1. Introduction to Statistics (MTS-102)
Course Outline Review
Introduction to Statistics (MTS-102)
BBA-II, BS, BBA (exec)
Spring Semester
Instructors:
Ms. Aniqa Kashif, Dr. Musarrat A. Khan,
Ms. Rubina Sethi &
Mr. Yaseen Ahmed Meenai

2. Course Description:
The course content includes; types of data, frequency distributions, measures of central tendency and dispersion, exploratory data analysis, introduction to set and probability theory, events and laws of probability, independence, conditional probability, discrete random variables, Binomial and Poisson distributions, index numbers and time series (IBA prog. Ann )
Prerequisites: Business Maths, Remedial College Algebra

3. Recommended Text & Ref. Books:
Neil A. Weiss; Introductory Statistics, Addison Wesley (5th Edition)
Ronald E. Walpole (3rd. Ed.); Elements of Statistics & Probability
___________________________
Handouts by the instructor

4. Grading Plan 1. 3 quizzes (will consider best of 2) 10 marks
2. 2 Hourly/Term Exams
40 marks
3. Term Report (Based on projects & case studies)
4. Home assignments
5. Final Examination
30 marks
100 marks (total)

5. Course Outline Chapter 1 : Presentation of Data
Introduction, Types of Data, Quantitative, Qualitative Data. Tabulation of Data, frequency distributions, Intervals, limits and boundaries. Graphical Presentation, Bar Charts and histograms, Frequency polygons, Pie diagrams
Sessions required? _____

6. Course Outline Chapter 2 : Statistical Measures
Introduction and Notation, variable and summation notation. The Arithmetic mean, for a set, for a frequency distribution, the method of coding. The Median, mode and the geometric mean, quantiles, Elementary measures of dispersion. The range, mean deviation, standard deviation & variance. Exploratory Data Analysis, Moments and measures of skewness & kurtosis
Sessions required? _____

7. Course Outline Chapter 3 : Probability
Introduction, Elementary set theory, Experiments and Events, types of Events, Elementary probability. Conditional Probability & Independence, Baye’s Theorem
Sessions required? _____

8. Course Outline Chapter 4 : Random Variables
Discrete Random variables, Density functions. A probability distribution.
Mathematical Expectation, properties of the operator ‘E’, variance of random variable ‘X’, moments of probability distribution, moment generating function (MGF)
Sessions required? _____

9. Course Outline Chapter 5 :
Some special probability distributions
Introduction, related mathematics. The Binomial distribution, Poisson distribution, mean and variance of Binomial & Poisson distributions
Sessions required? _____

10. Course Outline Chapter 6 : Time Series & Index Numbers
Introduction, components of the time series, multiplicative & additive models. The trend exploration techniques, semi average technique, moving averages, method of least squares.Index numbers, price relatives, simple and multiple index numbers, value index, Laspeyre’s , Paasche’s and Fisher index
Sessions required? _____

11. Course Outline Computer Lab sessions
Introduction to MINITAB & SPSS (statistical packages), computing measures by using commands & MACRO programming

12. Thankyou 