This project helps to make teaching and learning of applied statistics, design of experiments and chemometrics a bit easier by providing interactive web-applications (apps). The apps allow you to play with most important concepts of the subjects and thus get a better understanding of the concepts. To run an app just click on Try button. Each app also has a short description/help and a short supplementary video (the full list of the video guides is available on YouTube). You can download any app and run it locally or incorporate to your own website. All technical information and source code available on GitHub repo of the project.
Descriptive statistics and plots
- asta-b101
Quantiles, quartiles, percentiles
How to compute simple statistics for a sample.
- asta-b102
Samples and populations
How a sample taken from a population looks like.
- asta-b103
PDF, CDF and ICDF
Main functions for theoretical distributions.
- asta-b104
Quantile-quantile plot
How to create and interpret a QQ-plot.
Confidence intervals
- asta-b201
Population based CI for proportion
Confidence interval for proportion, based on population parameter.
- asta-b202
Sample based CI for proportion
Confidence interval for proportion, based on sample statistic.
- asta-b203
Population based CI for mean
Confidence interval for mean, based on population parameter.
- asta-b204
Sample based CI for mean
Confidence interval for mean, based on sample statistics.
Hypothesis testing
- asta-b205
What is p-value?
Explanation of p-value using coin experiment.
- asta-b206
Test for sample proportion
How test for proportion works.
- asta-b207
One sample t-test
Test for mean of one sample.
- asta-b208
Power of test and Type II error
How often you will be able to reject wrong H0.
Comparing means
- asta-b209
Two sample t-test
How to compare mean of two samples.
- asta-b210
Multiple comparison and Bonferroni correction
What if we apply t-test to more than 2 groups.
- asta-b211
One-way ANOVA (simplified)
How Analysis of Variance works for one factor.
- asta-b212
One-way ANOVA (full)
A more detailed app.
Covariance and regression
- asta-b301
Covariance
How to compute and understand the covariance.
- asta-b302
Correlation and population based CI
Pearson's correlation coefficient and population based CI.
- asta-b303
Correlation and sample based CI
Pearson's correlation coefficient and sample based CI.
- asta-b304
Simple linear regression
SLR and its main outcomes.
- asta-b305
Sampling error and overfitting
How sampling error depends on sample size and model complexity.
- asta-b306
Cross-validation
Cross-validation, model performance and overfitting.
- asta-b307
Jackknifing
Jackknife resampling for regression coefficients.
- asta-b308
Multiple Linear Regression
Multiple Linear Regression.
- asta-b309
MLR and colinearity
MLR and colinearity.
Principal component analysis
- mda-b201
PCA: distances and variances
How PCA fits the data.
- mda-b202
PCA NIPALS algorithm
How to find orientation of PCs.
- mda-b203
Elements of PCA model
Visual representation of main PCA outcomes.
- mda-b204
PCA: 3D example
PCA outcomes for dataset with three variables.