## TYBSCstatitisticsSyllabus 1 Syllabus Mumbai University by munotes

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UNIVERSITY OF MUMBAI

Syllabus for the T.Y.B.Sc.

Programme: B.Sc.

Sem. V & Sem. VI

Course: STATISTICS

(As per Credit Based Choice System

with effect from the academic year 2018 –2019)

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T. Y. B. Sc. STATISTICS SYLLABUS

CREDIT BASED AND CHOICE SYSTEM

TO BE IMPLEMENTED FROM THE ACADEMIC YEAR 2018 -19

SEMESTER V

Theory

Course UNIT TOPICS Credits L ectures

USST501 I PROBABILITY

2.5 15

II INEQUALITIES AND LAW OF

LARGE NUMBERS 15

III JOINT MOMENT GENERATING

FUNCTION, TRINOMIAL AND

MULTINOMIAL DISTRIBUTION 15

IV ORDER STATISTICS 15

Course UNIT TOPICS Credits L ectures

USST502 I POINT ESTIMATION AND

PROPERTIES OF ESTIMATORS

2.5 15

II METHODS OF POINT ESTIMATION 15

III BAYESIAN ESTIMATION METHOD

& INTERVAL ESTIMATI ON 15

IV INTRODUC TION TO LINEAR

MODELS 15

Course UNIT TOPICS Credits L ectures

USST501 I EPIDEMIC MODELS

2.5 15

II BIOASSAYS 15

III CLINICAL TRIALS 15

IV CLINICAL TRIALS and

BIOEQUIVALENCE 15

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Course UNIT TOPICS Credits L ectures

USST50 4A

(Elective) I FUNDAME NTALS OF R

2.5 15

II SIMPLE LINEAR REGRESSION

MODEL 15

III MULTIPLE LINEAR REGRESSION

MODEL 15

IV VALIDITY OF ASSUMPTIONS 15

Course UNIT TOPICS Credits L ectures

USST50 4B

(Elective) I INTRODUCTION

2.5 15

II NUMPY, PANDAS AND DATA

EXPLORATION 15

III DESCRIPTIVE STATISTICS AND

STATISTICAL METHODS

15

IV INFERENTIAL STATISTICS 15

Course Practicals Credits Lectures per week

USSTP05 Practicals of course USST501+USST5 02 3 8

USSTP06 Pract icals of course USST503+USST5 04 3 8

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Course Code Title Credits

USST501 PROBABILITY AND DISTRIBUTION THEORY 2.5

Credits

(60 L ectures)

Unit I : PROBABILITY

(i) Basic definitions: Random Experiment, Outcome, Event, Sample Space,

Complementary, Mutually Exclusive, Exhaustive and Equally Likely Events .

(ii) Mathematical, Statistical, Axiomatic and Subjective probability.

(iii) Addition Theorem for (a) two (b) three events

(iv) Conditional Probability: Multiplication Theorem for two, three events.

(v) Bayes’ theorem.

(vi) Theorems on Probability of realization of :

(a) At least one (b) Exactly m (c) At least m of N events A 1, A2, A3…A N.

Classical occupancy problems, Matching and Guessing problems.

Problems based on them.

15 Lectures

Unit II : INEQUALITIES AND LAW OF LARGE NUMBERS

(i) Markov Inequ ality

(ii) Tchebyshev’s Inequality

(iii) Boole’s Inequality

(iv) Cauchy Schwartz’s Inequality

(v) Weak law of large numbers.

(Ref.9,10) 15 Lectures

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Unit III : JOINT MOMENT GENERATING FUNCTION ,

TRINOMAIL DISTRIBUTION AND MULTINOMIAL DISTRIBUTION

(i) Definition an d properties of Moment Generating Function (MGF) of two

random variables of discrete and continuous type. Necessary and Sufficient

condition for independence of two random variables.

Concept and definition of Bivariate MGF.

(ii) Trinomial distributi on

Definition of joint probability distribution of (X, Y). Joint moment generating

function, moments rs where r=0, 1, 2 and s=0, 1, 2.

Marginal & Conditional distributions. Their Means & Variances.

Correlation coefficient between (X, Y). Distribution of the Sum X+Y

Extension to Multinomial distribution with parameters (n, p1, p2,…pk -1)

where p1+ p2,+…pk -1+ pk = 1. Expression for joint MGF. Derivation of: joint

probability distribution of (Xi, Xj). Conditional probability distribution of Xi

15 Lectures

Unit IV : ORDER STATISTICS

(i) Definition of Order Statistics based on a random sample.

(ii) Derivation of:

(a) Cumulative distribution function of rth order statistic.

(b) Probability density functions of the rth order statistic.

(c) Joint Probability density function of the rth and the sth order statistic ( r~~ (d) Joint Probability density functions of all n ordered statistics. ~~

(e) Distribution of Maximum observation (nth order statistic) and Minimum

observation ( first order statistic) in case of uniform and Exponential distribution .

(f) Probability density function of the difference between rth and sth order

statistic ( r~~ (Ref.2,3,4) 15 Lectures ~~

REFERENCES

1. Feller W: An introduction to probability theory and it’s applications, Volume: 1, Third

edition, Wiley Eastern Limited.

2. Hogg R V. & Craig Allen T.: Introduction to Mathematical Statistics, Fifth edition, Pearson

Education (Singapore) Pvt. L td.

3. Mood A. M., Graybill F. A., Boes D. C.: Introduction to the theory of statistics, Third

edition, Mcgraw - Hill Series.

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4. Hogg R. V. and Tanis E.A. : Probability and Statistical Inference, Fourth edition, McMillan

Publishing Company.

5. Gupta S C & Kapoor V K: Fundamentals of Mathematical statistics, Eleventh edition, Sultan

Chand & Sons.

