(36) (A) STATISTICS (GENERAL) : 1 Paper
(B) STATISTICS (SPECIAL) : 2 Papers
( 1 Theory, 1 Practical)
(37) MATHEMATICAL STATISTICS (GENERAL) : 1 Paper
(38) APPLIED STATISTICS (GENERAL) : 1 Paper
(40) STATISTICAL PRE-REQUISITES : 1 Paper
(GENERAL)
( TO BE EFFECTIVE FROM 2010-2011 )
2
UNIVERSITY OF PUNE
Revised Syllabus of
(36) STATISTICS
(General and Special)
Note : (1) A student of the Three-Year B.A. Degree Course offering 'Statistics' at
the special level must offer `Mathematical Statistics' as a General level subject in all
the three years of the course.
Further students of the three-year B.A. Degree Course are advised not to
offer 'Statistics' as the General level subject unless they have offered
'Mathematical Statistics' as a General level subject in all the three years of the
course.
(2) A student of three-year B.A. Degree Course offering 'Statistics' will not be
allowed to offer 'Applied Statistics' in any of the three years of the course.
(3) A student offering `Statistics' at the Special level must complete all
practicals in Practical Paper to the satisfaction of the teacher concerned.
(4) He/She must produce the laboratory journal along with the
completion certificate signed by the Head of the Department at the time
of Practical Examination.
(5) Structure of evaluation of practical paper at T.Y.B.A
(A) Continuous Internal Evaluation Marks
(i) Journal
(ii) Viva-voce
10
10
Total (A) 20
(B) Annual practical examination
Section Nature Marks Time
I Examination using computer:
Note : Question is compulsory
Q1 : MSEXCEL : Execute the commands
and write the same in answer book along
with answers
10 Maximum
20 minutes
II Using Calculator
Note : Attempt any two of the following four
questions : Q2 : Q3 : Q4 : Q5 :
60 2 hours
40 minutes
III Viva-voce 10 10 minutes
Total (B) 80 3 Hours and
10 minutes
Total of A and B 100 ---
3
(6) Duration of the practical examination be extended by 10 minutes to
compensate for the loss of time for viva-voce of the candidates.
(7) Batch size should be of maximum 12 students.
(8) To perform and complete the practicals, it is necessary to have computing
facility. So there should be sufficient number of computers, UPS and
electronic calculator in the laboratory.
(9) In order to acquaint the students with applications of statistical methods in
various fields such as industries, agricultural sectors, government institutes,
etc. at least one Study Tour for T.Y. B.A. Statistics students must be arranged.
4
36 (A) STATISTICS (GENERAL)
Title : Design of Experiments and Operations Research
First Term
DESIGN OF EXPERIMENTS
1. Design of Experiments (20 L)
Basic terms of design of experiments : Experimental unit,
treatment , layout of an experiment.
Basic principles of design of experiments : Replication,
randomization and local control.
Choice of size and shape of a plot for uniformity trials, the
empirical formula for the variance per unit area of plots.
Analysis of variance (ANOVA ): concept and technique.
Completely Randomized Design (CRD) : Application of the
principles of design of experiment in CRD, Layout, Model:
(Fixed effect )
Xij = μ + αi + εij i= 1,2, …….t, j = 1,2,………., ni
assumptions and interpretations. Testing normality by pp plot.
Breakup of total sum of squares into components. Estimation of
parameters, expected values of mean sums of squares,
components of variance, preparation of analysis of variance
(ANOVA) table, testing for equality of treatment effects, linear
treatment contrast, Hypothesis to be tested
H0 : α1= α2 = … = αt = 0 and interpretation, comparison of
treatment means using box plot technique. Statement of
Cochran’s theorem.
F test for testing H0 with justification (independence of chisquares
is to be assumed), test for equality of two specified
treatment effects using critical difference (C.D.).
1.6 Randomized Block Design (RBD) : Application of the principles of
design of experiments in RBD, layout, model:
Xij = μ + αi + βj +εij i= 1,2, …….t, j = 1,2,………. b,
Assumptions and interpretations.
Breakup of total sum of squares into components. Estimation of
parameters, expected values of mean sums of squares, components
5
of variance , preparation of analysis of variance table, Hypotheses to
be tested H01 : α1= α2 = α3 = …= αt= 0
H02 : β1= β2 = β3 = …= βb= 0
F test for testing H01 and H02 with justification (independence of chisquares
is to be assumed), test for equality of two specified
treatment effects using critical difference(C.D.).
1.7 Latin Square Design (LSD): Application of the principles of design
of experiments in LSD, layout, Model :
Xij(k) = μ + αi + βj +γk+εij(k) i = 1,2, ···m, j = 1,2···,m, k=1,2···,m.
Assumptions and interpretations.
Breakup of total sum of squares into components. Estimation of
parameters, expected values of mean sums of squares, components
of variance , preparation of analysis of variance table, hypotheses to
be tested
H01 : α1= α2 = ··· = αm= 0
H02 : β1= β2 = ··· = βm= 0
H03 : γ1= γ 2 = ··· = γ m= 0 and their interpretation.
Justification of F test for H01, H02 and H03 (independence of chisquares
is to be assumed). Preparation of ANOVA table and F test
for H01, H02 and H03 , testing for equality of two specified treatment
effects, comparison of treatment effects using critical difference,
linear treatment contrast and testing its significance.
1.8 Analysis of non- normal data using
i) Square root transformation for counts,
ii) Sin-1(.) transformation for proportions,.
iii) Kruskal Wallis H test.
1.9 Identification of real life situations where the above designs are
used.
2. Efficiency of Design (5L)
2.1 Concept and definition of efficiency of a design.
2.2 Efficiency of RBD over CRD.
2.3 Efficiency of LSD over CRD and RBD.
3. Split Plot Design (4L)
3.1 General description of a split plot design.
3.2 Layout and model.
3.3 Analysis of variance table for testing significance of main effects
and interactions.
