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.

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)