Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
July 26, 2025International Journal of Environmental Sciences

Prediction of Agricultural GSDP of Assam using Cobb Douglas, Constant Elasticity of Substitution and Multiple Linear Regression models

View Full Paper
Ask AI
Bookmark
Share

Authors

SBSmrita BorthakurCentral University of HaryanaRSRakesh Kumar SahooSiksha O Anusandhan University

Discussion

Loading...

Member takes

Overview

Statistical analysis predicts agricultural GSDP in Assam, indicating MLR as the optimal model for policy formulation.

Key Points

  • The multiple linear regression model demonstrated the highest R² value, lowest mean squared error, and lowest Akaike Information Criterion.
  • Area and labour emerged as significant determinants of Assam's agricultural economic growth, driving productivity and GSDP performance.
  • Ordinary least squares and non-linear curve fitting were applied to estimate parameters for the Cobb-Douglas, CES, and MLR models.
  • Findings emphasize MLR's effectiveness in capturing agricultural input-output relationships, aiding policy formulation efforts.

Cite This Study

Borthakur et al. (2025) studied this question.

synapsesocial.com/papers/68af5bbcad7bf08b1eadf93ahttps://doi.org/10.64252/0gktjg86
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Sustainable Green Economy, Tropical Fruit Productivity, and Agricultural Supply Response in the Mugesera Region: A Machine Learning Approach2026
  2. 2The Relevance of the Cobb–Douglas Function Nowadays: Insights from the Global Agricultural Sector2026
  3. 3Predictive modelling of apple area, production, and productivity in Srinagar2025
  4. 4Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models2024 · 57 citations
  5. 5Hectareage Prediction Models for Paddy Crop of Middle Gujarat2024