Expected Learning Outcomes:
1) Enable learners to know descriptive statistical concepts
2) Enable study of probability concept required for Computer learners
Data types : attribute, variable, discrete and continuous variable
Data presentation : frequency distribution, histogram o give, curves, stem and leaf
Measures of Central tendency: Mean, Median, mode for raw data, discrete, grouped
Measures dispersion: Variance, standard deviation, coefficient of variation for raw
data, discrete and grouped frequency distribution, quartiles, quantiles Real life
Moments: raw moments, central moments, relation between raw and central
Measures of Skewness and Kurtosis: based on moments, quartiles, relation between
mean, median, mode for symmetric, asymmetric frequency curve.
Correlation and Regression: bivariate data, scatter plot, correlation, nonsense
correlation, Karl pearson’s coefficients of correlation, independence.
Linear regression: fitting of linear regression using least square regression, coefficient
of determination, properties of regression coefficients (only statement)
Probability : Random experiment, sample space, events types and operations of
Probability definition : classical, axiomatic, Elementary Theorems of probability
0 ≤ P(A) ≤ 1,
P(A B) = P(A) + P(B) - P(A B)
P (A’) = 1 - P(A)
P(A) ≤ P(B) if A B
Conditional probability, ‘Bayes’ theorem, independence, Examples on Probability
1. Trivedi, K.S.(2001) : Probability, Statistics, Design of Experiments and Queuing theory, with applications
of Computer Science, Prentice Hall of India, New Delhi
1. Ross, S.M. (2006): A First course in probability. 6th Edⁿ Pearson
2. Kulkarni, M.B., Ghatpande, S.B. and Gore, S.D. (1999): common statistical tests.
Satyajeet Prakashan, Pune
3. Gupta, S.C. and Kapoor, V.K. (1987): Fundamentals of Mathematical Statistics,
S. Chand and Sons, New Delhi
4. Gupta, S.C. and Kapoor, V.K. (1999): Applied Statistics, S. Chand and Son’s, New Delhi
5. Montgomery, D.C. (2001): Planning and Analysis of Experiments, wiley.