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Author: Kanda Data

Multiple Linear Regression

Understanding the Importance of the Coefficient of Determination in Linear Regression Analysis

By Kanda Data / Date Mar 21.2024

In linear regression analysis, one important parameter often encountered is the coefficient of determination. The value of this coefficient provides an indication of how well the linear regression model can explain the variation in the data.

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Statistics

Understanding the Essence of the Difference Between Descriptive Statistics and Inferential Statistics in Research

By Kanda Data / Date Mar 20.2024

In conducting research, understanding the basic theory of statistics becomes crucial for researchers. Why is this so important? Because to extract accurate conclusions from research data, careful analysis using statistical tools is needed.

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Assumptions of Linear Regression

Understanding the Essence of Assumption Testing in Linear Regression Analysis: Prominent Differences between Cross-Sectional Data and Time Series Data

By Kanda Data / Date Mar 19.2024

Linear regression analysis has become one of the primary tools for researchers to explore the influence of independent variables on dependent variables. The Ordinary Least Squares (OLS) method has been a mainstay in conducting this linear regression analysis.

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Comparison Test

Understanding the Difference Between Paired T-Test and Wilcoxon Test in Statistics

By Kanda Data / Date Mar 15.2024

In the realm of statistics, associative tests play a crucial role in examining differences, relationships, and influences between variables. One common form of associative test is the test for differences, which aims to compare the means of two or more sample groups.

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Assumptions of Linear Regression

The data that cannot be transformed using natural logarithm (Ln)

By Kanda Data / Date Mar 11.2024

In quantitative data analysis, to ensure unbiased and consistent estimations, it’s important to meet several assumptions required in the conducted tests. However, sometimes, the test results may not meet the desired expectations.

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Profit Analysis

Calculation Method of Net Present Value (NPV) in Project Feasibility Studies

By Kanda Data / Date Mar 08.2024

One of the indicators to evaluate the profit potential of an investment project is Net Present Value (NPV). The calculation of NPV forms a strong foundation in project feasibility analysis, enabling business owners to determine whether a project is feasible to pursue or not.

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Multiple Linear Regression

How to Determine the F-Table Value (F Critical Value) in Excel

By Kanda Data / Date Feb 09.2024

In assessing the fit of a linear regression model, researchers need to find the critical values from the F-distribution (F-table). Typically, researchers often use these tables to evaluate the results of regression analysis. However, with technological advancements, determining the F-table value can easily be obtained using Excel.

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Multiple Linear Regression

How to Determine the T-table (T critical value) in Excel for Linear Regression Analysis

By Kanda Data / Date Feb 07.2024

In linear regression analysis, to determine the significance of the regression coefficients, researchers need to find the critical values from the t-student distribution (T-table). Typically, researchers often use these tables to evaluate the results of regression analysis. However, with technological advancements, determining the T-table value can easily be obtained using a spreadsheet, such as Excel.

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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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  • Differences in Nominal, Ordinal, Interval, and Ratio Data Measurement Scales for Research
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  • How to Create a Research Location Map in Excel: District, Province, and Country Maps
  • How to Determine the Minimum Sample Size in Survey Research to Ensure Representativeness
  • Regression Analysis for Binary Categorical Dependent Variables
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