On a dataset I had done Descriptive & Regression Model Analysis

Isra Islam

Target Variable: MPG ​
Model Fit Metrics:
Multiple R: 0.99094212 (strong positive correlation) ​
R Square: 0.981966286 (98.2% variation explained) ​
Adjusted R Square: 0.979142015 (strong model fit) ​
Model Significance:
F Statistic: 3503.05902 (high value, low p-value, model is significant) ​
Key Findings:
Strong model fit and significant variables. ​
Coefficients indicate the direction and magnitude of relationships. ​
Q8: Regression Function ​
Equation:
MPG = -4.596 * Cylinders + 0.119 * ModelYear - 0.00393 * Displacement + 0.0836 * Horsepower + 0.8526 * Weight - 0.867 * Acceleration ​
Explanation: Coefficients show how each feature affects MPG.
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Posted Sep 23, 2024

Linear regression predicted vehicle MPG using attributes like weight, horsepower, and origin, providing insights to enhance vehicle design and improve fuel effi

Data Analysis using Excel
Analyze job talent pool in data
domain
Data Analysis using Excel Analyze job talent pool in data domain
I created wireframe for a project
I created wireframe for a project