What Does the Green Square in the Middle of the Interval Represent Art of Stat

Exploratory Data Analysis

Explore Categorical Information

Explore Quantitative Information

Construct frequency and contingency tables and bar graphs to explore distributions of categorical variables.

For one or two variables.

Explore Quantitative Data

Find summary statistics and construct interactive histograms, boxplots, dotplots or stem & leafage plots.

For ane, two, or more groups.

Time Series Plots

Explore Quantitative Data

Time Series Plots

Plot a simple time series and add a shine or linear trend. Use preloaded data or provide your own.

Mean vs. Median

Time Series Plots

Explore the human relationship between the mean and median for data coming from a variety of distributions, or enter your ain information

Random Numbers

Mean vs. Median

Random Numbers

Generate random numbers or flips of a (biased) coin. Continue track of generated numbers with a bar chart.

Association, Correlation & Regression

Association Between 2 Categorical Variables (2x2 tables)

Association Betwixt Two Categorical Variables (2x2 tables)

Association Between Two Categorical Variables (2x2 tables)

Construct ii x ii contingency tables, obtain conditional proportions and become a bar graph. Find the difference or ratio of proportions to describe the strength of the association. Congenital the sampling distribution of the difference or ratio via resampling.

Scatterplots & Correlation

Association Between Two Categorical Variables (2x2 tables)

Clan Between Two Chiselled Variables (2x2 tables)

Construct interactive scatterplots, hover over points, motility them around (or remove them) and overlay a smooth trend line. Find the correlation coefficient r and see if it is robust to outliers. Built the sampling distribution of r via resampling.

Estimate the Correlation

Association Between Two Chiselled Variables (2x2 tables)

Explore Linear Regression

Randomly generate scatterplots to judge the correlation coefficient r. Optionally, display the regression line. How do your guesses correlate with the bodily values?

Explore Linear Regression

Create scatterplots from scratch past clicking in an empty plot to add or remove points. Investigate the upshot of outliers on the correlation coefficient or regression line. Simulate linear or not-linear relationships.

Linear Regression

Explore Linear Regression

Multivariate Relationships

Fit a simple linear regression model and obtain the regression equation and related statistics such as r-squared. Make predictions and construct conviction intervals. Display and clarify residuals.

Multivariate Relationships

Explore Linear Regression

Multivariate Relationships

Construct interactive scatterplots to explore the relationship betwixt two quantitative variables, while accounting for a third (categorical or quantitative) grouping variable. Multiple Linear Regression.

Multiple Linear Regression

Under Construction. In the meantime, use the Multivariate Relationships app, which has some capablities to fit a multiple linear regression model with two explanatory variables.

Exponential Regression

Multiple Linear Regression

Under Structure. Fit and visualize a uncomplicated exponential regression model to data such as the number of COVID-19 infections in New York City in March 2020 (Example 16, Chapter 13).

Logistic Regression

Multiple Linear Regression

Logistic Regression

Nether Construction. Fit a logistic regression model with a single quantitative predictor. Obtain parameter estimates, a graph of the fitted probabilities and construct confidence intervals.

Distributions: Explore Shapes & Discover Probabilities

Binomial Distribution

Explore how the shape of the Binomial distribution depends on the parameter n (the sample size) and p (the probability of success in a Bernoulli trial). Find and visualize probabilities of diverse kinds.

Normal Distribution

Binomial Distribution

Meet how the shape of the normal distribution depends on the hateful and standard deviation. Find and visualize i- and two-tailed probabilities and percentiles (critical values).

t Distribution

Binomial Distribution

Chi-Squared Distribution

See how the shape of the t distribution depends on the degrees of freedom. Detect and visualize ane-and two-tailed probabilities and percentiles (disquisitional values).

Chi-Squared Distribution

Run into how the shape of the Chi-Squared distribution depends on the degrees of freedom. Find and visualize probabilities and percentiles (critical values).

F Distribution

Chi-Squared Distribution

Poisson Distribution

See how the shape of the F distribution depends on the degrees of liberty. Find and visualize probabilities and percentiles (critical values).

Poisson Distribution

Chi-Squared Distribution

Poisson Distribution

Explore how the shape of the Poisson distribution depends on the parameter λ (the mean). Observe and visualize probabilities of various kinds.

Sampling Distributions and the Central Limit Theorem

Sampling Distribution of the Sample Proportion

Sampling Distribution of the Sample Mean (Continuous Population)

Experience how the sampling distribution of the sample proportion builds upwardly one sample at a time. Use sliders to explore the shape of the sampling distribution as the sample size north increases, or as the population proportion p changes. Overlay a normal distribution to explore the Fundamental Limit Theorem.

