One of the most common metrics used to measure the prediction accuracy of a model is MSE, which stands for mean squared error. It is calculated as:…

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The root mean square error (RMSE) is a metric that tells us how far apart our predicted values are from our observed values in a regression analysis,…

This tutorial explains how to work with the Poisson distribution in R using the following functions dpois: returns the value of the Poisson probability density…

A relative frequency histogram is a graph that displays the relative frequencies of values in a dataset. This tutorial explains how to create a relative frequency…

Residual plots are often used to assess whether or not the residuals in a regression analysis are normally distributed and whether or not they exhibit heteroscedasticity.…

Whenever you conduct a hypothesis test, you will get a test statistic as a result. To determine if the results of the hypothesis test are statistically…

One of the most common metrics used to measure the forecasting accuracy of a model is MAPE, which stands for mean absolute percentage error. The formula to…

A Breusch-Pagan Test is used to determine if heteroscedasticity is present in a regression analysis. This tutorial explains how to perform a Breusch-Pagan Test in R. Example: Breusch-Pagan Test…

McNemar’s Test is used to determine if there is a statistically significant difference in proportions between paired data. This tutorial explains how to perform McNemar’s Test…

A Mann-Kendall Trend Test is used to determine whether or not a trend exists in time series data. It is a non-parametric test, meaning there is no…