# Statistics

Statistical analysis solves complicated problems that create a big impact across the fields.

## Causal Conclusions and Control of Confounding Variables in Research

Causal Conclusions and Control of Confounding Variables, establishing a causal relationship between variables is crucial for drawing accurate conclusions. Randomization plays a vital role in achieving this goal by controlling confounding variables that can distort the relationship between independent and dependent variables. This article discusses the importance of causal conclusions, randomization, random sampling, and the […]

## Role of Confounding Variables in Research

Role of Confounding Variables in Research, confounding variables pose a significant challenge to accurately determining the relationship between independent and dependent variables. These variables, which are not the main focus of the study, can influence the outcome being investigated and create confusion about the true impact of the independent variable. To ensure the validity of

## When to use Kruskal Wallis Test

A nonparametric hypothesis test that compares three or more independent groups is the Kruskal-Wallis test. You probably already know about one-way ANOVA, which compares the means of at least three groups, if you analyze data. The nonparametric variant of it is the Kruskal-Wallis test. The analysis makes fewer assumptions about your data than its parametric

## Exponential Smoothing Forecast in Time Series

Exponential Smoothing Forecast in Time Series, A forecasting technique for univariate time series data is exponential smoothing. With this strategy, forecasts are weighted averages of historical observations, with the weights of older observations decreasing exponentially. The study can now include model data with trends and seasonal components thanks to various forms of exponential smoothing. In

## Missing Value Imputation in R

Missing Value Imputation in R, Every data user is aware of the problem: Nearly all data sets contain some missing data, which can cause major issues like skewed estimations or decreased efficiency owing to a smaller data set. Imputation techniques can be used to replace missing data with new values in order to lessen these