Tag: Statistical Analysis
Data Transformation to Address Non-Normally Distributed Data
The assumption that data must be normally distributed is often a prerequisite for using certain inferential statistical tests. However, sometimes the test results do not meet expectations, indicating that the data is not normally distributed.
Linear Regression Residual Calculation Formula
In linear regression analysis, testing residuals is a very common practice. One crucial assumption in linear regression using the least squares method is that the residuals must be normally distributed.
Calculation Formula for the Coefficient of Determination (R Square) in Simple Linear Regression
The coefficient of determination plays a crucial role in regression analysis. It is not surprising that various studies using regression analysis often present the value of the coefficient of determination. Recognizing the importance of this value, Kanda Data will discuss this topic in detail.
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.
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.