• 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

analysis example

One Way ANOVA Overview & Example

By Jim Frost Leave a Comment

What is One Way ANOVA?

Use one way ANOVA to compare the means of three or more groups. This analysis is an inferential hypothesis test that uses samples to draw conclusions about populations. Specifically, it tells you whether your sample provides sufficient evidence to conclude that the groups’ population means are different. ANOVA stands for analysis of variance. [Read more…] about One Way ANOVA Overview & Example

Filed Under: ANOVA Tagged With: analysis example, assumptions, interpreting results

One Sample T Test: Definition, Using & Example

By Jim Frost Leave a Comment

What is a One Sample T Test?

Use a one sample t test to evaluate a population mean using a single sample. Usually, you conduct this hypothesis test to determine whether a population mean differs from a hypothesized value you specify. The hypothesized value can be theoretically important in the study area, a reference value, or a target. [Read more…] about One Sample T Test: Definition, Using & Example

Filed Under: Hypothesis Testing Tagged With: analysis example, assumptions, choosing analysis, interpreting results

What is a Parsimonious Model? Benefits and Selecting

By Jim Frost Leave a Comment

What is a Parsimonious Model?

A parsimonious model in statistics is one that uses relatively few independent variables to obtain a good fit to the data. [Read more…] about What is a Parsimonious Model? Benefits and Selecting

Filed Under: Regression Tagged With: analysis example, conceptual, interpreting results

T Test Overview: How to Use & Examples

By Jim Frost 6 Comments

What is a T Test?

A t test is a statistical hypothesis test that assesses sample means to draw conclusions about population means. Frequently, analysts use a t test to determine whether the population means for two groups are different. For example, it can determine whether the difference between the treatment and control group means is statistically significant. [Read more…] about T Test Overview: How to Use & Examples

Filed Under: Hypothesis Testing Tagged With: analysis example, assumptions, choosing analysis, interpreting results

Correlation Coefficient Formula Walkthrough

By Jim Frost 1 Comment

Pearson’s correlation coefficient formula produces a number ranging from -1 to +1, quantifying the strength and direction of a relationship between two continuous variables. A correlation of -1 means a perfect negative relationship, +1 represents a perfect positive relationship, and 0 indicates no relationship. [Read more…] about Correlation Coefficient Formula Walkthrough

Filed Under: Basics Tagged With: analysis example

Two-Way Table Explained

By Jim Frost Leave a Comment

What is a Two-Way Table?

A two-way table displays frequencies for combinations of two categorical variables. Columns correspond to the values of one variable, while the rows relate to the other. The intersection of each row and column displays a frequency or relative frequency of observations having a pair of categorical attributes. Statisticians also refer to them as contingency tables. [Read more…] about Two-Way Table Explained

Filed Under: Basics Tagged With: analysis example, interpreting results

Kruskal Wallis Test Explained

By Jim Frost Leave a Comment

What is the Kruskal Wallis Test?

The Kruskal Wallis test is a nonparametric hypothesis test that compares three or more independent groups. Statisticians also refer to it as one-way ANOVA on ranks. This analysis extends the Mann Whitney U nonparametric test that can compare only two groups. [Read more…] about Kruskal Wallis Test Explained

Filed Under: Hypothesis Testing Tagged With: analysis example, assumptions, choosing analysis, distributions, interpreting results, nonparametric

Mann Whitney U Test Explained

By Jim Frost 8 Comments

What is the Mann Whitney U Test?

The Mann Whitney U test is a nonparametric hypothesis test that compares two independent groups. Statisticians also refer to it as the Wilcoxon rank sum test. The Kruskal Wallis test extends this analysis so that can compare more than two groups. [Read more…] about Mann Whitney U Test Explained

Filed Under: Hypothesis Testing Tagged With: analysis example, assumptions, choosing analysis, distributions, interpreting results, nonparametric

Covariance: Definition, Formula & Example

By Jim Frost Leave a Comment

What is Covariance?

Covariance in statistics measures the extent to which two variables vary linearly. It reveals whether two variables move in the same or opposite directions. [Read more…] about Covariance: Definition, Formula & Example

Filed Under: Basics Tagged With: analysis example, conceptual, interpreting results

Range Rule of Thumb: Overview and Formula

By Jim Frost 4 Comments

What is the Range Rule of Thumb?

The range rule of thumb allows you to estimate the standard deviation of a dataset quickly. This process is not as accurate as the actual calculation for the standard deviation, but it’s so simple you can do it in your head. [Read more…] about Range Rule of Thumb: Overview and Formula

Filed Under: Basics Tagged With: analysis example, distributions

Joint Probability: Definition, Formula & Examples

By Jim Frost 4 Comments

What is Joint Probability?

Joint probability is the likelihood that two or more events will coincide. Knowing how to calculate them allows you to solve problems such as the following. What is the probability of:

  • Getting two heads in two coin tosses?
  • Consecutively drawing two aces from a deck of cards?
  • The next customer being a woman who buys a Mac computer?
  • A bike rental customer getting both a flat front tire and a flat rear tire?

[Read more…] about Joint Probability: Definition, Formula & Examples

Filed Under: Probability Tagged With: analysis example, choosing analysis, conceptual

Independent Events: Definition & Probability

By Jim Frost Leave a Comment

What are Independent Events?