6. Biswas S.: Topics in Statistical Methodology, First edition, Wiley Eastern Ltd.

7. Kapur J. N. & Saxena H. C.: Mathematical Statistics, Fifteenth edition, S. Chand and

Compan y.

8. Chandra T.K. & Chatterjee D.: A First Course in Probability, Second Edition, Narosa

Publishing House.

9. S.C. Gupta and V.K.Kapoor : Fundamental of Mathematical Statistics,Sultan Chand and

Sons

10. V K Rohatgi: An Introduction to probability and Mathematical Statistics,

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Course

Code Title Credits

USST502 THEORY OF ESTIMATION 2.5

Credits

(60

Lectures)

Unit I : POINT ESTIMATION AND PROPERTIES OF ESTIMATORS

Notion of a Parameter and Parameter Space.

Problem of Point estimation.

Definitions : Statistic, Estimator and Estimate.

Properties of a good estimator :

1. Unbiasedness :Definition of an unbiased estimator,

Illustrations and examples.

Proofs of the following results:

(i) Two distinct unbiased estimators of U(

) give rise to

infinitely many unbiased estimators.

(ii) If T is an unbiased estimator of

then U(T) is an unbiased

estimator of U(

) provided U(

) is a linear function.

2. Consistency :Definition of Consistency.

Sufficient co ndition for consistency , proof & Illustrations

3. Sufficiency :Concept and Definition of sufficient statistic.

Neyman’s Factorization theorem (without proof). Exponential

family of probability distributions and sufficient statistics.

4. Relative efficiency of an estimator & illustrative examples . 15

Lectures

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Minimum variance unbiased estimator(MVUE) and Cramer Rao

Inequality:

1.Definition of MVUE

2. Uniqueness property of MVUE ( proof).

3. Fisher's information function

4.Regularity conditions.

5. Statement and proof of Cra mer-Rao inequality.

6. Cramer -Rao lower bound (CRLB), Efficiency of an estimator

using CRLB.

7. Condition when equality is attained in Cramer Rao Inequality and

its use in finding MVUE.

Ref. 1,3,8

UNIT II : METHODS OF POINT ESTIMATION

Method of Maximum Likelihood Estimation (M.L.E.) :

1. Definition of likelihood as a function of unknown parameter for a

random sample from: Discrete distributi on & Continuous distribution.

2. Derivation of Maximum likelihood estimator (M.L.E.) for

parameters of Standard distributions (case of one and two unknown

parameters).

3. Properties of MLE (without proof).

Method of Moments :

1. Derivation of M oment estimators for standard distributions (case

of one and two unknown parameters)

Illustrations of situations where MLE and Moment Estimators are

distinct and their comparison using Mean Square error.

Method of Minimum Chi -square and Modified Minimum C hi-

Square

Ref: 1,2,3 15

Lectures

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UNIT III: BAYESIAN ESTIMATION METHOD & INTERVAL

ESTIMATION

Bayes Estimation:

1. Prior distribution, Posterior distributio n

2. Loss function, Risk function

3. Types of Loss function: Squared error Loss function

(SELF) , Absolute error Loss function (AELF)

4. Bayes' risk.

5.Bayes' method of finding Point estimator (assuming

SELF)

Examples : (i) Binomial - Beta (ii) Poisson - Gamma

(iii) Gamma -Gamma (iv) Normal -Normal

Interval Estimation:

1. Concept of confidence interval & confidence limits.

2. Definition of Pivotal quantity and its use in obtaining confidence

limits.

3. Derivation of 100(1 -

% equal tailed confidence interval for :

( a)The population mean :

(population variance

known/ unknown) ( b) the population variance:

( Normal

distribution) . Confidence interval for the parameters of

Binomial, Poisson and Exponential distributions.

Ref. 1,2,3 15

Lectures

UNIT IV :INTRODUCTION TO LINEAR MODELS

Explanation of General Linear Model of full rank with

assumptions.

Model:

=

+ e where e

N (0,

I)

Derivation of : 1) Least squares estimator of β

2) E

3) V

15

Lectures

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Reference books:

1. HoggR.V., CraigA.T.: Introduction to Mathematical Statistics, Fourth Edition; Col lier

McMillan Publishers.

2. HoggR.V., TannisE. A.: Probability and Statistical Inference, Third Edition; Collier

McMillan Publishers.

3. Rohatgi, V. K, EhsanesSaleh A.K. Md.: An introduction to Probability Theory and

Mathematical Statistics, Second Edition, Wil ey series in Probability and Statistics.

4. John E. Freund’s Mathematical Statistics: I. Miller, M. Miller; Sixth Edition; Pearson

Education Inc.

5. HoelP.G.: Introduction to Mathematical Statistics; Fourth Edition; John Wiley & Sons

Inc.

6. GuptaS.C., KapoorV.K.: Fundamentals of Mathematical Statistics; Eighth Edition; Sultan

Chand & Sons.

7. KapurJ.N., SaxenaH.C.: Mathematical Statistics; Fifteenth Edition; S. Chand & Company

Ltd.

8. AroraSanjay and BansiLal : New Mathematical Statistics, SatyaPrakashan, New Market,

New Delhi,5(1989) GuassMarkoff theorem for full rank Model: Y = Xβ + e.

Derivation of : 1) E( l

2) V( l'

.

Confidence interval for l'

when

is known.

Confidence interval of

when

is known.

Ref. 9,10.

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9. A.M.Kshirsagar; Linear Models

10. F.A. Graybill; An Introduction to Linear Models

2Course Code Title Credits

USST503 BIOSTATISTICS 2.5 Credits

(60

lectures )

Unit I : EPIDEMIC MODELS

(i) The features of Epidemic spread. Definitions of va rious terms

involved. Simple mathematical models for epidemics:

Deterministic model without removals (for ‘a’ introductions),

Carrier model.

