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Home/Archive for: October 2023

Month: October 2023

Multiple Linear Regression

Choosing the Right Variables in Linear Regression using the OLS Method

By Kanda Data / Date Oct 25.2023

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.

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Nonparametric Statistics

Binary Logistic Regression Analysis in Research | Basic Theory

By Kanda Data / Date Oct 14.2023

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.

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Econometrics

Can Data Transformation Be Done More Than Once?

By Kanda Data / Date Oct 11.2023

For those of us accustomed to conducting research, understanding how to analyze data is a crucial skill to master. In the process, when we are processing data, we are sometimes faced with the choice of data transformation.

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Excel Tutorial for Statistics

Easy Guide to Finding the Standard Deviation of Research Variables in Excel

By Kanda Data / Date Oct 07.2023

Standard deviation is a statistical metric designed to measure the spread or variation of data within a research variable. You can determine how far each data point is from the average data in that variable through the standard deviation value.

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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
  • Statistics

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  • How to Tabulate Data Using Pivot Table for Your Research Results
  • Dummy Variables: A Solution for Categorical Variables in OLS Linear Regression
  • The Difference Between Residual and Error in Statistics
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