4. Factorial Experiments (12L)
4.1 General description of mn factorial experiment, 22 and 23 factorial
6
experiments arranged in RBD.
4.2 Definitions of main effects and interaction effects in 22 and 23
factorial experiments.
4.3 Yates’ procedure, preparation of ANOVA table, test for main effects
and interaction effects.
4.4 General idea of confounding in factorial experiments.
4.5 Total confounding (confounding only one interaction), ANOVA
table, testing main effects and interaction effects.
4.6 Partial confounding (confounding only one interaction per replicate),
ANOVA table, testing main effects and interaction effects.
Construction of layouts in total confounding and partial confounding
in 22 and 23 factorial experiments.
5. Analysis of Covariance (ANOCOVA) with One Concomitant
Variable (7L)
5.1 Situations where analysis of covariance is applicable.
5.2 Model for covariance in CRD, RBD. Estimation of parameters
(derivations are not expected)
5.3 Preparation of analysis of variance -covariance table, test for ( β=0),
test for equality of treatment effects (computational technique only).
Note : For given data, irrespective of the outcome of the test of
regression coefficient (β), ANOCOVA should be carried out.
Second Term
OPERATIONS RESEARCH
6. Linear programming (20L)
6.1 Statement of the linear Programming Problem (LPP), Formulation of problem
as L.P. problem.
Definition of (i) A slack variable, (ii) A surplus Variable.
L.P. Problem in (i) Canonical form ,(ii) standard form.
Definition of i) a slack variable, ii)a surplus variable. L.P. problem in
i) canonical form, ii) standard form.
Definition of i) a solution , ii) a feasible solution, iii) a basic feasible solution,
iv) a degenerate and non –generate solution, v) an optimal solution , vi) basic
and non- basic variables .
7
6.2 Solution of L.P.P. by
i) Graphical Method : convex set solution space , unique and non-unique
solutions , obtaining an optimal solution, alternate solution, infinite solution,
no solution, unbounded solution, sensitivity analysis.
ii) Simplex Method:
a) initial basic feasible solution (IBFS) is readily available : obtaining an
IBFS, criteria for deciding whether obtained solution is optimal,
criteria for unbounded solution , no solution , more than one solution .
b) IBFS not readily available: introduction of artificial variable, Big-M
method, modified objective function, modifications and application of
simplex method to L.P.P. with artificial variables.
6.3 Duality Theory: Writing dual of a primal problem, solution of a L.P.P. by using
its dual problem.
6.4 Examples and problems.
7. Transportation and assignment problems (16L)
7.1 Transportation problem (T.P.), statement of T.P., balanced and unbalanced T.P.
7.2 Methods of obtaining basic feasible solution of T.P. i) North-West corner rule ii)
Method of matrix minima (least cost method), iii) Vogel’s approximation method
(VAM).
7.3 u-v method of obtaing Optimal solution of T.P., uniqueness and non-uniqueness
of optimal solutions, degenerate solution.
7.4 Assignment problems: statement of an assignment, balanced and unbalanced
problem, relation with T.P., optimal solution of an assignment problem.
7.5 Examples and problems.
8. Sequencing (6L)
8.1 Statement of sequencing problem of two machines and n-jobs, three machines
and n- jobs (reducible to two machines and n-jobs).
8.2 Calculation of total elapsed time, idle time of a machine, simple numerical
problems.
8.3 Examples and problems.
8
9. Simulation (6L)
9.1 Introduction to simulation, merits, demerits, limitations.
9.2 Pseudo random number generators: Linear congruential generator, mid square
method.
9.3 Model sampling from normal distribution ( using Box- Muller transformation),
uniform and exponential distributions.
9.4 Monte Carlo method of simulation.
9.5 Applications of simulation in various fields.
9.6 Statistical applications of simulation in numerical integration, queuing theory
etc.
Note: Verify the solutions using TORA package.
Books Recommended
1. Federer, W.T. : Experimental Design : Oxford and IBH Publishing Co.,
New Delhi.
2. Cochran W.G. and Cox, C.M. : Experimental Design, John Wiley and
Sons, Inc., New York.
3. Montgomery , D.C.: Design and Analysis of Experiments, and sons, Inc.,
New York.
4. Dass, M.N. and Giri,N.C. : Design and Analysis of Experiments, Wiley
Eastern Ltd., New Delhi.
5. Goulden G.H. : Methods of Statistical Analysis, Asia Publishing House
Mumbai
6. Kempthhorne, O: Design Analysis of Experiments.
Wiley Eastern Ltd., New Delhi.
7. Snedecor, G.W. and Cochran, W.G. : Statistical Methods, Affiliated East –
West Press, New Delhi.
8. Goon Gupta,Dasgupta : Fundamentals Of Statistics, Vol.II, The world Press
Pvt. Ltd. Calcutta.
9. Gupta S.C. and Kapoor V.K.: Fundamentals of Applied Statistics, S.Chand
Sons, New Delhi.
10. C.F. Jeff Wu, Michael Hamda: Experiments, Planning, Analysis and
Parameter Design Optimization.
11. G.W. Snedecor , W.G. Cochran : Statistical Methods 8th edition, Eastern
Press, Delhi ( for 1.8)
9
12. Miller and Freund : Probability and Statistics for engineers, Pearson
Education, Delhi ( for 1.8)
13. Gass,E.: Linear programming method and applications,Narosa Publishing
House, New Delhi.
14. Taha, R.A.: Operation research, An Introduction, fifth edition, Prentice Hall
of India, New Delhi.
15. Saceini,Yaspan,Friedman : Operation Research methods and problems,
Willey International Edition.
16. Shrinath.L.S : Linear Programming ,Affiliated East-West Pvt. Ltd , New
Delhi.
17. Phillips,D.T, Ravindra , A, Solberg, I.: Operation Research principles and
practice , John Willey and sons Inc.
18. Sharma, J.K.: Mathematical Models in Operation Research , Tata McGraw
Hill Publishing Company Ltd., New Delhi.
