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Probability and Statistics

Probability and Statistics focuses on the "logical" collection, analysis, and interpretation of data using mathematical frameworks. It provides the "technical clarity" needed for "technical" decision-making, interpreting market trends, and understanding "unit economics" like CAC and LTV within "Business Information Systems".

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TabFlux . Probability and Statistics . FWU . BIT

Probability and Statistics

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Course Title: Probability and Statistics

Course No: BIT124

Nature of the Course: Theory + Lab

Semester: 2

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description

Course Objectives

Course Contents

1. Introduction and Data Management
6 hrs
1.1. Basic Concept of Statistics
1.2. Application of Statistics in including Information Technology (IT) Field
1.3. Scales of Measurement
1.4. Types of Data and Data source
1.5. Basic Vocabulary of Statistics
1.6. Organizing Numerical Data
  • Ordered array
  • Stem-and-Leaf display
  • Frequency Distribution
1.7. Visualizing Categorical Data
  • Bar Diagrams
  • Pareto Charts
  • Pie Chart
1.8. Visualizing Numerical Data
  • Histogram
  • Frequency polygon
  • Cumulative Frequency Curve or Ogive
2. Descriptive Statistics
7 hrs
2.1. Measures of Central Tendency
  • Mean
  • Median
  • Mode
2.2. Measures of Dispersion
  • Range
  • Semi-inter Quartile Range
  • Standard Deviation and Coefficient of Variation
2.3. Measures of Shape
  • Skewness (Using Karl Pearson's Method)
  • Kurtosis (Using Percentile Method)
2.4. Exploratory Data Analysis
  • Five Number Summary
  • Box and Whisker Plot
3. Basic Probability
6 hrs
3.1. Basic probability concepts
3.2. Basic Terminology Used in Probability
  • Random Experiment or Trial
  • Outcomes or Events
  • Sample Space
  • Mutually Exclusive Events
  • Equally Likely Events
  • Favorable Events
  • Exhaustive Events
  • Independent and Dependent Events
3.3. Definition of Probability
  • Classical, Statistical, Subjective and Axiomatic Definitions
  • Basic Principles of Counting, Permutation and Combinations
3.4. Laws of Probability
  • Additive
  • Multiplicative
3.5. Conditional Probability
  • Marginal and Joint probability
  • Statistical independence
4. Random variable and Probability Distribution
7 hrs
4.1. Random Variables
  • Concept of a Random Variable
  • Types of Random Variables
  • Probability Distribution of a Random Variable
  • Mathematical Expectation of a Random Variable
4.2. Discrete Probability Distribution
  • Binomial
  • Poisson
  • Geometric Distribution their Characteristic Features, Mean, and Variance
4.3. Continuous Probability Distribution
  • Normal
  • Standard Normal
  • Exponential, and Pareto Distribution their Characteristic Features, Mean, and Variance
5. Estimation
6 hrs
5.1. Definitions of Population; Sample; Parameter; Statistic and Standard Error
5.2. Concept of Point and Interval Estimation
5.3. Characteristics of Good Estimator
5.4. Confidence Interval Estimation for Mean
5.5. Concept of Method of Maximum Likelihood Estimation (MLE)
  • MLE for Parameters of Binomial, Poisson, Geometric Normal, Exponential, and Pareto Distribution
6. Hypothesis Testing
7 hrs
6.1. Hypothesis Testing Methodology
  • The Null and Alternative Hypothesis
  • Risks of Decision-Making using Hypothesis Testing
  • Level of Significance
  • Tails of a Test
  • Regions of Rejection and Non-rejection
  • Steps for Testing of Hypothesis
6.2. Test of Significance for Large Samples
  • Z test of Hypothesis for One Mean (σ known or unknown)
  • Z test for the Difference between Two Means
6.3. Test of Significance for Small Samples
  • Assumptions and Application of t-distribution
  • Degree of Freedom
  • t-test of Hypothesis for One Mean
  • t-test for the Difference between Two Means and Paired t-test
7. Simple Linear Correlation and Regression Analysis
6 hrs
7.1. Types of Correlations
7.2. Methods of Studying Simple Correlation
  • Scatter Diagram
  • Karl Pearson's Coefficient of Correlation
7.3. Test of Significance of Simple Co-relation Coefficient
7.4. Simple Linear Regression
  • Determining the Simple Linear Regression Equation using Least Squares Method
7.5. Standard Error of the Estimate
7.6. The Coefficient of Determination
7.7. Testing the Significance of Regression Coefficient in Simple Linear Regression Model

Laboratory Works

  1. 1.Laboratory Works

Text Books

  1. 1.Levine, Stephan, Krehbiel and Berenson (2008), Statistics for Managers using Microsoft excel, 5th Edition, New Delhi: Prentice Hall of India.
  2. 2.Douglas A Lind, William G Marchal, Samuel A Wathen (2008), Statistical Techniques in Business and Economics, 13th edition, Tata McGraw-Hill India.
  3. 3.Douglas C. Montgomery & George C. Ranger (2003). Applied Statistics and Probability for Engineers. 3rd Ed., John Willey and Sons, Inc.
  4. 4.Richard A. Johnson (2001). Probability and Statistics for Engineers. 6th Ed., Pearson Education, India

Source:

This course is designed to provide the students with basic concept of statistics. The course covers the basic tools used to describe data for the purposes of transforming data into information. In addition, the course presents the fundamentals of statistical inference.
Upon the completion of this course, students will be able to: Explain how statistics is used in IT; Explain the principles of data presentation; Develop tables and charts for Numerical data; Organize the numerical data; Understand basic probability concepts; Define the term random variable and probability distribution; Understand the concepts of estimation; Perform hypothesis testing; Compute correlation coefficient and interpret its values; Perform regression analysis