Statistics in R
- Average, Variance and Standard Deviation in R
- Mean, Median and Mode in R Programming
- Probability in R
- Discrete distributions
- Benford Distribution
- Bernoulli
- Binomial
- Hypergeometric distribution
- Geometric distribution
- Multinomial
- Negative binomial distribution
- Poisson distribution
- Zipf’s law
- Continuous distributions
- Beta distributions
- Dirichlet distributions
- Cauchy
- Chi-Square distribution
- Exponential
- Fisher-Snedecor
- Gamma
- Levy
- Log-normal distribution
- Normal and related distributions
- Pareto Distributions
- Student’s t distribution
- Uniform distribution
- Weibull
- Calculate Conditional Probability
- Binomial Distribution
- Normal Distribution in R
- Beta Distribution in R
- Discrete distributions
- Hypothesis in R
- Types of Hypothesis
- Null Hypothesis
- Alternative Hypothesis
- Decision Errors in R
- Type I Error
- Type II Error
- Confidence Intervals
- Correlation and Covariance
- Covariance Matrix
- Pearson Correlation
- Normal Probability Plot
- Quantile Quantile plots
- Residuals Leverage Plot
- Spearman’s Rank Correlation Measure
- Kendall Rank Correlation Measure
- Evaluation Metrics – Accuracy, Precision, Recall, F1-Score, MAE, MSE
- Root-Mean-Square Error
- ROC and AUC curve
R – Statistics
Statistics is a form of mathematical analysis that concerns the collection, organization, analysis, interpretation, and presentation of data. Statistical analysis helps to make the best use of the vast data available and improves the efficiency of solutions.
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