Tag: Kanda data
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.
Can nominal scale data be analyzed using regression analysis?
Regression analysis is commonly used to examine the influence of independent variables on dependent variables observed in a study. However, regression analysis is more suitable for data with interval or ratio scales. How about data with nominal scales, can regression still be used?
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.
Simple Linear Regression Analysis in Excel and How to Interpret the Results
Simple linear regression analysis aims to determine the influence of one independent variable on a dependent variable. In this analysis, we can understand and measure how much the independent variable explains the variation in the dependent variable.
How to Calculate Net Present Value (NPV) to Assess Investment Viability
Net Present Value (NPV) is a crucial investment evaluation method employed to assist companies in determining whether an investment or project will yield financial gains or losses over a specified period. NPV is the difference between the present value of cash inflow and the present value of cash outflow from an investment over a specific timeframe.
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.