Linear Regression using R
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This document presents a linear regression analysis performed in R. It defines dependent and independent variables for a linear regression model using data from a CSV file. It generates an ANOVA table, correlation plots, scatter plots with and without regression lines, normal probability plots of residuals, a residuals histogram, and calculates errors. It also produces confidence and prediction intervals for predicted mean responses at new x-values.
Linear Regression using R
- 1. Linear Regression using R Presented by Sunil Kumar P (A13020) Maruthi Nataraj K (A13009) Praxis Business School, Kolkata
- 2. READING CSV FILE FROM SPECIFIED LOCATION Contents display
- 3. DEFINING DEPENDENT (Y) AND INDEPEDENT (X) VARIABLES FOR LINEAR REGRESSION MODEL ANOVA Correlation
- 4. SCATTER PLOT OF Y AND X WITHOUT & WITH REGRESSION LINE
- 5. NORMAL PROBABILITY PLOTS OF RESIDUALS
- 6. NORMAL PROBABILITY PLOTS OF RESIDUALS
- 7. RESIDUALS HISTOGRAM AND NORMALITY TESTS
- 8. MEAN ABSOLUTE PERCENTAGE ERROR CALCULATION
- 9. CONFIDENCE AND PREDICTION INTERVAL FOR THE MEAN RESPONSE AT NEW X VALUE (95% default) Confidence Interval for the mean response at new x value and desired confidence interval (level) Confidence Interval for the mean response at various new x values
- 10. SCATTER PLOT WITH FITTED LINE ,CONFIDENCE BANDS AND PREDICTION BANDS(95% CI default)