Statistics I
Deep Understanding: 60 hours
This course contains basics of statistics, descriptive statistics, probability, sampling, random variables and mathematical expectations, probability distribution, correlation and regression.
Impart knowledge of descriptive statistics, correlation, regression, and sampling,Provide theoretical and applied knowledge of probability and probability distributions
Course Contents
Basic concept of statistics, Application of statistics in Computer Science & IT, Scales of measurement, Variables, Types of data, Notion of a statistical population
Measures of central tendency, Measures of dispersion, Measures of skewness, Measures of kurtosis, Moments, Stem and leaf display, Five number summary, Box plot, Problems and illustrative examples related to Computer Science and IT
Concepts of probability, Definitions of probability, Laws of probability, Bayes theorem, Prior and posterior probabilities, Problems and illustrative examples related to Computer Science and IT
Definitions of population, Sample survey vs. census survey, Sampling error and non-sampling error, Types of sampling
Concept of a random variable, Types of random variables, Probability distribution of a random variable, Mathematical expectation of a random variable, Addition and multiplicative theorems of expectation, Problems and illustrative examples related to Computer Science and IT
Probability distribution function, Joint probability distribution of two random variables, Discrete distributions: Bernoulli trial, Binomial, and Poisson distributions, Continuous distributions: Normal distribution, Standardization of normal distribution, Normal distribution as an approximation of Binomial and Poisson distribution, Exponential and Gamma distributions, Problems and illustrative examples related to Computer Science and IT
Bivariate data, Bivariate frequency distribution, Correlation between two variables, Karl Pearson’s coefficient of correlation (r), Spearman’s rank correlation, Regression Analysis: Fitting of lines of regression by the least squares method, Coefficient of determination, Problems and illustrative examples related to Computer Science and IT
Laboratory Works
Use of statistical software such as Microsoft Excel, SPSS, STATA for practical problems,Computation of measures of central tendency (ungrouped and grouped data),Computation of measures of dispersion and coefficient of variation,Measures of skewness and kurtosis using method of moments and Box & whisker plot,Scatter diagram and correlation coefficient computation,Fitting of lines of regression and verification with computer output,Conditional probability and Bayes theorem,Obtaining descriptive statistics of probability distributions,Fitting probability distributions in real data (Binomial, Poisson, Normal)