e-Statistics
e-Statistics
For Students
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Data in Statistical Studies
Textbook Data Sets
Worksheet Data Sets
Data and Story Library
Exploratory Graphics
Stem and Leaf Plot
Histogram
Boxplot
Scatter Plot
QQ plot
Bar Graph
Descriptive Statistics
Summary Statistics
Frequency Table
Grouped Data
Transformed Data
Normal Distribution
Normal Density Function
Z-score
Critical Value
Central Limit Theorem
Discrete Distributions
Binomial Distribution
Frequency Function
Normal Approximation
Inference on Mean
t-Distribution
Confidence Interval
Hypothesis Test
Power of Test
One-sided CI
Comparison of Two Groups
Test for Independent Groups
Nonparametric Test
Test for Paired Data
Simple Linear Regression
Regression Line
R Square
Residual Plot
Inference on Parameters
Predicting Responses
One-Way ANOVA Model
F-Distribution
Analysis of Variance
Strip Chart
Multiple Comparisons
Multiple Linear Regression
Model Estimate and Residuals
Model Selection and F-test
Two-Way Additive Model
Test of Effect
Analysis of Variance
Inference on Variances
Testing a Standard Deviation
Test for Two Groups
Test for Homogeneity
Inference on Proportions
Testing a Proportion
Comparison of Two Proportions
Chi-square Test
Chi-square Distribution
Goodness of Fit
Test for Homogeneity
Logistic Regression
Inference on parameters
Adjusted log OR

Chi-square Test

In experiments on pea breeding, Mendel observed the different kinds of seeds obtained by crosses between round yellow seeds and wrinkled green seeds. Possible types of progeny were: “round yellow”, “wrinkled yellow”, “round green”, and “wrinkled green.” We call the data categorical when the recorded data

$\displaystyle C_1,\ldots,C_n $

take values on several types, or categories. Then the frequency of a particular value (or type) is observed in the categorical data, and the probability of such occurrence is called the population proportion.

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