• Skip to secondary menu
  • Skip to main content
  • Skip to primary sidebar
  • My Store
  • Glossary
  • Home
  • About Me
  • Contact Me

Statistics By Jim

Making statistics intuitive

  • Graphs
  • Basics
  • Hypothesis Testing
  • Regression
  • ANOVA
  • Probability
  • Time Series
  • Fun

Parameter

By Jim Frost

Parameters are the unknown values of an entire population, such as the mean and standard deviation. Samples can estimate population parameters but their exact values are usually unknowable.

Parameters are also the constant values that appear in probability functions. These parameters define the shape of probability distributions. Parameters are typically denoted using Greek symbols to distinguish them from sample statistics.

For example, the parameters of the normal distribution are μ ( mu = population mean) and σ (sigma = population standard deviation).

Related

Related Articles:
  • How to Interpret P-values and Coefficients in Regression Analysis
  • How Hypothesis Tests Work: Significance Levels (Alpha) and P values
  • Curve Fitting using Linear and Nonlinear Regression
  • How to Identify the Distribution of Your Data
  • Overfitting Regression Models: Problems, Detection, and Avoidance

Primary Sidebar

Meet Jim

I’ll help you intuitively understand statistics by focusing on concepts and using plain English so you can concentrate on understanding your results.

Read More...

Buy My Introduction to Statistics eBook!

New! Buy My Hypothesis Testing eBook!

Buy My Regression eBook!

Subscribe by Email

Enter your email address to receive notifications of new posts by email.

    I won't send you spam. Unsubscribe at any time.

    Follow Me

    • FacebookFacebook
    • RSS FeedRSS Feed
    • TwitterTwitter
    • Popular
    • Latest
    Popular
    • How To Interpret R-squared in Regression Analysis
    • How to Interpret P-values and Coefficients in Regression Analysis
    • Measures of Central Tendency: Mean, Median, and Mode
    • Normal Distribution in Statistics
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
    • How to Interpret the F-test of Overall Significance in Regression Analysis
    • Understanding Interaction Effects in Statistics
    Latest
    • How to Find the P value: Process and Calculations
    • Sampling Methods: Different Types in Research
    • Beta Distribution: Uses, Parameters & Examples
    • Geometric Distribution: Uses, Calculator & Formula
    • What is Power in Statistics?
    • Conditional Distribution: Definition & Finding
    • Marginal Distribution: Definition & Finding

    Recent Comments

    • Hannah on How to Interpret Adjusted R-Squared and Predicted R-Squared in Regression Analysis
    • James on Introduction to Bootstrapping in Statistics with an Example
    • Jim Frost on Introduction to Bootstrapping in Statistics with an Example
    • Jim Frost on How To Interpret R-squared in Regression Analysis
    • Jim Frost on Comparing Regression Lines with Hypothesis Tests

    Copyright © 2022 · Jim Frost · Privacy Policy