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Author: Kanda Data

Data Analysis in R

Simple Linear Regression Analysis Using R Studio and How to Interpret It

By Kanda Data / Date Dec 04.2023

In the real world, accurate decisions need to be based on a deep understanding of data. One tool for processing and elaborating data is simple linear regression analysis. Simple linear regression analysis allows us to read patterns among scattered data points. A correct understanding of regression analysis gives us the power to make more accurate decisions and minimize uncertainty.

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Data Analysis in R

How to Test for Normality in Linear Regression Analysis Using R Studio

By Kanda Data / Date Nov 21.2023

Testing for normality in linear regression analysis is a crucial part of inferential method assumptions, requiring regression residuals to be normally distributed. Residuals are the differences between observed values and those predicted by the linear regression model.

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Excel Tutorial for Statistics

Descriptive Statistics Analysis in Excel: A Step-by-Step Guide for Researcher

By Kanda Data / Date Nov 15.2023

Descriptive statistics analysis is a crucial step in research, aimed at summarizing and illustrating fundamental information from a dataset. Through descriptive statistics, we can present data in a more informative and easily understandable format.

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Regression Tutorial using Excel

How to Perform Linear Regression Analysis Using Excel | A Complete Step-by-Step Tutorial and Guide

By Kanda Data / Date Nov 05.2023

In today’s post, Kanda Data provides a comprehensive tutorial on how to perform linear regression analysis using Microsoft Excel. Before we dive into the tutorial, the first step is to activate the Analysis ToolPak in Excel. Next, Kanda Data presents a case study example of research data to be analyzed using linear regression analysis.

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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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