Non Linear Regression Analysis - Quadratic Model using SPSS #datascience #dataanalytics

Non Linear Regression Analysis Quadratic Model using SPSS #datascience #dataanalytics #spss
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📊 Welcome to "My Easy Statistics"! Prepare to be captivated as we embark on a fascinating exploration into the world of statistics, where we'll unveil the true potential of quadratic curve linear regression. Join us as we demystify the art of regression analysis and apply it to the intriguing puzzle of the height-weight relationship.
📈 While linear regression is a well-known concept, it often paints a simplistic picture of the world. Real-life data, however, is rarely so straightforward. That's where nonlinear regression models come into play, and in this video, we shine the spotlight on quadratic curve linear regression—a powerful tool for deciphering complex relationships.
🔍 In this enlightening case study, we'll dive deep into the analysis of data from 55 individuals, meticulously processed using the robust SPSS software. Our mission? To uncover the hidden nuances in the connection between height and weight.
Here's a sneak peek of what awaits you: 🔍 Hypothesis: We begin with the intriguing hypothesis that there may be no significant relationship between height and weight. 📊 Data: Our dataset boasts 55 respondents, each contributing measurements of their height (in centimeters) and weight (in kilograms).
Revealing Insights from the Linear Model: 📈 Linear Model Summary:
• R-Value: 0.598 (Moderate positive correlation)
• R-Square: 0.357 (Explaining 35.7% of weight variance)
• Adjusted R-Square: 0.345 (34.5% variance while considering other factors)
• Standardized Error of Estimation: 8.879 kg 📊 ANOVA Table:
• F-Statistic: 29.485 (Unquestionably significant relationship)
• P-Value: 0.000 (Statistical significance at its finest)
Unveiling the Triumph of the Quadratic Model: 📈 Quadratic Model Summary:
• R-Value: 0.695 (A stronger correlation emerges)
• R-Square: 0.482 (Unraveling 48.2% of weight variance, surpassing linear) 📊 Coefficients:
• Height: -19.054 (Weight initially decreases with height)
• Height Square: 0.065 (Weight increases after reaching a pivotal point)
• Intercept: 1446.612 (A significant starting point)
The Grand Finale: 🔍 Our comprehensive analysis leads to a riveting conclusion—the quadratic model reigns supreme in decoding the intricate relationship between height and weight. Visualized through a captivating parabolic curve, we unveil how weight initially wanes with height, only to surge beyond a crucial threshold.
📊 Whether you're a curious student, an avid researcher, or a data enthusiast, this video is your ticket to mastering the art of quadratic curve linear regression. Join us on this thrilling statistical journey and remember to like, share, and subscribe for a regular dose of statistical wisdom! Thank you for choosing "My Easy Statistics" as your trusted guide in the world of data analysis.

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