(ii) Chain binomial models. Reed -Frost and Greenwood models.

Distribution of individual chains and total number of cases .

Maximum likelihood estimator of `p’ and its asymptotic variance

for households of sizes up to 4.

(Ref. 1) 15 Lectures

Unit II : BIOASSAYS

(i) Meaning and scope of bioassays. Relative potency. Direct assays.

Fieller’s theorem.

(ii) Indirect assays. Dose -response relationship. Conditions of similarity

and Monotony. Linearizing transformations. Parallel line assays.

Symmetrical (2, 2) and (3, 3) parallel line assays. Validity tests

using orthogonal contrasts. Point Estimate and Interval Estimate

of Relative potency .

(iii)Quantal Response assays. Tolerance distribution. Median effective

dose ED50 and LD50. Probit and Logit analysis.

(Ref.2, 3) 15 Lectures

Unit III : CLINICAL TRIALS :

Introduction to clinical trials : The need and ethics of clinical trials.

Common termino logy used in clinical trials. Over view of phases (I -IV).

Introduction to ICH E9 guidelines, Study Protocol, Case record/Report

form, Blinding (Single/Double) Randomized controlled (Placebo/Active

controlled), Study Designs (Parallel, Cross Over).

Types of Trials : Inferiority, Superiority and Equivalence, Multicentric 15 Lectures

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Trial. Inclusion/Exclusion Criteria. Sample size estimation.

(Ref. 4, 5, 6, 7, 8)

Unit IV : CLINICAL TRIALS and BIOEQUIVALENCE :

Statistical tools : Analysis of parallel Design u sing Analysis of Variance.

Concept of odds ratio. Concept of Repeated Measures ANOVA. Survival

analysis for estimating Median survival time, Kaplan -Meire approach for

survival analysis.

BIOEQUIVALENCE :

Definitions of Generic Drug product. Bioavailability, Bioequivalence,

Pharmacokinetic (PK) parameters C max, AUC t , AUC 0-∞, Tmax, K el, Thalf.

Estimation of PK parameters using `time vs. concentration’ profiles.

Designs in Bioequivalence: Parallel, Cross over (Concept only).

Advantages of Crossover design over Parallel design. Analysis of Parallel

design using logarithmic transformation (Summary statistics, ANOVA

and 90% confidence interval).

Confidence Interval approach to establish bioequivalence (80/125 rule).

(Ref. 4, 5, 6, 7, 8, 9) 15 Lectures

REFERENCES :

1. Bailey N.TJ. : The Mathematical theory of infectious diseases, Second edition,

Charles Griffin and Co. London.

2. Das M.N. and Giri N.C. : Design and Analysis of Experiments, Second edition, Wiley

Eastern.

3. Finney D.J. : Statistical Methods in Biol ogical Assays, First edition, Charles Griffin

and Co. London.

4. Sanford Boltan and Charles Bon : Pharmaceutical Statistics, Fourth edition, Marcel

Dekker Inc.

5. Zar Jerrold H. :Biostatistical Analysis, Fourth edition, Pearson’s education.

6. Daniel Wayne W. : Bio statistics . A Foundation for Analysis in the Health Sciences,

7th Edition, Wiley Series in Probability and Statistics.

7. Friedman L. M., Furburg C., Demets D. L. : Fundamentals of Clinical Trials,

First edition, Springer Verlag.

8. Fleiss J. L. The Design and Analysis of Clinical Experiments, Second edition, Wiley

and Sons.

9. Shein -Chung -Chow ; Design and Analysis of Bioavailability & Bioequivalence

studies, Third Edition, Chapman & Hall/CRC Biostatistics series.

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Title Credits

USST50 4A Regre ssion Analysis using R software 2.5 Credits

(60 lectures )

Unit I : Fundamentals of R

Introduction to R features of R, installation of R, Starting and ending R

session, getting help in R , Value assigning to variables

Basic Operations : +, -, *, ÷, ^, sqrt

Numerical functions : log 10, log , sort, max, unique, range, length,

var, prod, sum,

summary, dim, sort, five num etc

Data Types : Vector, list, matrices, array and data frame

Variable Type : logical, numeric, integer, complex, character

and factor

Data Manipulation : Selecting random N rows, removing

duplicate row(s), dropping a variable(s),

Renaming variable(s), sub setting data,

creating a new variable(s), selecting of

random fraction of row(s), appending of

row(s) and column(s), simulation of

variables.

Data Process ing : Data import and export, setting working

directory, checking structure of Data

:Str(), Class(), Changing type of variable

(for eg as.factor, as.numeric)

Data Visualisation using ggplot: Simple bar diagram, subdivided bar

diagram, multiple bar diagram pie diagram,

Box plot for one and more variables,

histogram, frequency polygon, scatter plot 15 Lect ures

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eg plot()

(Ref.6, 7, 8, 9 ,10)

Unit II : Simple linear regression model

Assumptions of the model, Derivation of ordinary least square (OLS)

estimators of regression coefficients for simple, Properties of least square

estimators (without proof), Coefficient of determination R2 and adjusted R2 ,

Procedure of testing

a) Overall significance of the model s

b) Significance of individual coefficients

c) Confidence intervals for the regression coefficients

Data Pre -processing: Detection and treatment of missing value(s)and

outliers, Variab le selection and Model building, Interpretation of output

produced by lm command in R. Weighted Least Square Method, Polynomial

Regression Models.

(Ref. 1,2,3,4,5) 15 Lectures

Unit III : Multiple linear regression model

Derivation of ordinary least s quare (OLS) estimators of regression

coefficients for multiple regression models, Coefficient of determination R2

and adjusted R2 , Procedure of testing

a) Overall significance of the model s

b) Significance of individual coefficients

c) Confidence intervals for th e regression coefficients

Data Pre -processing: Detection and treatment of missing value(s) and

outliers, Variable selection and Model building, Interpretation of output

produced by lm command in R.

