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2 wrz 2020 · In this post, I’ll define independent and dependent samples, explain their pros and cons, highlight the appropriate analyses for each type, and illustrate how dependent groups can increase your statistical power.
3 lut 2022 · An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study. Independent variables are also called: Explanatory variables (they explain an event or outcome)
16 mar 2021 · There are three common types of statistical tests that make this assumption of independence: 1. Two Sample t-test. 2. ANOVA (Analysis of Variance) 3. Linear Regression. In the following sections, we explain why this assumption is made for each type of test along with how to determine whether or not this assumption is met. Assumption of ...
25 sie 2021 · Independent variables and dependent variables are the two fundamental types of variables in statistical modeling and experimental designs. Analysts use these methods to understand the relationships between the variables and estimate effect sizes.
Having independent and identically distributed (IID) data is a common assumption for statistical procedures and hypothesis tests. But what does that mouthful of words actually mean? That’s the topic of this post! And, I’ll provide helpful tips for determining whether your data are IID.
1 sty 2024 · Independent variables, often predictors or causes, are the factors that we expect to influence outcomes. They are the variables that researchers manipulate or select in an experiment to observe their effect on other variables.
Statistical independence is a fundamental concept in statistics. In this post, I explain its definitions with intuitive interpretations, examples, and resources for statistical testing of independence (R code and Excel function).
