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Examples of Independent and Paired Samples in the field of research, comparing groups is a common practice to understand the effectiveness of interventions, treatments, or programs. Independent and paired samples are two statistical comparison...
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...
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...
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...
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...
Autocorrelation and Partial Autocorrelation in Time Series, The correlation between two observations made at various times throughout a time series is known as autocorrelation. For instance, there might be a significant positive or negative...
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...
Cross-validation in Machine Learning, cross-validation is a word that everyone who works with machine learning techniques will come across at some point. We provide you with a quick overview of cross-validation in this blog...
Principal Component Analysis Advantages, with the help of Principal Component Analysis (PCA), a statistical technique, we are able to reduce the number of features in our data from a large number to just a...
How to Analyze Likert Scale Data? The most popular technique for sizing replies in survey investigations is the use of Likert scales. The Likert scale is used in survey questions that ask you to...
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