(Ref. 1,2,3,4,5) 15 Lectures

Unit IV : Validity of Assumptions

Residual Diagnostics: Standardized residuals, Studentized residuals, residual

plots, Interpretation of four plots of ,Interpretation output produced by plot

command in R and corrective measures such as t ransformation of respo nse

variable, testing normality of data .

Autocorrelation: Concept and detection using Durbin Watson Test,

Interpretation of output produced by DW -test function in R,

Heteroscedasticity: Concept and detection using Breusch –Pagan -Godfrey

Test, Interpretati on of output produced by bptest function in R,

Multicollinearity: Concept and detection using R2 and t -ratios ii) pairwise

correlation between repressors iii) Variance Inflation Factor(VIF),

Interpretation of output produced by mctest function in R,

Conseq uences of using OLS estimators in presence of Autocorrelation,

Heteroscedasticity and Multicollinearity, Remedial measures,

Ridge Regression : Concept and case study using R,

(Ref. 1,2,3,4,5) 15 Lectures

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References:

1) Draper, N. R. and Smith, H. (1 998), Applied Regression Analysis (John Wiley), Third

Edition.

2) Montgomery, D. C., Peck, E. A. and Vining, G. G. (2003), Introduction to Linear

Regression Analysis (Wiley).

3) Neter, J., W., Kutner, M. H. ;Nachtsheim , C.J. and Wasserman, W.(1996), Applied Line ar

Statistical Models, fourth edition, Irwin USA.

4) DamodarGujrati, Sangetha,Basic Econometrics, fourth edition, McGraw Hill Companies.

5) William Geene (1991), Econometrics Analysis, first edition, Mc Millan Publishing

Company.

6) Crawley, M. J. (2006 ). Statisti cs - An introduction using R. John Wiley, London

7) Purohit, S.G.; Gore, S.D. and Deshmukh, S.R. (2015). Statistics using R, second edition.

Narosa Publishing House, New Delhi.

8) Shahababa , B. (2011). Biostatistics with R, Springer, New York

9) Verzani, J. (2005 ). Using R for Introductory Statistics, Chapman and Hall /CRC Press,

New York

10) Asha Jindal (Ed.) (2018) , Analysing and Visualising Data with R software - A Practical

Manual, Shailja Prakashan, K.C.College.

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Title Credits

USST50 4B Statistical Data Analysis using PYTHON 2.5 Credits

(60 lectures )

Unit I : Introduction To PYTHON Software

Python Setup

Python Arithmetic

Basic Data Types

Variables

Lists

Tuples and Strings

Dictionaries and se ts

Ref: 1,2,3 15 Lectures

Unit II : Numpy, Pandas and Data Exploration

numpy arrays: Creating arrays crating n -dimensional arrays using

np.array and array operations(indexing and slicing, transpose,

mathematical operations)

pandas dataframes: Creating series and dataframes and Operations on

series and dataframes

Reading and writing data: From and to Excel and CSV files 15 Lectures

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Control statements: if, if -else, if -elif, while loop, for loop

Defining functions: def statement

Text data operations: len, upper, lower, slice, replace, contains

Frequency Tables

Ref: 1,2,3

Unit III : Descriptive statistics and Statistical Methods

Plotting: using “matplotlib”(Histograms, Box plots, Scatter plot, Bar

plot, Line plot)

Descript ive Statistics: mean, median, mode, min, max, quantile, std,

var, skew, kurt, correlation

Probability distributions: (using scipy.stats)

Simulation from distributions, computations of probabilities,

Cumulative probabilities, quan tiles and drawing random sample

using functions for following distributions:

Binomial, Poisson, Hypergeometric, normal, exponential, gamma,

Cauchy, Lognormal,Weibull, uniform, laplace ,Graphs of pmf/pdf by

varying parameters for above distributions and Fitting of

distributions ..

Ref: 1,2,3 15 Lectures

Unit IV : Inferential Statistics

Hypothesis testing and T -Tests: (using scipy.stats, math)ttest_1samp,

ttest_ind(2 sample test), ttest_rel(paired), Type I and Type II error

Chi-square tests: (us ing scipy.stats) chisquare, chi2

ANOVA: (using scipy.stats) f_oneway

Linear regression: from sklearn import linear model and use

linearmodel.linearregression function.

Ref: 1,2,3 15 Lectures

REFERENCES :

1. Python for Data Analysis by O’Reilly Media ( Second Edition)

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2. How to think like a computer scientist learning with Python by Allen Downey.

3. Python for Data Analysis by Armando Fernandgo

DISRIBUTION OF TOPICS FOR PRACTICALS

SEMESTER V

COURSE CODE USSTPO5 :

Sr. No. Practical topics from USST 501 Sr. No. Practical topics from USST 502

5.1.1 Probability -I 5.2.1 MVUE and MVBUE

5.1.2 Probability -II 5.2.2 Methods of Estimation

5.1.3 Inequalit ies and WLLN 5.2.3 Baye’s Estimaion

5.1.4 Trinomial and Multinomial

Distribution 5.2.4 Confidence Interval

5.1.5 Order statistics -I 5.2.5 Linear model

5.1.6 Order statistics -II 5.2.6 Use of R software

COURSE CODE USSTPO6 :

Sr.

No. Practical topi cs

from USST 503 Sr.

