statistical Tests
    2. Formalism Used
    3. Different Types of Tests
    4. z Test for a Single Mean
    5. z Test for Two Means
    6. t Test for a single mean
    7. t Test for Two Means
    8. paired t Test for comparing Means
    9. One-Way ANOVA F Test
    10. z Test for a Single Proportion
    11. z Test for Two Proportions
    12. Testing whether Proportion A Is Greater than Proportion B in Microsoft Excel
    13. Spearman's Rank Coefficient
    14. Pearson's Product Moment Correlation Coefficient
    15. Chi-Squared Tests
      1. Chi-Squared Test for Multiple Proportions
      2. Chi-Squared Test for Contingency
    16. Approximations of distributions
  10. Point Estimates100% developed  as of 12:07, 28 March 2007 (UTC) (12:07, 28 March 2007 (UTC))
    1. Unbiasedness
    2. Measures of goodness
    3. UMVUE
    4. Completeness
    5. Sufficiency and Minimal Sufficiency
    6. Ancillarity
  11. Practice Problems
    1. Summary Statistics Problems
    2. Data-Display Problems
    3. Distributions Problems
    4. Data-Testing Problems
  12. Numerical Methods
    1. Basic Linear Algebra and Gram-Schmidt Orthogonalization
    2. Unconstrained Optimization
    3. Quantile Regression
    4. Numerical Comparison of Statistical Software
    5. Numerics in Excel
    6. Statistics/Numerical_Methods/Random Number Generation
  13. Multivariate Data Analysis
    1. Principal Component Analysis
    2. Factor Analysis for metrical data
    3. Factor Analysis for ordinal data
    4. Canonical Correlation Analysis
    5. Discriminant Analysis
  14. Analysis of Specific Datasets
    1. Analysis of Tuberculosis
  15. Appendix
    1. Authors
    2. Glossary
    3. Index
    4. Links

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Combinatorics studies permutations and combinations of objects chosen from a sample space. A preliminary knowledge of combinatorics is necessary for a good command of statistics.

Counting Principle[edit]

The Counting Principle is similar to the Multiplicative Principle. If a process involves steps and the th step can be done in ways, then the entire process can be completed in different ways.


A permutation is a distinct arrangement of elements of a set. By the Counting Principle, the number of possible arrangements of objects in a set is . What if some of the elements are not distinct? Then, if there are distinct kinds of elements, the total number of possible arrangements is . What if we are arranging the elements in a circle, rather than a line? Then the number of permutations is .

When faced with very large factorials, a useful approximation is Stirling's formula:

Now suppose we only choose r distinct elements from the set (without replacement). Then the number of possible permutations becomes .


A combination is essentially a subset. It is like a permutation, except with no regard to order. Suppose we have a set of elements and take elements. The number of possible combinations is .

Note also that

Combinations are found in binomial expansion. Consider the following binomial expansions:

As you may have noticed from the above, for any positive integer ,

Another observation from the above is known as Pascal's law. It states that

This allows us to construct Pascal's triangle, which is useful for determining combinations:

Pascal's triangle 5.svg