Secret Tutorials2024-01-19T20:29:59+00:00

## Regression Analysis

A simple linear regression analysis is a statistical method that helps to predict the value of a dependent variable based on the value of an independent [...]

## One-Way ANOVA

One-Way ANOVA If you aim to investigate whether there are any statistically significant distinctions in the means of two or more distinct groups, you can employ [...]

## Independent-Samples T-Test

The independent-samples t-test is used to determine if a difference exists between the means of two independent groups on a continuous dependent variable. More specifically, it [...]

## Binomial Logistic Regression

Binomial logistic regression is a statistical test for predicting the likelihood of an observation belonging to one of two possible categories of a binary dependent variable. [...]

## Paired-Samples T-Test

The paired-samples t-test serves the purpose of assessing whether the mean discrepancy between interconnected observations is statistically significant. These observations may involve the same individuals evaluated [...]

## Two-Way ANCOVA

The two-way ANCOVA is a statistical test to assess whether there is an interaction effect between two distinct, independent variables on a continuous [...]

## One-Way MANCOVA

The one-way multivariate analysis of covariance (one-way MANCOVA) extends the capabilities of the one-way MANOVA and one-way ANCOVA by incorporating either a continuous covariate or multiple [...]

## HMR

Like standard multiple regression, hierarchical multiple regression (also known as sequential multiple regression) allows you to predict a dependent variable based on multiple independent variables. However, the procedure [...]

## PCA

Principal components analysis (i.e., PCA) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Its aim is to reduce a larger set of [...]

## Two-Way MANOVA

The two-way multivariate analysis of variance (MANOVA) is an analytical technique that extends the principles of the two-way ANOVA to scenarios with multiple dependent variables. It [...]

## Multiple Regression Analysis

A multiple regression is used to predict a continuous dependent variable based on multiple independent variables. As such, it extends simple linear regression, which is used when [...]

## One-Way RM ANOVA

The one-way repeated measures analysis of variance (ANOVA) is a statistical technique that extends the concept of the paired-samples t-test. It is utilized to identify if [...]

## Correlation Analysis

Correlation Analysis The Pearson product-moment correlation is used to determine the strength and direction of a linear relationship between two continuous variables. More specifically, the test [...]

## Chi-Square Test

The chi-square test can be used to test a variety of sizes of contingency tables, as well as more than one type of null and alternative [...]

## Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA that assesses the interaction effect between two independent variables on a continuous dependent [...]

## One-Way MANOVA

The one-way multivariate analysis of variance (MANOVA) is a statistical method that extends the one-way ANOVA by accommodating two or more dependent variables instead of one. [...]

## One-Way ANCOVA

ANCOVA is a statistical method that extends the one-way ANOVA to include a covariate variable. This covariate is linearly related to the dependent variable, and its [...]

## Three-Way RM ANOVA

The three-way repeated measures ANOVA is a robust statistical test used in experimental psychology and other scientific fields. The three-way repeated measures ANOVA enables researchers to [...]

## Two-Way RM ANOVA

The two-way repeated measures ANOVA is a statistical test used to identify whether there is a significant interaction effect between two within-subjects factors [...]

## Kaplan-Meier Analysis

Kaplan-Meier Analysis The Kaplan-Meier method (Kaplan & Meier, 1958) (also known as the "product-limit method") is a nonparametric method used to estimate the probability of survival [...]

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