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

Strip Chart

The data set consists of

  1. provides factor levels which identifies group $ i$.
  2. contains measurement values $ X_{ij}$'s.
Strip chart shows the individual observations (as dots) for each level overlaid with group mean values (solid line segments) and the overall mean value (dashed horizontal line).

The assumption for an analysis of variance (AOV) test is described as follows: The random variable $\varepsilon_{ij} = X_{ij} - \alpha_i$ is called a residual, and it is assumed that all $ \varepsilon_{ij}$'s are independent and approximately normally distributed with mean 0 and common variance $ \sigma^2$. When the nonnormality cannot be eliminated by the use of transformation, the Kruskal-Wallis test is appropriate for the hypothesis testing. Here the null hypothesis $ H_0$ is that k population distributions (not necessarily normal) are identical. It calculates the test statistic and the p-value . By rejecting $ H_0$ we can find some evidence supporting that not all the distributions are the same.

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