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Tag: time series data

Understanding Cross-Section, Time Series, and Panel Data Structures in Research

By Kanda Data / Date Jul 19.2025 / Category Statistics

For those of you currently conducting research, I believe it’s important to have a solid understanding of data structure before starting. This is crucial because the structure of your data will determine the appropriate analytical tools to use when analyzing your research results.

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How to Analyze Heteroskedasticity for Time Series Data in Multiple Linear Regression and Its Interpretation

By Kanda Data / Date Dec 14.2024 / Category Data Analysis in R

The heteroskedasticity test is one of the assumption tests in the Ordinary Least Squares (OLS) linear regression method, aimed at ensuring that the residual variance remains constant. If the multiple linear regression equation being tested shows non-constant residual variance, this is referred to as heteroskedasticity.

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How to Perform Multiple Linear Regression Analysis on Time Series Data Using R Studio

By Kanda Data / Date Oct 07.2024 / Category Data Analysis in R

Multiple linear regression analysis on time series data, along with its assumption tests, can be performed using R Studio. In a previous article, I explained how to conduct multiple linear regression analysis and assumption tests for cross-sectional data.

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Assumptions of Linear Regression

Assumptions of Multiple Linear Regression on Time Series Data

By Kanda Data / Date Jul 25.2024

Multiple linear regression is a statistical analysis technique used to model the relationship between one dependent variable and two or more independent variables. The multiple linear regression model is used to predict the value of the dependent variable based on the estimated values of the independent variables.

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Assumptions of Linear Regression

When is autocorrelation testing performed in linear regression analysis?

By Kanda Data / Date Apr 24.2024

In regression analysis, researchers must ensure that the constructed model meets several required assumptions. One assumption in ordinary least square linear regression is the absence of autocorrelation in the model’s residuals. Autocorrelation occurs when there is a correlation pattern among the residual values in the regression model.

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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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Statistics

How to Distinguish Cross-Section Data, Time Series Data, and Panel Data

By Kanda Data / Date Jul 28.2023

Based on the collection method, data can be divided into cross-section, time series, and panel data. A good understanding of the differences between the three types and how to collect the three types of data will lead to the right choice of analysis.

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