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Practical Implementation on Linear & Multiple Regression Analysis

This guide provides a practical implementation of linear and multiple regression analysis using the IRIS dataset. It includes a clear explanation of the linear regression concept, illustrated via a scatter plot of the dataset. The tutorial covers how to fit a regression curve and calculate errors. By evaluating the fitted regression line, users are equipped to determine fitted values corresponding to observed data points. An example showcases how to calculate the residuals, enhancing understanding of error measurement in regression analysis.

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Practical Implementation on Linear & Multiple Regression Analysis

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  1. Practical Implementation on Linear & Multiple Regression Analysis

  2. Concept of Linear Regression

  3. Given Example

  4. Scatter Plot of given data set

  5. Fitted Regression Curve

  6. Calculating Error • Once the fitted regression line is known, the fitted value of corresponding to any observed data point can be calculated. For example, the fitted value corresponding to the 21st observation in above Table is: • The observed response at this point is y21 = 194 Therefore, the residual at this point is:

  7. Calculated Error Table

  8. IRIS Dataset Description

  9. Simple Linear Regression on IRIS data set

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