No. Practical topics from

USST 504A Sr. No. Practical topics from

USST 504B

5.3.1 Epidemic

Models 5.4A.1 Fundamentals of R 5.4B.1 Descriptive statistics

5.3.2 Direct Assays 5.4A.2 Graphs using R 5.4B.2 Correlations and

Simple Regres sion

5.3.3 Parallel Line

Assays 5.4A.3 Diagrams using R 5.4B.3 Probability

Distributions :Discrete

5.3.4 Quantal

Response

Assays 5.4A. 4 Simple Linear

Regression using R 5.4B.4 Probability

Distributions

:Continuous

5.3.5 Clinical Trials 5.4A.5 Weight ed Least

Square using R 5.4B.5 Statistical Test: t test

Chisquare and F test

5.3.6 Bioequivalance 5.4A.6 Multiple Linear

Regression and Ridge

Regression using R 5.4B.6 ANOVA

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T. Y. B. Sc. STATISTICS SYLLABUS

CREDIT BASED AND CHOICE SYSTEM

TO BE IMP LEMENTED FROM THE ACADEMIC YEAR 2018 -19

SEMESTER VI

Theory

COURSE UNIT TOPICS CREDITS LECTURES

USST601 I BIVARIATE NORMAL DISTRIBUTION

2.5 15

II GENERATING FUNCTIONS 15

III STOCHASTIC PROCESSES 15

IV QUEUING THEORY 15

USST602 I MOST POWERFUL T ESTS

2.5 15

II UNIFORMLY MOST POWERFUL &

LIKELIHOOD RATIO TESTS 15

III SEQUENTIAL PROBABILITY RATIO

TESTS 15

IV NON -PARAMETRIC TESTS 15

USST603 I LINEAR PROGRAMMING PROBLEM

2.5 15

II INVENTORY C ONTROL 15

III REPLACEMENT 15

IV SIMULATION AND RELIABILITY 15

USST604A

(Elective) I MORTALITY TABLES

2.5 15

II COMPOUND INTEREST AND

ANNUITIES CERTAIN 15

III LIFE ANNUITIES 15

IV ASSURANCE BENEFITS 15

USST604B

(Elective) I INTRODUCTION TO BASIC

STATISTICS

2.5 15

II SIX SIGMA 15

III CONTROL CHARTS I 15

IV CONTROL CHARTS II 15

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Course Practicals Credits Lectures per week

USSTP07 Practicals of course USST601+USST602 3 8

USSTP08 Practicals of course USST603+USST604 3 8

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Course Code Title Credits

USST601 DISTRIBUTION THEORY AND

STOCHASTIC PROCESSES

2.5 Credits

(60 lectures )

Unit I : BIVARIATE NORMAL DISTRIBUTION

(i) Definition of joint pr obability distribution (X, Y). Joint Moment

Generating

function, moments μ rs where r=0, 1, 2 and s=0, 1, 2. Marginal & Conditional

distributions. Their Means & Variances. Correlation coefficient between the

random variables. Necessary and sufficient condit ion for the independence of

X and Y.

Distribution of aX + bY, where ‘a’ and ‘b’ are constants.

(ii) Distribution of sample correlation coefficient when ρ = 0.Testing the

significance of a correlation coefficient. Fisher’s z – transformation.

Tests for i) H 0: ρ = ρ 0 ii) H 0: ρ1 = ρ 2, Confidence interval for ρ.

(Ref. 2,3,5,9) 15 Lectures

Unit II : GENERATING FUNCTIONS

Definitions of generating function and probability generating function.

Expression for mean and variance in terms of generating functions.

Definition of a convolution of two or more sequences. Generating function of

a convolution.

Generating functions of the standard discrete distributions. Relation between:

i) Bernoulli and Binomial distributions

ii) Geometric and Negative Binomial

distributions in te rms of convolutions. (Ref.1,5) 15 Lectures

Unit III : STOCHASTIC PROCESSES

Definition of stochastic process. Postulates and difference differential

equations for :

(i)Pure birth process, (ii)Poisson process w ith initially ‘a’ members, for a =0

and a >0, (iii)Yule Furry process, (iv)Pure death process, (v)Death process

with μ n=μ, (vi) Death process with μ n= nμ, (vii)Birth and death process,

(viii)Linear growth model.

Derivation of P n (t), mean and variance wh ere ever applicable. (Ref.1,7,9) 15 Lectures

Unit IV : QUEUING THEORY

Basic elements of the Queuing model.

Roles of the Poisson and Exponential distributions.

Derivation of Steady state probabilities for birth and death process. Steady

state probabil ities and various average characteristics for the following

models:

(i) (M/M/1) : (GD/ ∞ /∞) (ii) (M/M/1) : (GD/ N /∞)

(iii) (M/M/c) : (GD/∞/∞) (iv) (M/M/c) : (GD/ N /∞)

(v) (M/M/∞) : (GD/ ∞ /∞) (Ref.6) 15 Lectures

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REFERENCES:

1. Feller W: An intro duction to probability theory and it’s applications, Volume: 1, Third

edition, Wiley Eastern Limited.

2. Hogg R. V. & Craig A.T.: Introduction to Mathematical Statistics, Fifth edition, Pearson

Education (Singapore) Pvt Ltd.

3. Mood A M, Graybill F A, Bo se D C: Introduction to the theory of statistics, Third edition,

Mcgraw - Hill Series.

4. Hogg R. V. and Tanis E.A.: Probability and Statistical Inference, Fourth edition,

McMillan Publishing Company

5. Gupta S C & Kapoor V K: Fundamentals of Mathematical s tatistics, Eleventh edition, Sultan

Chand & Sons.

6. Taha H.A.: Operations Research: An introduction, Eighth edition, Prentice Hall of India Pvt.

Ltd.

7. Medhi J: Stochastic Processes, Second edition, Wiley Eastern Ltd.

8. Biswas S.: Topics in Statistical Methodology (1992), First edition, Wiley Eastern Ltd.

9. Kapur J. N., Saxena H. C.: Mathematical Statistics, Fifteenth edition, S. Chand and Company

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Course Code Title Credits

USST602 TESTING OF HYPOTHESIS 2.5 Credits

(60 lectures )

Unit I : MOST POWERFUL TESTS

Problem of testing of hypothesis.

