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Tag: statistics

Data Analysis in R

How to Test Heteroscedasticity in Linear Regression and Interpretation in R (Part 3)

By Kanda Data / Date Apr 30.2023

One of the assumptions required in Ordinary Least Squares (OLS) linear regression is that the variance of the residuals is constant. This assumption is often referred to as the homoscedasticity assumption. Some researchers are more familiar with the term heteroscedasticity test.

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Data Analysis in R

How to Analyze Multicollinearity in Linear Regression and its Interpretation in R (Part 2)

By Kanda Data / Date Apr 17.2023

Non-multicollinearity is one of the assumptions required in the ordinary least square (OLS) method of linear regression analysis. Non-multicollinearity assumption implies that there is no strong correlation among the independent variables in the equation.

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Data Analysis in R

How to Analyze Multiple Linear Regression and Interpretation in R (Part 1)

By Kanda Data / Date Apr 11.2023

Multiple linear regression analysis has been widely used by researchers to analyze the influence of independent variables on dependent variables. There are many tools that researchers can use to analyze multiple linear regression.

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Excel Tutorial for Statistics

How to Transform Natural Logarithm (ln) and Reverse (anti-Ln) in Excel

By Kanda Data / Date Apr 06.2023

Researchers often transform data to convert original data into another form to meet certain assumptions. Researchers can do several forms of data transformation. Natural logarithm transformation is a form of transformation frequently used by researchers in data analysis.

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Excel Tutorial for Statistics

Step-by-Step Tutorial: Finding Predicted and Residual Values in Linear Regression with Excel

By Kanda Data / Date Apr 03.2023

In linear regression analysis, residual values play an important role in supporting the main analysis. Residual values are the difference between actual values and predicted values. In the assumption testing of linear regression using the OLS method, residual values are needed for the testing of assumptions.

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Research Methodology

Sampling Methods and Statistical Analysis in Survey Research

By Kanda Data / Date Mar 31.2023

In conducting research, the primary objective is to analyze a phenomenon and find effective solutions to problems. The process of research involves collecting facts, evidence or results to develop, test or enhance knowledge about natural and social phenomena. Research plays a significant role in advancing science, and it is essential that research findings are made accessible to all.

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Multiple Linear Regression

How to Interpret Negative Coefficient Estimations in Linear Regression?

By Kanda Data / Date Mar 28.2023

The ordinary least squares (OLS) method is commonly employed in linear regression analysis to establish the relationship between the independent and dependent variables. Despite its numerous advantages, researchers must meet certain requirements to use this method.

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Simple Linear Regression

How to Perform Linear Regression using Data Analysis in Excel

By Kanda Data / Date Mar 23.2023

Researchers have widely used linear regression analysis to analyze the effect of a variable on other variables. Linear regression analysis consists of the dependent variable and the independent variable. The difference between the two is that the dependent variable is the affected variable, while the independent variable is the influencing variable.

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Categories

  • Article Publication
  • Assumptions of Linear Regression
  • Comparison Test
  • Correlation Test
  • Data Analysis in R
  • Econometrics
  • Excel Tutorial for Statistics
  • Multiple Linear Regression
  • Nonparametric Statistics
  • Profit Analysis
  • Regression Tutorial using Excel
  • Research Methodology
  • Simple Linear Regression
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