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Related Concept Videos

Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

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Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
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Multiple Regression01:25

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective

Suriyan Jomthanachai1, Wai Peng Wong2, Khai Wah Khaw3

  • 1Faculty of Management Sciences, Prince of Songkla University (PSU), Songkhla, 90112 Thailand.

Computational Economics
|February 7, 2023
PubMed
Summary

Machine learning models predict logistics performance using economic data across six ASEAN nations. Artificial neural networks excelled for Singapore, Malaysia, and the Philippines, while Ridge regression was best for Indonesia, Thailand, and Vietnam.

Keywords:
Artificial neural networkData envelopment analysisLinear regressionLogistics performance indexMachine learningPrediction

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Area of Science:

  • Economics
  • Computer Science
  • Logistics and Supply Chain Management

Background:

  • Logistics performance is crucial for economic development and global trade.
  • Predicting logistics performance requires analyzing complex economic factors.
  • Existing models may not fully capture country-specific economic drivers of logistics.

Purpose of the Study:

  • To develop and compare machine learning models for predicting the Logistics Performance Index (LPI).
  • To utilize both macroeconomic and microeconomic data for enhanced prediction accuracy.
  • To identify key economic drivers influencing LPI in six ASEAN member countries.

Main Methods:

  • Employed linear (Ridge regression) and non-linear (Artificial Neural Network) machine learning algorithms.
  • Integrated macroeconomic panel data with microeconomic panel data derived from Data Envelopment Analysis (DEA) for financial efficiency.
  • Applied the models to a dataset encompassing six ASEAN member countries.

Main Results:

  • Artificial Neural Network demonstrated superior performance for aggregated data from Singapore, Malaysia, and the Philippines.
  • Ridge regression proved effective for datasets from Indonesia, Thailand, and Vietnam.
  • Identified specific drivers: macroeconomic factors in Vietnam and financial efficiency in Malaysia's logistics sector.

Conclusions:

  • Machine learning models offer precise trend forecasting for LPI.
  • Country-specific model selection is crucial due to data limitations and varying economic influences.
  • Findings support targeted policy interventions to enhance national logistics and supply chain capabilities.