Definitions and illustrations of i) Simple hypothesis ii) Composite

hypothesis iii)Null Hypothesis iv) Alternative Hypothesis v)Test of

hypothesis vi) Critical region vii) Type I and Type II erro rs viii) Level of

significance ix) p -value x) Size of the test xi) Power of the test xii)

Power function of a test xiii) Power curve.

Definition of most powerful test of size α for a simple hypothesis against

a simple alternative hypothesis. Neyman -Pearson fundamental lemma.

Randomised test ( Ref. 1,2,10) 15 Lectures

Unit II :UNIFORMLY MOST POWER FUL& LIKELIHOOD RATI O

TESTS

Definition, Existence and Construction of Uniformly most powerful

(UMP) test ( Ref. 1,2,10)

Likelihood ratio principle: Definition of test statistic and its asymptotic

distribution (statement only). Construction of LRT for the mean of

Normal distribution for (i) Known σ2 (ii) Unknown σ2(two sided

alternatives).LRT for variance of normal distribution for (i ) known µ (ii)

unknown µ (two sided alternatives hypothesis)

(Ref. 1,2,3,4) 15 Lectures

Unit III : SEQUENTIAL PROBABILITY RATIO TESTS

Sequential test procedure for testing a simple null hypothesis against a

simple alternativ e hypothesis. Its comparison with fixed sample size

(Neyman -Pearson) test procedure.

Definition of Wald’s SPRT of strength (α, β). Graphical /Tabular

procedure for carrying out SPRT. Problems based on Bernoulli,

Binomial. Poisson, Normal & Exponential distributions.

(Ref. 1,6,7,8) 15

Lectures

Unit IV : NON -PARAMETRIC TESTS

Need for non parametric tests.

Distinction between a parametric and a non parametric test.

Concept of a distribution free statistic. Single sample and two sample

Nonparametric tests. (i) Sign test (ii) Wilcoxon’s signed rank test (iii)

Median test (iv) Mann –Whitney test (v) Run test (vi) Fisher exact test

(vii) Kruskal -Wallis test (viii) Friedman test 15 Lectures

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Assumptions, justification of the test procedure for small & large samples

. (Ref.5,9)

REFERENCES :

1. Hogg R.V. and Craig A.T: Introduction to Mathematical Statistics, Fourth edition London

Macmillan Co. Ltd.

2. Hogg R.V. and Tanis E.A.: Probabil ity and Statistical Inference, Third edition Delhi Pearson

Education.

3. Lehmann, E. L: Testing of Statistical Hypothesis, Wiley &Sons

4. Rao, C. R.: Linear Statistical Inference and its applications, Second Edition Wiley Series in

Probability and Statistics.

5. Daniel W.W.:Applied Non Parametric Statistics, First edition Boston -Houghton Mifflin

Company.

6. Wald A.: Sequential Analysis, First edition New York John Wiley & Sons

7. Gupta S.C. and Kapoor V.K.: Fundamentals of Mathematical Statistics, Tenth edition New

Delhi S. Chand & Company Ltd.

8. Sanjay Aroraand BansiLal: New Mathematical Statistics, SatyaPrakashan, New Market, New

Delhi, 5(1989).

9. Sidney Siegal& N John Castellan Jr.:Non parametric test for behavioral sciences, McGraw

Hill c -1988

10. A. Mood , F. Gra ybill& D. Boes:Introduction to the theory of Statistics - McGraw Hill

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Course Code Title Credits

USST603 OPERATIONS RESEARCH TECHNIQUES 2.5 Credits

(60 lectures )

Unit I : LINEAR PROGRAMMING PROBLEM

Two-Phase Simplex Method, Algorithm.

Dual Simplex Method, Algorithm. Post Optimality Sensitivity Analysis.

Effect on optimal solution to the LPP and improvement in the solution due to

(i) Change in cost coefficient, (ii)Change in the element of r equirement vector,

(iii) Addition/deletion of a variable,(iv) Addition/deletion of a constraint.

(All expressions without proof) (Ref. 2, 3) 15 Lectures

Unit II : INVENTORY CONTROL

Introduction to Inventory Problem

Deterministic Models :

Single item static EOQ models for

(i) Constant rate of demand with instantaneous replenishment, with and

without shortages.

(ii) Constant rate of demand with uniform rate of replenishment, with and

without shortages.

(iii)Constant rate of demand with instantaneous replenishment without

shortages, with at most two price breaks.

Probabilistic models : Single period with

(i) Instantaneous demand (discrete and continuous) without setup cost.

(ii) Uniform demand (disc rete and continuous) without set up cost.

(Ref. 1, 2, 3) 15 Lectures

Unit III : REPLACEMENT

Replacement of items that deteriorate with time and value of money (i) remai ns

constant, (ii) changes with time.

Replacement of items that fail completely : Individual replacement and Group

replacement policies. (Ref. 3) 15 Lectures

Unit IV : SIMULATION AND RE LIABILITY

Concept and Scope of simulation. Monte Carlo Technique of

Simulation.Generation of random numbers using (i) Mid. Square Method and (ii)

Multiplicative Congruential Method. Inverse method of ge neration of random

observations from (i) Uniform distribution, (ii) Exponential distribution, (iii)

Gamma distribution, (iv) Normal distribution. Simulation techniques applied to

inventory and queueing model. (Ref. 1, 4)

RELIABILITY: Concept of reliability, Hazard -rate. Bath tub curve.

Failure time distributions : (i) Exponential, (ii) Gamma, (iii) Weibull, (iv) 15 Lectures

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Gumbel, Definitions of increasing (decreasing) failure rate.System Reliability.

Reliability of (i) series ; (ii) parallel system of independent components having

exponential life distributions. Mean Time to Failure of a system (MTTF).