19. Kapoor,V.K.: Operations Research , Sultan Chand and Sons. New Delhi.
20. Gupta, P.K.and Hira , D.S.: Operation Research, S.Chand and company Ltd.,
New Delhi.
10
36 (B) STATISTICS (SPECIAL)
S-1 :PAPER I : DISTRIBUTION THEORY
FIRST TERM
1. Multinomial Distribution (10 L)
P(X1= x1, X2= x2, · · · ,Xk=xk) = ! ! !
!
1 2
1 2
1 2
k
x
k
x x
x x x
n p p p k
L
K
,
xi= 0,1,2 · · · , n; i=1,2 · · · ,k
x1+x2+· · · xk= n
p1+p2+· · · + pk= 1
0< pi< 1, i=1,2 · · · ,k
= 0 ,elsewhere.
Notation : (X1, X2, · · · ,Xk) ~ MD(n, p1, · · · , pk) , X~ MD (n, p )
where, X = (X1, X2, · · · , Xk) , P = ( k p , p , , p 1 2
K )
1.1 Joint MGF of (X1, X2, · · · , Xk)
1.2 Use of MGF to obtain means, variances, covariances, total correlation
coefficients, multiple and partial correlation coefficients for k= 3,
univariate marginal distributions, distribution of Xi+Xj, Conditional
distribution of Xi given Xi+Xj= r
1.3 Variance – covariance matrix, rank of variance – covariance matrix and its
interpretation.
1.4 Real life situations and applications.
2. Beta distribution (8L)
2.1 Beta distribution of first kind
p.d.f.
( , )
1
( )
B m n
f x = xm-1 ( 1-x)n-1, 0≤x ≤1, m,n >0
= 0 , elsewhere
Notation : X~ B1 (m,n)
11
Nature of probability curve, mean, variance, properties, rth raw moment,
harmonic mean.
2.2 Relation with U(0,1), if X and Y are iid B1(1,1) the probability
distributions of
,
1
X
X+Y, X-Y, XY, ,
Y
X
2.3 Beta distribution of second kind
p.d.f. m n
m
x
x
B m n
f x +
-
+
=
(1 )
.
( , )
1
( )
1
, x ≥ 0, m,n >0
= 0, elsewhere,
Notation : X~ B2 (m,n)
Nature of probability curve, mean, variance, properties, rth raw moment,
harmonic mean.
2.4 Interrelation between B1(m,n) and B2 (m,n).
2.5 Distribution of
X Y
X
Y
X
+
, etc. when X and Y are independent gamma
variates.
2.6 Relation between distribution function of B1(m,n) and binomial
distribution.
2.7 Real life situations and applications.
3. Weibull distribution (6 L)
p.d.f
1
( )
-
=
b
a a
b x
f x exp
-
b
a
x
x ≥ 0, α , β>0
= 0, elsewhere
Notation : X~ W(α , β).
Probability curve, location parameter, shape parameter, scale parameter,
Distribution function, quartiles, mean and variance, coefficient of variation,
relationship with gamma and exponential distribution, Hazard rate, IFR,DFR
property.
Real life situations and applications.
12
4. Order Statistics (10 L)
Order statistics for a random sample of size n from a continuous distribution,
definition, derivation of distribution function and density function of the i-th order
statistics X(i) particular cases for i=1 and i=n. Distribution of X(i) for random
sample from uniform and exponential distributions.
Derivation of joint p.d.f. of (X(i), X(j)), distribution function of the sample
range X(n)-X(1).
Distribution of the sample median.
If X1, X2 ……Xn are i.i.d. uniform r.v.s then Corr ( X(i), X(j)), distribution of
X(n) - X(1) and sample median. Comment on unbiased estimator of θ for U(0, θ) and
exponential(θ) based on order statistics.
5. Chebychev’s inequality (6L)
5.1 Chebychev’s theorem : If g (x) is a non – negative function of r.v. X such
that E[g(X)] < ∞, then P[g(X) ³ k]£ E[g(X)]/ k ,where k is positive
real number.
5.2 Chebychev’s inequality for discrete and continuous distributions in the
forms
[ ] 2
2
k
P X k
-m ³ £ s , k>1 and [ ] 2
1
k
P X -m ³ ks £
where μ= E(X) and σ2= Var (X) <∞.
5.3 Applications of Chebychev’s inequality in control charts, statistical
inference.
6. Central Limit Theorem and Weak Law of Large Numbers (8 L)
6.1 Sequence of r.v.s. , convergence of sequence of r.v. in a) probability
b) distribution with simple illustrations.
6.2 Statement and proof of the central limit theorem for i.i.d. r.v.s. (proof
based on MGF).
6.3 Weak law of large numbers (WLLN).
6.4 Applications of CLT and WLLN.
13
Second Term
7. Cauchy distribution (6L)
7.1 p.d.f. 2 2 ( )
1
( )
p l m
l
+ -
=
x
f x - ∞<x<∞, - ∞<μ<∞,λ>0,
= 0, elsewhere
Notation : X~ C (μ,λ).
7.2 Nature of the probability curve.
7.3 Distribution function, quartiles, non – existence of moments, distribution
of aX + b. Distribution of
X
1 , X2 for X~ C (0,1)
7.4 Additive property for two independent Cauchy variates (statement only),
statement of distribution of the sample mean, comment on limiting
distribution of
-X
.
7.5 Relationship with uniform , Student’s t and normal distribution.
7.6 Applications of C(μ,λ).
8. Laplace ( double exponential) distribution (6 L)
8.1 p.d.f. exp ( )
2
f (x) = l -l x -m , - ∞ < x < ∞, - ∞<μ<∞, λ>0,
= 0, elsewhere
Notation : X~ L (μ,λ).
8.2 Nature of the probability curve.
8.3 Distribution function, quartiles, comment on MLE of μ, λ.
8.4 MGF, CGF, moments and cumulants, β1, β2, γ1, γ2
8.5 Laplace distribution as the distribution of the difference of two i.i.d.
exponential variates with mean
l
1 .
