Tag: statistics
How to Analyze Heteroskedasticity in Linear Regression Using R Studio
Heteroskedasticity testing is an assumption test in linear regression using the OLS method to ensure that the residual variance is constant. A constant residual variance is referred to as homoskedasticity.
How to Perform an Independent Sample t-Test and Interpret the Results in R Studio
The independent sample t-test in R Studio is used to compare two independent groups. Through this t-test, we can determine whether there is a significant difference between the means of the two groups being compared.
How to Perform Paired Sample t-Tests in R Studio and Interpret the Results
Paired sample t-tests, which aim to identify differences between two paired data sets, can be analyzed using R Studio. Through paired sample t-tests, we can determine whether there are significant changes after a certain treatment or program carried out during the research activity.
How to Perform Multiple Linear Regression Analysis on Time Series Data Using R Studio
Multiple linear regression analysis on time series data, along with its assumption tests, can be performed using R Studio. In a previous article, I explained how to conduct multiple linear regression analysis and assumption tests for cross-sectional data.
How to Perform Multiple Linear Regression Analysis Using R Studio: A Complete Guide
Multiple linear regression analysis requires commands to be executed in R Studio. Given the importance of understanding how to analyze and interpret multiple linear regression using R Studio, Kanda Data will write an article discussing this topic.
How to Analyze Likert Scale Variables | Non-Parametric Ordinal Scale Variables
Non-parametric variables measured using the Likert scale (ordinal scale) have a slightly different analysis approach compared to parametric variables. This Likert scale approach is often used in social and management research. In such studies, we are often confronted with variables that cannot be directly quantified.
Assumption Tests for Multiple Linear Regression on Cross-Sectional Data
In multiple linear regression analysis using cross-sectional data, there are several assumption tests that must be conducted to obtain the best linear unbiased estimator. It is crucial to understand which assumption tests are required for research utilizing cross-sectional data. This is important because the assumption tests for cross-sectional, time series, and panel data differ in some respects.
How to Choose a 5% or 10% Margin of Error in Slovin’s Formula | Calculating the Minimum Sample Size
In calculating the minimum sample size using Slovin’s formula, researchers can choose a 5% or 10% margin of error. What’s the difference, and how do you choose the right one? In survey research, when observing a population, we are often faced with the challenge of a large population size that needs to be observed.