Course outcomes:
CO1:Install and use R for simple programming tasks.
CO 2. Extend the functionality of R by using add-on packages
CO 3. Extract data from files and other sources and perform various data manipulation tasks on them. CO 4. Code statistical functions in R.
CO 5. Use R Graphics and Tables to visualize results of various statistical operations on data . CO6. Apply the knowledge of R gained to data Analytics for real life applications.

List of Practical

• Graphical representation of data.
• Problems based on measures of central tendency.
• Problems based on measures of dispersion.
• Problems based on combined mean and variance and coefficient of variation
• Problems based on moments, skewness and kurtosis
• Fitting of binomial distributions for n and p = q = ½.
• Fitting of binomial distributions for given n and p.
• Fitting of binomial distributions after computing mean and variance.
• Fitting of Poisson distributions for given value of the parameter.
• Fitting of Poisson distributions after computing mean.
• Fitting of negative binomial.
• Fitting of suitable distribution.
• Application problems based on binomial distribution.
• Application problems based on Poisson distribution.
• Application problems based on negative binomial distribution.
• Problems based on area property of normal distribution
• To find the ordinate for a given area for normal distribution.
• Application based problems using normal distribution.
• Fitting of normal distribution when parameters are given
• Fitting of normal distribution when parameters are not given
• Fitting of polynomials, exponential curves.
• Karl Pearson correlation coefficient.
• Correlation coefficient for a bivariate frequency distribution.
• Lines of regression, angle between lines and estimated values of variables