8.6 Applications and real life situations.
9. Lognormal distribution (8L)
9.1 p.d.f.
( )s 2p
1
( )
x a
f x
-
= [ ]
- - - 2
2 log ( )
2
1
exp m
s
x a e , a <x , -¥ < μ < ∞, σ > 0,
= 0 , elsewhere
Notation : X~ LN (a ,μ,σ2).
9.2 Nature of the probability curve.
9.3 Moments (r- th moment of X-a), first four moments , β1 and γ1
14
coefficients, quartiles, mode.
9.4 Relation with N (μ,σ2) distribution.
9.5 Distribution of P Xi , Xi’s independent lognormal variates.
9.6 Applications and real life situations
10. Truncated distributions (8L)
10.1 Truncated distribution, truncation to the right, left and on both sides.
10.2 Binomial distribution B(n,p) left truncated at X=0, (value zero is
discarded), its p.m.f. , mean, variance.
10.3 Poisson distribution P(m), left truncated at X=0 ,(value zero is
discarded),its p.m.f. mean, variance.
10.4 Normal distribution N (μ,σ2) truncated i) to the left below a ii) to the
right above b iii) to the left below a and to the right above b , (a < b) its
p.d.f. and derivation of mean and statement (without derivation) of
variance in all the three cases.
10.5 Real life situations and applications.
11. Bivariate normal distribution (10L)
11.1 p.d.f. of a bivariate normal distribution.
2
1 2 2 1
1
( )
ps s - r
f x =
-
-
-
-
+
-
-
-
2
2
1
1
2
2
2
2
1
1
2 2
2(1 )
1
exp
s
m
s
m
r
s
m
s
m
r
x y x y
- ∞<x, y < ∞,
- ∞ < μ1, μ2 < ∞,
σ1, σ2 > 0, -1 < ρ < +1
= 0 , elsewhere
Notation : (X,Y)~ BN (μ1, μ2 , , 2 ,
2
2
1 s s ρ ), X ~ Np (μ , Σ), use of matrix
algebra is recommended.
11.2 Nature of surface of p.d.f. ,marginal and conditional distributions,
identification of parameters, regression of Y on X and of X on Y,
independence and uncorrelated- ness, MGF and moments. Distribution
of aX +bY +c , X/Y.
11.3 Applications and real life situations
12. Finite markov chains (10L)
12.1 Definition of a sequence of discrete r.v.s. , Markov property, Markov
chain, state space and finite Markov chain (M.C.), one step and n step
transition probability, stationary transition probability,
stochastic matrix P, one step and n step transition probability matrix
15
(t.p.m.) Chapman – Kolmogorov equations, t.p.m. of random walk and
gambler’s ruin problem.
12.2 Applications and real life situations.
Books Recommended
1. H. Cramer : Mathematical Methods of Statistics, Asia Publishing House,
Mumbai.
2. Mood, A.M. Graybill, F.Bose, D.C : Introduction to Theory(IIIrd Edition )
Mc-Graw Hill Series.
3. B.W. Lindgren : Statistical Theory (IIIrd Edition) Collier Macmillan
International Edition, Macmillan Publishing Co.Inc. NewYork.
4. Hogg, R.V. and Craig A.T. : Introduction to Mathematical Statistics ( IIIrd
Edition), Macmillan Publishing Company, Inc.866 34d Avenue, New York,
10022.
5. Sanjay Arora and Bansi Lal : New Mathematical Statistics (Ist Edition ),
Satya Prakashan16/17698, New Market, New Delhi,5(1989).
6. S.C. Gupta and V. K. Kapoor : Fundamentals of Mathematical Statistics,
Sultan
Chand and Sons, 88, Daryaganj, New Delhi, 2.
7. V.K. Rohatgi : An Introduction to Probability Theory and Mathematical
Statistics, Wiley Eastern Ltd. New Delhi.
8. J. Medhi: Stochastic Processes, Wiley Eastern Ltd…. New Delhi.
9. Hoel, Port and Stone, Introduction to Stochastic Processes, Houghton
Miffin.
10. Feller W. : An Introduction of Probability Theory and Its Applications, Vol.
I, Wiley Eastern Ltd. Mumbai.
11. Sheldon Ross: A first course in probability ( 6th edition) : Pearson
Education, Delhi.
16
36 (B) STATISTIC (SPECIAL)
S2 : PAPER II : PRACTICALS
Sr No. Title of the Experiment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Fitting of lognormal distribution.
Fitting of lognormal distribution using MS- EXCEL/SPREAD SHEET
Fitting of truncated binomial and truncated Poisson distributions
Fitting of truncated binomial and truncated Poisson distributions
using MS-EXCEL/SPREAD SHEET
Applications of truncated binomial, truncated Poisson
and truncated normal distributions
Model sampling from Laplace and Cauchy distributions
(also using MS-Excel / Spread sheet)
Applications of bivariate normal distribution
Maximum likelihood estimation
Testing of hypotheses (UMP test)
Construction of confidence interval for proportions, difference of
proportions
Construction of confidence interval for mean, difference of two
means from normal population
Construction of confidence interval for variance, ratio of
two variances from normal population
Nonparametric Tests I (Mann-Whitney. Run test (one
sample and two samples) , Median test)
Nonparametric Tests II (Kolmogorov - Smirnov Test)
Sequential probability ratio test for Bernoulli and Poisson distributions
(Graphical and Tabular procedure)
Sequential probability ratio test for normal and exponential distributions
(Graphical and tabular procedure)
17
17 Solution of LPP by simplex method
18 Solution of LPP by using its dual
19 Transportation problem
20 Analysis of CRD and RBD
21 Analysis of LSD and efficiency
22 Analysis of 23 factorial experiments arranged in RBD
23 A n a l y s i s o f t o t a l a n d p a r t i a l c o n f o u n d i n g i n 2 3factorial experiments
in RBD
24 Analysis of covariance in CRD and RBD
Note :
(1) Computer printouts are to be attached with journal for Experiment
Nos. 2,4 and 6.
