## COURSE OBJECTIVES

• To provide students a deeper understanding about the statistical data its types, collection, interpretation and analysis of data.
• Learn and use the concepts of theory of Probability.
• To provide students, the knowledge of Discrete and Continuous Probability distribution and their applications in computer engineering.
• To enable the students to learn and apply the tools for curve fitting via Linear Regression and Correlation.

### COURSE LEARNING OUTCOMES (CLO)

CLO: 1. Demonstrate an ability to use descriptive techniques to describe the statistical data.
CLO: 2. Define and illustrate the concepts and methods of probability theory.
CLO: 3. Use inferential statistical methods to solve problems in engineering sciences.

## COURSE CONTENTS

1. Introduction, CLOs, Outlines, Course Evaluation etc. Why Statistics? Probability? Descriptive and Inferential Statistics.
2. Variable, Data, and their types as Qualitative and Quantitative, Nominal, Ordinal, Interval, Ratio Data and Examples. Descriptive Methods for Qualitative Data.
3. Graphical Methods for describing qualitative data, Bar Charts, Summary Tables, Pie Charts. Dot-plots, Stem-Leaf Display, Frequency/Relative/Percentage Frequency distributions, Histogram, Cumulative Frequencies, CF Curves.
4. Numerical Methods for describing Data. Measures of Central Tendency: Mean, Median, Mode. Measures of Dispersion, Range, Describing Data using Chebysheve Rule, Empirical Rule.
5. Measures of relative standing, Z-Scores, Percentiles, Coefficient of Variation, Quartiles and Box-Plots.
6. Numerical Measures from Grouped Data. Short cut formula for the computation of standard deviation.
7. Introduction to probability theory, random experiment, sample space, simple and compound events. Compound Events. Venn Diagrams, Assigning probabilities. Prior-Classical method, empirical method, subjective method.
8. Additive Rule. Venn Diagram Examples, Compound Events and their probabilities. Conditional Probability, Independent Events.
9. Law of total probability and Bayes Rule. Examples and Exercises on prior and posterior probabilities.
10. Introduction to Random Variable, Discrete Random Variables and their Probability Distributions, Expected value and Variance.
11. Binomial Random Variable and its Probability Distribution, Mean and Variance of Binomial random variable. Poisson Random Variable its Distribution Function and Probabilities. Poisson Approximation to Binomial Distribution
12. Introduction to Continuous Random Variables and their Probabilities as definite integrals. Uniform distribution. Introduction to Normal Distribution.
13. Examples on finding probabilities of a normally distributed random variable by using Standard Normal Curve.
14. Normal Approximation to Binomial Distributions. Exponential Distribution.
15. Curve Fitting and linear regression models.