(Ref. 5,6)

REFERENCES :

1. Vora N. D. : Quantitative Techniques in Management, Third edition, McGr aw Hill

Companies.

2. Kantiswarup, P.K. Gupta, Manmohan: Operations Research, Twelfth edition, Sultan Chand

& sons.

3. Sharma S. D. : Operations Research, Eighth edition, Kedarnath Ramnath & Co.

4. Taha Hamdy A. : Operations Research : Eighth edition, Prentice Hall of India Pvt. Ltd.

5. Barlow R. E. and Prochan Frank : Statistical Theory of Reliability and Life Testing Reprint,

First edition, Holt, Reinhart and Winston.

6. Mann N. R., Schafer R.E ., Singapurwalla N. D.: Methods for Statistical Analysis of

Reliability and L ife Data. First edition, John Wiley & Sons.

## Page 28

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Course Code Title Credits

USST604A ACTURIAL SCIENCE 2.5 Credits

(60 lectures )

Unit I: MORTALITY TABLES

Various mortality functions. Probabilities of living and dying. The force of

mortality . Estimation of μx from the mortality table. Central Mortality Rate.

Laws of mortality: Gompertz’s and Makeham’s first law. Select, Ultimate

and Aggregate mortality tables. Stationary population. Expectation of life

and Average life at death. (Ref.2,3) 15 Lectures

Unit II: COMPOUND INTEREST AND ANNUITIES CERTAIN

Accumulated value and present value, nominal and effective rates of int erest.

Varying rates of interest. Equation of value. Equated time of payment.

Present and accumulated values of annuity certain (immediate and due) with

and without deferment period. Present value for perpetuity (immediate and

due) with and without deferme nt Period. Present and accumulated values of

(i) increasing annuity (ii) increasing annuity when successive instalments

form arithmetic progression (iii) annuity with frequency different from that

with which interest is convertible. Redemption of loan. (Re f.2 ) 15 Lectures

Unit III: LIFE ANNUITIES

Present value in terms of commutation functions of Life annuities and

Temporary life annuities (immediate and due) with and without deferment

period. Present values of Variable, increasing life annuities and increasing

Temporary life annuities (immediate and due). (Ref.1,2 ) 15 Lectures

Unit IV: ASSURANCE BENEFITS

Present value of Assurance benefits in terms of commutation functions of :

(i) pure endowment assurance (ii) temporary assurance (iii) endow ment

assurance (iv) whole life assurance (v) double endowment assurance (vi)

special endowment assurance (vii) deferred temporary assurance. Net

premiums: Net level annual premiums (including limited period of payment)

for various assurance plans .Natura l and Office premiums. (Ref.1,2 ) 15 Lectures

## Page 29

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REFERENCES :

1. Neill A. : Life Contingencies, First edition, Heineman educational book s London

2. Dixit S.P., Modi C.S., Joshi R.V.: Mathematical Basis of Life Assurance, First edition

Insurance Institute of India.

3. Gupta S. C. &. Kapoor V. K.: Fundamentals of Applied Statistics, Fourth edition,

Sultan Chand & Sons.

## Page 30

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Course Code Title Credits

USST604B INTRODUCTION TO SIX SIGMA 2.5 Credits

(60 lectures )

Unit I : INTRODUCTION TO BASIC STATISTICS

Descriptive Statistics, Data Distribution, Skewness, Kurtosis, Box and

Whisker plots, Inferential Statistics (Sample, Population, Normal

Distri bution, CLT theorem, Sampling distribution of mean), Hypothesis

testing with Normal and Non -Normal data : [ 1 and 2 sample tests, 1 sample

variance, One way ANOVA, Mann -Whitney U test, Kruskal -Wallis test,

Moods median test, Chi -square test ], Regression ana lysis, Designed

experiments. (Ref. 1,2) 15 Lectures

Unit II : SIX SIGMA

History and concept, Basic Principles, Goals, six sigma v/s TQM, ISO 9000,

Traditional Management, Quality de fined, VOC and CTQ, Quality

measurement to six sigma, Seven tools of quality and its application: 1)

Histogram or Stem and Leaf display. 2) Check sheet. 3) Pareto Chart. 4)

Cause and Effect diagram (Fish bone Diagram) 5) Defect concentration

diagram. 6) Scatter diagram. 7) Control charts ( Only concept of control

chart ), DMAIC with case study, introduction to Lean Six Sigma.

(Ref. 3,4,5,6,7,8,9,10) 15 Lectures

Unit III : CONTROL CHARTS I

Introduction, Chance and assignable causes, Statistical basis of th e control

chart: Basic principles of control chart (Shewhart control charts), Choice of

control limits, Sample size and sampling frequency, Rational subgroups,

Analysis of patterns on control charts, Discussion of sensitizing rules for

control chart. Intr oduction to the concept of attribute, Defect. P, np, c and u

charts, their uses. p -chart with variable sample size. Operating -Characteristic

function, Average run length. Applications of variable control charts. (In

addition problems involving setting up s tandards for future use is also

expected), Guidelines to implement control charts. (In addition problems

involving setting up standards for future use is also expected) . Ref.

11,12,13,14,15,16) 15 Lectures

Unit IV : CONTROL CHARTS II

Control chart for var iables variables. X -Bar, R, S [ sample standard deviation ]

(Construction, charts based on standard values, Interpretation), Operating -

Characteristic function, Average run length. Applications of variable control

charts.

Introduction to process capability co ncept, Specification limits, natural 15 Lectures

## Page 31

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References:

1. Fundamental of Mathematical Statistics, Gupta and Kapoor.

2. Probability and Random process by T. Veerarajan.

3. Six Sigma For Business Excellence, (200 5), Penelope Przekop, McGraw -Hill

Six Sigma Handbook, by Pyzdek , McGraw Hill Education; 4 edition (1 July 2017).

