Tag: statistics
Benefits of Using Cross Tabulation in Descriptive Statistical Analysis
When performing descriptive statistical analysis, the primary goal is to provide a general overview of the data being studied. One highly useful tool that supports descriptive analysis is cross tabulation (crosstabs).
How to Correctly Interpret a Negative Estimation Coefficient
The goal of linear regression analysis is to understand the influence of independent variables on dependent variables. The result of linear regression analysis is the regression coefficient, which indicates the size and magnitude of the influence of independent variables on dependent variables.
Correlation Analysis on Non-Parametric Variables Measured Using Likert Scale
For researchers, correlation analysis is a familiar tool used to measure the strength and direction of the relationship between two variables. However, correlation analysis on non-parametric variables measured using the Likert scale has fundamental differences compared to correlation analysis on parametric variables.
The Difference Between Pearson Correlation and Spearman Rank Correlation in Research
For those currently conducting data analysis, correlation tests are commonly used to measure the relationship between two variables. However, not all correlation tests are suitable for every type of data. As we all know, data types and characteristics can vary.
Differences Between Paired Sample T-Tests and Independent Sample T-Tests
When we want to compare the means of two data groups, we often use a difference test. The most commonly used difference test is the t-test. In a t-test, the samples being compared can be either paired or independent.
The Difference Between Residual and Error in Statistics
In the field of statistics, the terms “residual” and “error” are often used interchangeably. Many researchers and practitioners consider these terms to have the same meaning, but in reality, they represent significantly different concepts.
Assumptions of Multiple Linear Regression on Cross-Section Data
Multiple linear regression is a statistical technique used to predict the value of a dependent variable based on several independent variables. This regression provides a way to understand and measure the influence of independent variables on the dependent variable.
Assumptions of Multiple Linear Regression on Time Series Data
Multiple linear regression is a statistical analysis technique used to model the relationship between one dependent variable and two or more independent variables. The multiple linear regression model is used to predict the value of the dependent variable based on the estimated values of the independent variables.