Course Outcome
CO1: Ability to find significance level and testing of hypothesis in software R
CO2: Ability to find regression equations and analyze the data in R
CO3: Ability to understand and analyze Multiple Regression and test using R- software.
CO4: Apply the knowledge of R gained to data Analytics in Estimation theory and Analysis of variance

List of Practical

• Testing of significance and confidence intervals for single proportion and difference of two proportion
• Testing of significance and confidence intervals for single mean and difference of two means and paired tests.
• Testing of significance and confidence intervals for difference of two standard deviations.
• Exact Sample Tests based on Chi-Square Distribution.
• Testing if the population variance has a specific value and its confidence intervals.
• Testing of goodness of fit.
• Testing of independence of attributes.
• Testing based on 2 X 2 contingency table without and with Yates’ corrections.
• Testing of significance and confidence intervals of an observed sample correlation coefficient.
• Testing and confidence intervals of equality of two population variances
• Simple Linear Regression
• Multiple Regression
• Tests for Linear Hypothesis
• Bias in regression estimates
• Lack of fit
• Orthogonal Polynomials
• Analysis of Variance of a one way classified data
• Analysis of Variance of a two way classified data with one observation per cell
• Analysis of Covariance of a one way classified data
• Analysis of Covariance of a two way classified data.