4. The Certified Six Sigma Green Belt Handbook , Roderick A. Munro and Govindarajan

Ramu , American Society for Quality (ASQ),

5. What Is Design For Six Sigma,(2005), Roland Cavana gh, Robert Neuman, Peter Pande,

Tata McGraw -Hill

6. The Six Sigma Way: How GE, Motorola, And Other Top Companies Are Honing Their

Performance, (2000), Peter S. Pande, Robert P. Neuman, Roland R. Cavanagh, McGraw -

Hill

7. What Is Lean Six Sigma,(2004), Mike Georg e, Dave Rowlands, Bill Kastle, McGraw -

Hill

8. Six Sigma Deployment,(2003), Cary W. Adams, Charles E Wilson Jrs, Praveen

Gupta, Elsevier Science.

9. Six Sigma For Beginners: Pocket Book (2018), Rajiv Tiwari Kindle Edition

10. Introduction to Statistical Quality Control(2009), Montgomery, Douglas, C ,Sixth

Edition, John Wiley & Sons.Inc.:.

11. Statistical Quality Control: E.L.Grant. 2nd edition, McGraw Hill, 1988.

12. Quality Control and Industrial Statistics: Duncan. 3rd edition, D.Taraporewala sons &

company.

13. Quality Control: Theory and Applications: Bertrand L. Ha nsen, (1973),Prentice Hall of

IndiaPvt. Ltd..

14. Introduction to Statistical Quality Control(2009), Montgomery, Douglas, C. , Sixth

Edition, John Wiley & Sons, Inc.:

15. Quality Control (1976), I.V. Burr, Mardekkar, New York,

16. Fundamentals of Applied Statistics , Gupta and Kapoor

tolerance limits and their comparisons , estimate of percent defectives,

Capability ratio and Capability indices(Cp), Capability performance indices

Cpk with respect to machine and process interpretation, relationship between

i) Cp and Cpk

ii) Defective parts per million and Cp

(Ref. 11,12,13,14,15,16)

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DISRIBUTION OF TOPICS FOR PRACTICALS

SEMESTER VI

COURSE CODE USSTPO7 :

Sr. No. Practical topics from USST601 Sr. No. Practical topics from USST602

6.1.1 Bivariate Normal Disribution 6.2.1 Testing of Hypothesis - I

6.1.2 Tests for correlation and Interval

estimation 6.2.2 Testing of Hypothesis - II

6.1.3 Generating Function 6.2.3 SPRT

6.1.4 Stochastic Process 6.2.4 Non-parametric Test - I

6.1.5 Queuing Theory - I 6.2.5 Non-parametric Test - II

6.1.6 Queuing Theory - II 6.2.6 Use of R software

COURSE CODE USSTPO8 :

Sr.

No. Practical

topics from

USST603 Sr.

No. Practical topics from

USST604A Sr.

No. Practical topics from

USST604B

6.3.1 L.P.P. 6.4A.1 Mortality table I 6.4B.1 Descriptive statistics

6.3.2 Invent ory I 6.4A.2 Mortality table II 6.4B.2 Testing of hypothesis

6.3.3 Inventory II 6.4A.3 Annuities I 6.4B.3 Seven Tools of Quality

6.3.4 Replacement 6.4A.4 Annuities II 6.4B.4 Attribute control charts

6.3.5 Simulation 6.4A.5 Life Annuities 6.4B.5 Variable Control Charts

and Capability Analysis

## Page 33

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6.3.6 Reliability 6.4A.6 Assurance benefits 6.4B.6 Practical based on

1,2,3,4,5 using MS -Excel

Semester End Examination:

Theory: At the end of the semester, Theory examination of three hours duration and 10 0 marks

based on the four units shall be held for each course.

Pattern of Theory question paper at the end of the semester for each course:

There shall be Five compulsory questions of twenty marks each with internal option.

Question 1 based on Unit I.

Question 2 based on Unit II.

Question 3 based on Unit III.

Question 4 based on Unit IV.

Question 5 based on all Four Units combined.

Semester End Examination Practicals : At the end of the semester, Practical examination of 3

hours duration and 100 ma rks (80+10*+10**) shall be held for each course as shown below:

Practical Course Part A Part B Duration Marks out of

USSTP05 Questions from

USST501 Questions from

USST502 3 hours 80

USSTP06 Questions from

USST503 Questions from

USST504 3 hours 80

USSTP 07 Questions from

USST601 Questions from

USST602 3 hours 80

USSTP08 Questions from

USST603 Questions from

USST604 3 hours 80

*: Practical journal 10 marks, **: Viva 10 marks

Pattern of practical question paper at the end of the semester for each course:

Every paper will consist of two parts A and B. Every part will consist of two questions o f 40

marks each. Students to attempt one question from each part.

Guidelines for conducting University examination of Paper on Statistical software at

T.Y. B.Sc. Sem ester V

a. The examination will be conducted in Statistics laboratory on computers.

b. Provision of at least 15 computers with necessary R / Python / MSExcel

software installed should be made available by the centre. Battery backup in

case of power failure is essential.

c. Duration of examination is one and hal hours.

d. The examination will be conducted batch wise. A batch will consist of at most 15

## Page 34

33

candidates.

e. The batches examined simultaneously will have same question paper. However

there will be separate que stion paper for each batch in case more (than one)

batches are required to be formed.

f. A candidate will solve the question paper given to him/ her on computer and the

output of work done by him/her will be evaluated by the examiner.

g. In case of partial powe r failure proportionate additional time may be given at

that centre for the concerned batch.

h. One internal examiner and one external examiner will be appointed for this

examination.

Workload Theory: 4 lectures per week per course. Practicals: 4 lecture periods per course per

week per batch. All four periods of the practicals shall be conducted in succession t ogether on a

single day.

***************************************************************************