Sampling Distribution of the Sample Mean (Continuous Population)

Sampling Distribution of the Sample Hateful (Continuous Population)

Sampling Distribution of the Sample Mean (Continuous Population)

Experience how the sampling distribution of the sample mean builds up 1 sample at a time. Use a variety of real or theoretical continuous population distributions (or create your own) to draw samples from. Move sliders to explore when the Cardinal Limit Theorem kicks in.

Sampling Distribution of the Sample Mean (Discrete Population)

Sampling Distribution of the Sample Mean (Continuous Population)

Sampling Distribution of the Sample Hateful (Discrete Population)

Feel how the sampling distribution of the sample mean builds up i sample at a time. Use a variety of real or theoretical discrete population distributions (or create your own) to draw samples from. Motility sliders to explore when the Central Limit Theorem kicks in.

Confidence Intervals & Significance Tests (One Sample)

Inference for a Proportion

Find conviction intervals or exam hypotheses nearly a population proportion. Obtain the margin of error or the z-test statistic and visualize the interval or the P-value on a graph.

Inference for a Mean

Inference for a Proportion

Detect confidence intervals or test hypotheses almost a population mean. Enter your own data or summary statistics. Use plots to check assumptions and visualize the interval or the P-value on a graph.

Bootstrap for Ane Sample

Inference for a Proportion

Bootstrap for One Sample

Create and visualize, footstep-past-stride, the bootstrap distribution of the mean, median or standard departure. Obtain summary statistics and find a percentile confidence interval.

Explore Coverage

Bootstrap for Ane Sample

What does "95% confidence" hateful? What affects the width of an interval? Explore these concepts for conviction intervals of proportions or means, using sliders to change parameters or the sample size.

Errors and Power

Explore Coverage

Errors and Power

Visualize and explore relationships betwixt Type I and Blazon II errors and the power of a test for proportions or means. See how they depend on sample size and the true values of population parameters.

Confidence Intervals & Tests Comparing Two Groups

Compare 2 Proportions

Bootstrap for Two Samples

Compare 2 Proportions

Confidence intervals or hypotheses tests well-nigh the difference of 2 population proportions. Obtain the margin of error or the z-test statistic and visualize the interval or the P-value on a graph.

For ii independent or dependent samples.

Compare Ii Means

Bootstrap for Ii Samples

Compare Two Proportions

Confidence intervals or hypotheses tests about the difference of ii population ways. Enter your own data or summary statistics. Visualize the interval or the P-value on a graph.

For two independent or dependent samples.

Bootstrap for Two Samples

Bootstrap for 2 Samples

Create and visualize, step-by-step, the bootstrap distribution for the divergence of means or medians . Obtain summary statistics (such as percentiles) of the bootstrap distribution and observe a percentile conviction interval.

Permutation Test

Difference & Ratio of Proportions

Bootstrap for Two Samples

Create and visualize, step-past-stride, the permutation distribution for statistics such equally the difference of means or medians or the t-statistic. Obtain summary statistics (such every bit percentiles) and find the permutation P-value.

Difference & Ratio of Proportions

Discover the bootstrap or permutation distribution for the difference or ratio of ii proportions, or for the odds-ratio. Utilize these to obtain percentile confidence intervals or permutation P-values for testing whether there is no association.

Fisher's Verbal Test

Difference & Ratio of Proportions

Visualize and run Fisher'south exact examination for

2 x 2 contingency tables. Obtain the exact P-value for one- or ii-sided tests.

Inference for Comparison Several Groups

Chi-Squared Test

Kruskal-Wallis Test

Chi-Squared Test

Test for independence, homogeneity or goodness of fit in contingency tables. Enter your own data as raw observations or as a contingency table. Obtain observed and expected counts and find residuals.

ANOVA (One-Way)

Kruskal-Wallis Exam

Chi-Squared Exam

Obtain the ANOVA table, F-statistic and side-by-side boxplots to check assumptions. Behave out pairwise comparisons, including simultaneous confidence intervals for pairwise differences of means.

Kruskal-Wallis Test

Kruskal-Wallis Examination

Permutation Test for Independence

Under Structure

Permutation Test for Independence

Visualize and run a permutation examination for testing independence in a contingency table. Obtain the permutation P-value of the Pearson Chi-squared statistic without relying on asymptotical results.

Permutation Test for Independence

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Source: https://artofstat.com/web-apps