(2) MS-EXCEL/SPREAD SHEET examination is to be conducted using computer
as per note no. (5).
Laboratory Equipments: Laboratory should be well equipped with sufficient
number of electronic calculators and computers along with necessary software,
printers and UPS.
18
37 ) MATHEMATICAL STATISTICS (GENERAL)
STATISTICAL INFERENCE
Note:
1) Mathematical Statistics can be offered only as a general level subject.
2) A student of three Year B.A. Degree Course offering Mathematical Statistics
will not be allowed to offer Applied Statistics in any of the three years of the
course.
First Term
THEORY OF ESTIMATION
1. Point Estimation (4L)
Notion of a parameter, parameter space , general problem of estimating an
unknown parameter by point and interval estimation.
Point Estimation : Definition of an estimator, distinction between estimator
and estimate , illustrative examples.
2. Methods of Estimation (12L)
Definition of likelihood as a function of unknown parameter for a random
sample from i) discrete distribution ii) continuous distribution, distinction
between likelihood function and p.d.f./ p.m.f.
Method of maximum likelihood: Derivation of maximum likelihood
estimator (M.L.E.) for parameters of only standard distributions ( case of two
unknown parameters only for normal distribution). Use of iterative procedure to
derive M.L.E. of location parameter μ of Cauchy distribution. Invariance property
of M.L.E.
a) M.L.E. of θ in uniform distribution over i) (0, θ) ii)(- θ, θ) iii) (mθ, nθ)
b) M.L.E. of θ in f(x ; θ)= Exp {-(x- θ)}, x > θ.
19
Method of Moments : Derivation of moment estimators for standard
distributions. Illustrations of situations where M.L.E. and moment estimators are
distinct and their comparison using mean square error.
3. Properties of Estimator (20L)
Unbiasedness : Definition of an unbiased estimator, biased estimator, positive
and negative bias, illustrations and examples( these should include unbiased and
biased estimators for the same parameters). Proofs of the following results
regarding unbiased estimators:
a) Two distinct unbiased estimators of j (θ) give rise to infinitely many
estimators.
b) If T is an unbiased estimator of θ , then j (T) is unbiased estimator of
j (θ ) provided j (.) is a linear function.
Notion of the Best Linear Unbiased Estimator and uniformly minimum
variance unbiased estimator (UMVUE), uniqueness of UMVUE whenever it
exists.
Sufficiency: Concept and definition of sufficiency, statement of Neyman’s
factorization theorem (Proof for discrete probability distribution). Pitmann –
Koopman form and sufficient statistic; Exponential family of probability
distributions and sufficient statistic.
Proofs of the following properties of sufficient statistics.
i) If T is sufficient for θ, then j (T) is also sufficient for θ provided j
is a one to one and onto function.
ii) If T is sufficient for θ then T is also sufficient for j (θ).
iii) M.L.E. is a function of sufficient statistic.
Fisher information function: amount of information obtained in a
statistic
T = T(x1,x2, …, xn). Statement regarding information in (x1,x2,…., xn) and
in a sufficient statistics T.
Cramer- Rao Inequality : Statement and proof of Cramer-Rao inequality,
Cramer – Rao Lower Bound(CRLB), definition of minimum variance
bound unbiased estimator (MVBUE) of ø (θ) . Proofs of following results:
a) If MVBUE exists for θ then MVBUE exists for j (θ) where j (.) is a
linear function.
b) If T is MVBUE for θ then T is sufficient for θ.
3.6 Efficiency : Comparison of variance with CRLB, relative efficiency of T1
w.r.t. T2 for (i) unbiased (ii) biased estimators. Efficiency of unbiased
estimator T w.r.t. CRLB.
20
4. Asymptotic Behaviour of an Estimator (6L)
4.1 Consistency : Definition , proof of the following theorems:
a. An estimator is consistent if its bias and variance both tend to
zero as the sample size tends to infinity.
b. If T is consistent estimator of θ and φ (.) is a continuous function,
then φ (T) is a consistent estimator of φ (θ).
4.2 Consistency of M.L.E.( Statement only).
5. Interval Estimation (6L)
5.1 Notion of interval estimation, definition of confidence interval (C.I.), length
of C.I., confidence bounds. Definition of pivotal quantity and its use in
obtaining confidence intervals.
5.2 Interval estimation for the following cases.
i) Mean (μ) of normal distribution (σ2 known and σ2 unknown).
ii) Variance (σ2) of normal distribution (μ known and μ unknown).
iii) Median, quartiles using order statistics.
Second Term
TESTING OF HYPOTHESES
6. Parametric Tests (15 L)
6.1 (a) Statistical hypothesis, problem of testing of hypothesis.
Definition and illustrations of (1) simple hypothesis, (2) composite
hypothesis, (3) test of hypothesis, (4) critical region, (5) type I and
type II errors. probabilities of type I error and type II error.
Problem of controlling the probabilities of errors of two kinds.
(b)Definition and illustrations of (i) level of significance, (ii)
observed level of significance (p-value), (iii) size of a test, (iv)
power of a test.
6.2 Definition of most powerful(M.P.) level α test of simple null
hypothesis against simple alternative. Statement of Neyman -
Pearson (N-P) lemma for constructing the most powerful level α
test of simple null hypothesis against simple alternative hypothesis.
Illustrations of construction of most powerful level α test.
21
6.3 Power function of a test, power curve, definition of uniformly most
powerful (UMP) level α test for one sided alternative.
Illustrations.
7. Likelihood ratio test : (9 L)
Notion of likelihood ratio test (LRT), λ (x)=Sup L(θ0|x) / Sup L(θ|x)
Construction of LRT for H0 : θ ε Q against H1: θ Ï Q for the
mean of normal distribution for i) known σ2 ii) unknown σ2 with
one tailed and two tailed hypotheses, LRT for variance of normal
distribution for i) known μ ii) unknown μ with one tailed and two
tailed hypotheses, LRT for parameter of binomial, of exponential etc.
two tailed alternative hypotheses, distribution. LRT as a function of
sufficient statistics , statement of asymptotic distribution of
-2 loge λ (x).
