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Assumptions of Linear Regression

How to Test the Normality Assumption in Linear Regression and Interpreting the Output

By Kanda Data / Date May 13.2022

The normality test is one of the assumption tests in linear regression using the ordinary least square (OLS) method. The normality test is intended to determine whether the residuals are normally distributed or not.

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Correlation Test

Correlation Test for Ratio Scale Variables

By Kanda Data / Date May 10.2022

Correlation analysis is an associative test to determine the relationship between variables. Correlation tests for parametric variables and non-parametric variables are different. Therefore you need to understand how to choose a correlation test according to statistical rules.

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Statistics

How to Write and Test Statistical Hypotheses in Simple Linear Regression

By Kanda Data / Date May 06.2022

We need to develop hypotheses when conducting research. A hypothesis is a provisional assumption or statement of the research. The hypothesis needs to be proven, whether true or false, through the research process.

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Statistics

How to Reverse Natural Logarithm to Initial Data in Excel

By Kanda Data / Date May 03.2022

Researchers often use the natural logarithm form (Ln) transformation. In the previous article, I have written about How to Transform Data into Natural Logarithm (Ln) in Excel.

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Statistics

How to Transform Data into Natural Logarithm (Ln) in Excel

By Kanda Data / Date Apr 29.2022

You may have seen research data transformed into other forms in analyzing research data. The data transformation is determined by changing the measurement scale of the original data into another form.

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Statistics

Create Residual and Y Predicted in Excel

By Kanda Data / Date Apr 26.2022

Residual and predicted Y have an important role in linear regression analysis using the Ordinary Least Square (OLS) method. Several OLS linear regression method assumptions were conducted to test the residuals, including normally distributed residuals and constant residual variance.

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Multiple Linear Regression

Multiple Linear Regression Analysis and Interpreting the Output in Excel

By Kanda Data / Date Apr 22.2022

We can use multiple linear regression analysis to estimate the effect of the independent variable on the dependent variable. Multiple linear regression using at least two independent variables.

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Correlation Test

The Fundamental Differences of Pearson Correlation, Spearman Rank, Kendall tau, and Chi-Square

By Kanda Data / Date Apr 19.2022

The associative correlation test between variables is currently the most frequently chosen by researchers, lecturers, and students to answer research hypotheses. There are several choices of correlation tests that can be chosen, but the selection of correlation tests must follow statistical rules.

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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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