Independent events in statistics are those in which one event does not affect the next event. More specifically, the occurrence of one event does not affect the probability of the following event happening. [Read more…] about Independent Events: Definition & Probability

Filed Under: Probability Tagged With: analysis example, conceptual

Random Variable: Discrete & Continuous

By Jim Frost Leave a Comment

What is a Random Variable?

A random variable is a variable where chance determines its value. They can take on either discrete or continuous values, and understanding the properties of each type is essential in many statistical applications. Random variables are a key concept in statistics and probability theory. [Read more…] about Random Variable: Discrete & Continuous

Filed Under: Probability Tagged With: analysis example, conceptual, distributions, graphs

Least Squares Regression: Definition, Formulas & Example

By Jim Frost 2 Comments

A least squares regression line represents the relationship between variables in a scatterplot. The procedure fits the line to the data points in a way that minimizes the sum of the squared vertical distances between the line and the points. It is also known as a line of best fit or a trend line. [Read more…] about Least Squares Regression: Definition, Formulas & Example

Filed Under: Regression Tagged With: analysis example, graphs, interpreting results

ANCOVA: Uses, Assumptions & Example

By Jim Frost 1 Comment

What is ANCOVA?

ANCOVA, or the analysis of covariance, is a powerful statistical method that analyzes the differences between three or more group means while controlling for the effects of at least one continuous covariate. [Read more…] about ANCOVA: Uses, Assumptions & Example

Filed Under: ANOVA Tagged With: analysis example, assumptions, choosing analysis, interpreting results

Cumulative Distribution Function (CDF): Uses, Graphs & vs PDF

By Jim Frost Leave a Comment

What is a Cumulative Distribution Function?

A cumulative distribution function (CDF) describes the probabilities of a random variable having values less than or equal to x. It is a cumulative function because it sums the total likelihood up to that point. Its output always ranges between 0 and 1. [Read more…] about Cumulative Distribution Function (CDF): Uses, Graphs & vs PDF

Filed Under: Probability Tagged With: analysis example, conceptual, distributions, graphs, interpreting results

Slope Intercept Form of Linear Equations: A Guide

By Jim Frost Leave a Comment

What is Slope Intercept Form?

The slope intercept form of linear equations is an algebraic representation of straight lines: y = mx + b. [Read more…] about Slope Intercept Form of Linear Equations: A Guide

Filed Under: Basics Tagged With: analysis example, graphs, interpreting results

Monte Carlo Simulation: Make Better Decisions

By Jim Frost 4 Comments

What is Monte Carlo Simulation?

Monte Carlo simulation uses random sampling to produce simulated outcomes of a process or system. This method uses random sampling to generate simulated input data and enters them into a mathematical model that describes the system. The simulation produces a distribution of outcomes that analysts can use to derive probabilities. [Read more…] about Monte Carlo Simulation: Make Better Decisions

Filed Under: Probability Tagged With: analysis example, distributions, Excel, interpreting results

Principal Component Analysis Guide & Example

By Jim Frost Leave a Comment

What is Principal Component Analysis?

Principal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. These indices retain most of the information in the original set of variables. Analysts refer to these new values as principal components. [Read more…] about Principal Component Analysis Guide & Example

Filed Under: Basics Tagged With: analysis example, choosing analysis, conceptual, interpreting results, multivariate

Fishers Exact Test: Using & Interpreting

By Jim Frost 3 Comments

Fishers exact test determines whether a statistically significant association exists between two categorical variables.

For example, does a relationship exist between gender (Male/Female) and voting Yes or No on a referendum? [Read more…] about Fishers Exact Test: Using & Interpreting

Filed Under: Hypothesis Testing Tagged With: analysis example, choosing analysis

  • Go to page 1
  • Go to page 2
  • Go to page 3
  • Interim pages omitted …
  • Go to page 5
  • Go to Next Page »

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 Book!

Cover of my Introduction to Statistics: An Intuitive Guide ebook.

Buy My Hypothesis Testing Book!

Cover image of my Hypothesis Testing: An Intuitive Guide ebook.

Buy My Regression Book!

Cover for my ebook, Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models.

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.

    Top Posts

    • How to Interpret P-values and Coefficients in Regression Analysis
    • F-table
    • How To Interpret R-squared in Regression Analysis
    • Z-table
    • How to do t-Tests in Excel
    • How to Find the P value: Process and Calculations
    • Weighted Average: Formula & Calculation Examples
    • Cronbach’s Alpha: Definition, Calculations & Example
    • T-Distribution Table of Critical Values
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions

    Recent Posts

    • Longitudinal Study: Overview, Examples & Benefits
    • Correlation vs Causation: Understanding the Differences
    • One Way ANOVA Overview & Example
    • Observational Study vs Experiment with Examples
    • Goodness of Fit: Definition & Tests
    • Binomial Distribution Formula: Probability, Standard Deviation & Mean

    Recent Comments

    • Jim Frost on Joint Probability: Definition, Formula & Examples
    • Harmeet on Joint Probability: Definition, Formula & Examples
    • kafia on Cronbach’s Alpha: Definition, Calculations & Example
    • Jim Frost on How to Interpret P-values and Coefficients in Regression Analysis
    • Jim Frost on Convenience Sampling: Definition & Examples

    Copyright © 2023 · Jim Frost · Privacy Policy