8. Sequential Tests (9 L)
Sequential test procedure for simple null hypothesis against simple
alternative hypothesis and its comparison with fixed sample size N-P
test procedure. Definition of Wald's SPRT of strength (α, β).
Illustration for standard distributions like Bernoulli, Poisson, Normal
and Exponential. SPRT as a function of sufficient statistics. Graphical
and tabular procedure for carrying out the test.
9. Nonparametric Tests (15 L)
9.1 Idea of non parametric problems. Distinction between a parametric and
a non-parametric problem. Concept of distribution free statistic. One
tailed and two tailed test procedure of (i) Sign test, ii) Wilcoxon
signed rank test (iii) Mann- Whitney U test, (iv) Run test, one sample
and two samples problems.
9.2 Empirical distribution function Sn (x). Properties of Sn (x) as estimator
of F (.). Kolmogorov – Smirnov test for completely specified
univariate distribution (one sample problem only) with two sided
alternative . Comparison with chi-square test.
22
Books recommended
1. Lindgren, B.W.: Statistical Theory (third edition) collier Macmillan
International Edition, Macmillan publishing Co., Inc. New York.
2. Mood, A.M., Graybill, F. and Bose, D.C. : Introduction to the theory of
Statistics (third edition) International Student Edition, McGraw Hill Kogakusha
Ltd.
3. Hogg, P.V. and Craig, A.J. : Introduction to Mathematical Statistics (fourth
edition), Collier Macmillan International Edition, Macmillan Publishing Co.
Inc., New York.
4. Siegel, S. : Nonparametric methods for the behavioural sciences, International
Student Edition, McGraw Hill Kogakusha Ltd.
5. Hoel, Port, Stone : Introduction to statistical Theory, Houghton Mifflin
Company (International) Dolphin Edition.
6. J.D. Gibbons : Non parametric Statistical Interence, McGraw Hill Book
Company, New York.
7. Daniel : Applied Nonparametric Statistics, Houghton Mifflin Company,
Roston.
8. V.K. Rohatgi : An introduction to probability theory and mathematical
statistics, Wiley Eastern Ltd., New Delhi.
9. Kendall and stuart : The advanced Theory of Statistics, Vol 1, Charles and
company Ltd., London.
10. Dudeweitz and Mishra : Modern Mathematical Statistic, John Wiley and Sons,
Inc., New York.
11. Kale, B.K. : A First Course In parametric Inference.
12. Kunte, S ., Purohit, S.G. and Wanjale, S.K. : Lecture Notes On Nonparametric
Tests.
13. B.L. Agarwal : Programmed Statistics: New Age International Publications ,
Delhi.
14. Sanjay Arora and Bansi Lal : New Mathematical Statistics (Ist Edition ), Satya
Prakashan16/17698, New Market, New Delhi,5(1989).
23
(38) APPLIED STATISTICS (GENERAL)
APPLICATIONS OF STATISTICS
Note :
(1) Applied Statistics can be offered only as a General Level subject.
(2) A Student of the Three Year B.A. Degree course offering
Applied Stat ist ics wi l l not be al lowed to of fer Mathemat ical
Stat ist ics and/or Statistics in any of the three years of the course.
First term
1. Continuous type distributions (12L)
1.1 Definition of continuous type of r.v. through p.d.f.,
Definition of distribution function of continuous type r.v.
Statement of properties of distribution function of continuous type r.v.s
1.2 Normal distribut ion p.d. f .
Standard normal distribut ion, s tatement of propert ies of normal
di s t r ibut ion, the graph of p.d.f., nature of probability curve.
Computat ion of probabi l i t ies.
1.3 Examples and problems
2. Tests of significance (18L)
2.1 Notion of a statistic as a function T(X1, X2 ,..., Xn) and its illustrations.
2.2 Sampling distribution of T(X1, X2 ,..., Xn). Notion of standard error of
a statistic.
2.3 Notion of hypothesis, critical region, level of significance.
24
2.4 Tests based on normal distribution for
(i) H0 : μ= μ 0 against H1 : μ ≠ μ 0 , μ<μ 0 , μ> μ 0
(ii) H0 : μ1= μ2 H1 : μ 1≠ μ 2 , μ 1 < μ2 , , μ 1 > μ2
(iii) H0 : P= P0 against H1 : P ≠ P 0 , P < P 0 , P > P 0
(iv) H0 : P1= P2 against H1 : P1 ≠ P 2 ,P1< P 2 , P1> P 2
(v) H0 : r= r 0 against H1 : r ≠ r 0 , r<r 0 , r> r 0
2.5
Examples and problems.
3. Tests based on t, chi-square and F distributions (18L)
3.1 t tests for
(i) H0 : μ= μ 0 against H1 : μ ≠ μ 0 , μ<μ 0 , μ> μ 0
(ii) H0 : μ1= μ2 H1 : μ 1≠ μ 2 , μ 1 < μ2 , , μ 1 > μ2
(iii) Paired observations
3.2 Tests for H0 : σ2= σ0
2against H1 : σ2 ≠ σ0
2 , σ2 <σ0
2 , σ2 >σ0
2
3.3 Chi square test of goodness of fit.
3.4 Chi square test for independence of attributes: Chi square test for
independence of 2 x 2 contingency- table (without proof).
Yate's correction not expected.
3.5 Tests for H0 : σ1
2 = σ2
2 against H1 : σ1
2 ≠ σ2
2 , σ1
2 < σ2
2 , σ1
2 >σ2
2
Second Term
4. An a l y s i s o f v a r i a n c e t e ch n i qu e s ( 1 2L)
4.1 Concept of analysis of variance.
4.2 One-way and two –way classification : break up of total sum of squares, analysis
of variance table, test of hypotheses of (i) equality of several means,
(ii) equality of two means.
4.3 Numerical problems.
25
5.
