Tag: Kanda data
Descriptive Statistical Analysis Using Excel | Easy and Accurate
Descriptive statistical analysis is one of the important methods in analyzing data to obtain useful information for researchers. With Excel, you can easily describe and interpret data to gain a better understanding of patterns and trends in the analyzed data.
Simple Linear Regression Analysis Easily Using Excel
Simple linear regression analysis is a useful statistical technique for measuring and understanding the relationship between two variables. In this analysis, one variable (independent variable) is used to predict or explain the other variable (dependent variable).
Multicollinearity Test in Multiple Linear Regression Analysis
In multiple linear regression analysis, there is an assumption that the model constructed is not affected by multicollinearity issues, where two or more independent variables are strongly correlated. Multicollinearity can lead to errors in parameter estimation and reduce the reliability of the model.
Assumption of Residual Normality in Regression Analysis
The assumption of residual normality in regression analysis is a crucial foundation that must be met to ensure the attainment of the Best Linear Unbiased Estimator (BLUE). However, often, many researchers face difficulties in understanding this concept thoroughly.
Difference between Paired t-test and Independent t-test
A deep understanding of the difference between paired t-test and independent t-test is crucial for researchers. A strong grasp of both methods is key to making informed decisions based on analyzed data. Paired t-test and independent t-test are used to determine the difference in means between two sample groups.
Can regression estimation coefficients have negative values?
In regression analysis, estimation coefficients are parameters used to understand the influence of independent variables on the dependent variable. However, an interesting question arises: Can regression estimation coefficients have negative values? In this article, Kanda Data will delve into this phenomenon and discuss its practical implications in linear regression analysis using the ordinary least squares method.
When is autocorrelation testing performed in linear regression analysis?
In regression analysis, researchers must ensure that the constructed model meets several required assumptions. One assumption in ordinary least square linear regression is the absence of autocorrelation in the model’s residuals. Autocorrelation occurs when there is a correlation pattern among the residual values in the regression model.
Reasons why Likert scale variables need to undergo validity and reliability testing
A solid understanding of statistics, I believe, is crucial for researchers to master. Having a good grasp of statistics will lead us to choose the appropriate statistical methods in research.