Tag: statistics
Tutorial on How to Calculate Residual Values in Excel
Regression analysis is already widely used by researchers to explore the influence of independent variables on dependent variables. If we use regression analysis, we must have a good understanding of residual values. These residual values are needed in regression analysis. In addition, in the assumption tests required in linear regression analysis using the ordinary least square method, some also use residual values.
Understanding the Difference Between R-squared and Adjusted R-squared in OLS Linear Regression Output
R-squared (R²) and Adjusted R-squared (R² adjusted) are key metrics frequently used to assess the effectiveness of a linear regression model. The R-squared value provides information about the proportion of variability in the dependent variable explained by the independent variable in the linear regression equation.
Data That Cannot Be Transformed Using Natural Logarithm (Ln)
In quantitative data analysis, data transformation is not a new concept. It is a process of converting the original form of data into another form to improve the data and meet the assumptions required for quantitative data analysis.
Differences Between Paired Sample T-Test, Independent Sample T-Test, and One-Way ANOVA
Differential testing is aimed at determining the mean differences in the tested sample groups. In practice, paired sample t-test, independent sample t-test, and one-way ANOVA are often used to test means in more than one sample group.
Testing and Interpreting Homoscedasticity in Simple Linear Regression with R Studio
Homoscedasticity is a crucial assumption in ordinary least square (OLS) linear regression analysis. This assumption refers to the consistent variability of regression residuals across all predictor values. Homoscedasticity assumes that the spread of residual regression errors remains relatively constant along the regression line.
Descriptive Statistics Analysis in Excel: A Step-by-Step Guide for Researcher
Descriptive statistics analysis is a crucial step in research, aimed at summarizing and illustrating fundamental information from a dataset. Through descriptive statistics, we can present data in a more informative and easily understandable format.
Choosing the Right Variables in Linear Regression using the OLS Method
Linear regression analysis is frequently employed by researchers to investigate the impact of independent variables on dependent variables. The Ordinary Least Squares (OLS) method is a popular choice among scholars for estimating parameters in linear regression models. The OLS technique aims to minimize the squared differences between observed and predicted values.
Binary Logistic Regression Analysis in Research | Basic Theory
Regression analysis has become a staple tool among researchers. Indeed, regression analysis serves as a familiar associative test, aiming to discern the impact of one variable on another.