No n - p a r ame t r i c t e s t s ( 1 0 L)
5.1 Distinction between a parametric and non-parametric problem-
5.2 Concept of distribution free statistic.
5.3 One tailed and two tailed test procedure of
(a) Sign test, (b) Wilcoxon's signed rank test.
5.4 Test for randomness.
6. St a t i s t i c a l qua l i t y co n t r o l ( 2 6L)
6.1 Meaning and purpose of statistical quality control.
6.2
Control chart: Chance and assignable causes of quality variations,
statistical basis of control chart (connection with test of hypothesis is
NOT expected). Control limits (3-sigma limits only). Criteria for
judging lack of control:
(i) One or more points outside the control limits and
(ii) Non-random variations within the control limits : such as a run
of seven or more points on either side of the control line, presence of trend
or cycle.
6.3
Control charts for variables: Purpose of R-chart and Xchart,
construction of R-chart, X-chart when standards are not given. Plotting the
simple mean and ranges on Xand R charts respectively. Necessity for
plotting R-chart. Revision of R-chart. Drawing conclusion about state of
process. Revision of Xchart. Control limits for future production.
6.4
Control chart for fraction defective (p-chart) only for fixed sample
size. Determination of central line, control limits on p-chart, plotting
of sample fraction defectives on p-chart. Revision of p-chart,
determination of state of control of the process and interpretation of high
and low spots on p-chart. Estimation of central line and control limits
for future production.
6.5 Control chart for number of defects per unit (c-chart)
Construction of c chart when standards are not given. Plotting of number
of defects per unit on c-chart, determination of state of control of the
process, revision of control limits for future production.
6.6 Numerical problems based on control charts.
26
6.7 Identification of real life situations where these charts can be used.
Note: (i) Proof or derivations of results are not expected.
(ii) Stress should be given on numerical problems.
Books Recommended
1. Larson H.J.: Introduction to Probability Theory and Statistical
Applications, A Wiley International Edition.
2. Meyer, P. L. Introductory Probabi lity Theory and Statistical
Applications, Addison-Wesley Publishing Company.
3. Hoel, P. G.: Introduction. of Mathematical Statistics, John Wiley and
Sons Co. New York.
4. Walpole Introduction to Statistics, Macmillan Publishing Co. New
York.
5. Lipschutz: Probability and Statistics.
6. Goon, Gupta and Dasgupta : Fundamental’s of Statistics, Vol. I, The
World Press Pvt. Ltd. Calcutta.
7. New mark, J. : Introduction to Statistics.
8. Miller and Freund: Modern Elementary Statistics.
9. Gupta, S. P. : Statistical Methods, Sultan Chand and Sons, Delhi.
10. Gupta and Kapoor : Fundamental s of Appl ied Stat i st ics ,
Sultan Chand and Sons, Delhi.
27
(40) STATISTICAL PRE-REQUISITES
The course in Statistical Pre-requisites may be offered only by candidates offering
one of the social Sciences as their special subject at the B.A. Degree Examination.
The course in Statistical Pre-requisites can not be offered by those who offer any of
the Courses in Statistics Groups for their B.A. Examination
(1)
First Term
Correlation and Regression
(2) Partial correlation
(3) Multiple correlation
(4) Index numbers
(5) Time series
Second Term
(6) Statistical process control
(7) Acceptance sampling
(8) Data analysis for production function estimation
(9) Economic specialization of the production function
(10) Miscellaneous empirical problems relating to the estimation of production
function
Books Recommended
1. Larson7 H.J.: Introduction to Probability Theory and Statistical
Applications, A Wiley International Edition.
2. Meyer, P. L. Introductory Probabili ty Theory and Statistical Applications,
Addison-Wesley Publishing Company.
3. Hoel, P. G.: Introduction. of Mathematical Statistics, John Wiley and
Sons Co. New York.
4. Walpole Introduction to Statistics, Macmillan Publishing Co. New
York.
5. Goon, Gupta and Dasgupta : Fundamental’s of Statistics, Vol. I, The World
Press Pvt. Ltd. Calcutta.
6. New mark, J. : Introduction to Statistics.
7. Miller and Freund: Modern Elementary Statistics.
8. Gupta, S. P. : Statistical Methods, Sultan Chand and Sons, Delhi.
9. Gupta and Kapoor : Fundamental s of Appl ied Stat is t ics ,
Sultan Chand and Sons, Delhi.
28
29
(B) STATISTICS (SPECIAL) : 2 Papers
( 1 Theory, 1 Practical)
(37) MATHEMATICAL STATISTICS (GENERAL) : 1 Paper
(38) APPLIED STATISTICS (GENERAL) : 1 Paper
(40) STATISTICAL PRE-REQUISITES : 1 Paper
(GENERAL)
( TO BE EFFECTIVE FROM 2010-2011 )
2
UNIVERSITY OF PUNE
Revised Syllabus of
(36) STATISTICS
(General and Special)
Note : (1) A student of the Three-Year B.A. Degree Course offering 'Statistics' at
the special level must offer `Mathematical Statistics' as a General level subject in all
the three years of the course.
Further students of the three-year B.A. Degree Course are advised not to
offer 'Statistics' as the General level subject unless they have offered
'Mathematical Statistics' as a General level subject in all the three years of the
course.
(2) A student of three-year B.A. Degree Course offering 'Statistics' will not be
allowed to offer 'Applied Statistics' in any of the three years of the course.
(3) A student offering `Statistics' at the Special level must complete all
practicals in Practical Paper to the satisfaction of the teacher concerned.
(4) He/She must produce the laboratory journal along with the
completion certificate signed by the Head of the Department at the time
of Practical Examination.
