🎯 Key Takeaways for quick navigation: 00:13 🎯 This video is an introduction to curve fitting using MATLAB, applicable to various fields beyond bituminous materials. 01:07 📝 The focus is on using the curve fitting tool or app in MATLAB, rather than writing custom code for curve fitting. 02:00 📊 The presentation outline covers five sections: Introduction to the curve fitting toolbox, error measures calculation, generating code for curve fitting, preparing a script, and customizing figures. 10:53 🔧 The video demonstrates using the curve fitting toolbox, selecting data in MATLAB, and evaluating fits for different equations/models. 22:16 📈 The presenter explains the importance of error measures like Sum of Squares due to Error (SSE) for evaluating the goodness of fit for various models. 26:01 📊 SST, SSR, and SSE are used to evaluate variability in curve fitting. SST is the total sample variability, SSR is the explained variability due to regression, and SSE is the unexplained variability due to error. 29:19 📏 R squared (R²) measures how much of the total variability is explained by the model. Higher R² values indicate a better fit, ranging from 0 to 1. 30:41 🔄 Adjusted R squared considers the number of parameters in the model and assesses if added terms genuinely improve the fit or result from randomness. It adjusts R² to account for the complexity of the model. 33:38 📏 Root Mean Square Error (RMSE) measures the standard deviation of residuals, showing how well the model fits the data. A smaller RMSE indicates a better fit. 36:18 ❓ SSE closer to 0 suggests the model has less random error and is more useful for prediction, while a higher R² indicates a greater proportion of variance accounted for by the model. Made with HARPA AI
Thank you very much - great introduction, just what I needed to get started :-)
Very nice explanation.Thank you
Very useful miss. Very thing explained from the scratch. Thank you so much
🎯 Key Takeaways for quick navigation:
00:13 🎯 This video is an introduction to curve fitting using MATLAB, applicable to various fields beyond bituminous materials.
01:07 📝 The focus is on using the curve fitting tool or app in MATLAB, rather than writing custom code for curve fitting.
02:00 📊 The presentation outline covers five sections: Introduction to the curve fitting toolbox, error measures calculation, generating code for curve fitting, preparing a script, and customizing figures.
10:53 🔧 The video demonstrates using the curve fitting toolbox, selecting data in MATLAB, and evaluating fits for different equations/models.
22:16 📈 The presenter explains the importance of error measures like Sum of Squares due to Error (SSE) for evaluating the goodness of fit for various models.
26:01 📊 SST, SSR, and SSE are used to evaluate variability in curve fitting. SST is the total sample variability, SSR is the explained variability due to regression, and SSE is the unexplained variability due to error.
29:19 📏 R squared (R²) measures how much of the total variability is explained by the model. Higher R² values indicate a better fit, ranging from 0 to 1.
30:41 🔄 Adjusted R squared considers the number of parameters in the model and assesses if added terms genuinely improve the fit or result from randomness. It adjusts R² to account for the complexity of the model.
33:38 📏 Root Mean Square Error (RMSE) measures the standard deviation of residuals, showing how well the model fits the data. A smaller RMSE indicates a better fit.
36:18 ❓ SSE closer to 0 suggests the model has less random error and is more useful for prediction, while a higher R² indicates a greater proportion of variance accounted for by the model.
Made with HARPA AI
You good 😊
thank you madam
The Matlab window is too small. It is very difficult to see the tables clearly .Next time have a bigger screen