e-Statistics
e-Statistics
For Students
For Contributors
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

QQ plot

If the size n of sample data is small (n < 30), the t-test procedure requires that the data be approximately normally distributed; otherwise, the result of t-test may not be reliable. The quantile-quantile normal plot (QQ normal plot) is one of the graphical methods to assess the fit of the data to a normal distribution.

How to read the QQ normal plot. The values at the horizontal axis are from the sample data, and observed as sample quantile. The values at the vertical axis are given to the corresponding quantiles from the standard normal distribution. For example, the values between -1.0 and 1.0 at the vertical axis consist of approximately 68% of the entire values, which corresponds to the sample data between $ \mu-\sigma$ and $ \mu+\sigma$ (at the horizontal axis) if the empirical rule applies. Thus, the straightness of the QQ normal plot indicates the normal distribution as a reasonable fit.

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