(5) Structure of evaluation of practical paper at T.Y.B.A
(A) Continuous Internal Evaluation Marks
(i) Journal
(ii) Viva-voce
10
10
Total (A) 20
(B) Annual practical examination
Section Nature Marks Time
I Examination using computer:
Note : Question is compulsory
Q1 : MSEXCEL : Execute the commands
and write the same in answer book along
with answers
10 Maximum
20 minutes
II Using Calculator
Note : Attempt any two of the following four
questions : Q2 : Q3 : Q4 : Q5 :
60 2 hours
40 minutes
III Viva-voce 10 10 minutes
Total (B) 80 3 Hours and
10 minutes
Total of A and B 100 ---
3
(6) Duration of the practical examination be extended by 10 minutes to
compensate for the loss of time for viva-voce of the candidates.
(7) Batch size should be of maximum 12 students.
(8) To perform and complete the practicals, it is necessary to have computing
facility. So there should be sufficient number of computers, UPS and
electronic calculator in the laboratory.
(9) In order to acquaint the students with applications of statistical methods in
various fields such as industries, agricultural sectors, government institutes,
etc. at least one Study Tour for T.Y. B.A. Statistics students must be arranged.
4
36 (A) STATISTICS (GENERAL)
Title : Design of Experiments and Operations Research
First Term
DESIGN OF EXPERIMENTS
1. Design of Experiments (20 L)
Basic terms of design of experiments : Experimental unit,
treatment , layout of an experiment.
Basic principles of design of experiments : Replication,
randomization and local control.
Choice of size and shape of a plot for uniformity trials, the
empirical formula for the variance per unit area of plots.
Analysis of variance (ANOVA ): concept and technique.
Completely Randomized Design (CRD) : Application of the
principles of design of experiment in CRD, Layout, Model:
(Fixed effect )
Xij = μ + αi + εij i= 1,2, …….t, j = 1,2,………., ni
assumptions and interpretations. Testing normality by pp plot.
Breakup of total sum of squares into components. Estimation of
parameters, expected values of mean sums of squares,
components of variance, preparation of analysis of variance
(ANOVA) table, testing for equality of treatment effects, linear
treatment contrast, Hypothesis to be tested
H0 : α1= α2 = … = αt = 0 and interpretation, comparison of
treatment means using box plot technique. Statement of
Cochran’s theorem.
F test for testing H0 with justification (independence of chisquares
is to be assumed), test for equality of two specified
treatment effects using critical difference (C.D.).
1.6 Randomized Block Design (RBD) : Application of the principles of
design of experiments in RBD, layout, model:
Xij = μ + αi + βj +εij i= 1,2, …….t, j = 1,2,………. b,
Assumptions and interpretations.
Breakup of total sum of squares into components. Estimation of
parameters, expected values of mean sums of squares, components
5
of variance , preparation of analysis of variance table, Hypotheses to
be tested H01 : α1= α2 = α3 = …= αt= 0
H02 : β1= β2 = β3 = …= βb= 0
F test for testing H01 and H02 with justification (independence of chisquares
is to be assumed), test for equality of two specified
treatment effects using critical difference(C.D.).
1.7 Latin Square Design (LSD): Application of the principles of design
of experiments in LSD, layout, Model :
Xij(k) = μ + αi + βj +γk+εij(k) i = 1,2, ···m, j = 1,2···,m, k=1,2···,m.
Assumptions and interpretations.
Breakup of total sum of squares into components. Estimation of
parameters, expected values of mean sums of squares, components
of variance , preparation of analysis of variance table, hypotheses to
be tested
H01 : α1= α2 = ··· = αm= 0
H02 : β1= β2 = ··· = βm= 0
H03 : γ1= γ 2 = ··· = γ m= 0 and their interpretation.
Justification of F test for H01, H02 and H03 (independence of chisquares
is to be assumed). Preparation of ANOVA table and F test
for H01, H02 and H03 , testing for equality of two specified treatment
effects, comparison of treatment effects using critical difference,
linear treatment contrast and testing its significance.
1.8 Analysis of non- normal data using
i) Square root transformation for counts,
ii) Sin-1(.) transformation for proportions,.
iii) Kruskal Wallis H test.
1.9 Identification of real life situations where the above designs are
used.
2. Efficiency of Design (5L)
2.1 Concept and definition of efficiency of a design.
2.2 Efficiency of RBD over CRD.
2.3 Efficiency of LSD over CRD and RBD.
3. Split Plot Design (4L)
3.1 General description of a split plot design.
3.2 Layout and model.
3.3 Analysis of variance table for testing significance of main effects
and interactions.
4. Factorial Experiments (12L)
4.1 General description of mn factorial experiment, 22 and 23 factorial
6
experiments arranged in RBD.
4.2 Definitions of main effects and interaction effects in 22 and 23
factorial experiments.
4.3 Yates’ procedure, preparation of ANOVA table, test for main effects
and interaction effects.
4.4 General idea of confounding in factorial experiments.
4.5 Total confounding (confounding only one interaction), ANOVA
table, testing main effects and interaction effects.
4.6 Partial confounding (confounding only one interaction per replicate),
ANOVA table, testing main effects and interaction effects.
Construction of layouts in total confounding and partial confounding
in 22 and 23 factorial experiments.
5. Analysis of Covariance (ANOCOVA) with One Concomitant
Variable (7L)
5.1 Situations where analysis of covariance is applicable.
5.2 Model for covariance in CRD, RBD. Estimation of parameters
(derivations are not expected)
5.3 Preparation of analysis of variance -covariance table, test for ( β=0),
test for equality of treatment effects (computational technique only).
Note : For given data, irrespective of the outcome of the test of
regression coefficient (β), ANOCOVA should be carried out.
Second Term
OPERATIONS RESEARCH
6. Linear programming (20L)
6.1 Statement of the linear Programming Problem (LPP), Formulation of problem
as L.P. problem.
Definition of (i) A slack variable, (ii) A surplus Variable.
L.P. Problem in (i) Canonical form ,(ii) standard form.
Definition of i) a slack variable, ii)a surplus variable. L.P. problem in
i) canonical form, ii) standard form.
Definition of i) a solution , ii) a feasible solution, iii) a basic feasible solution,
iv) a degenerate and non –generate solution, v) an optimal solution , vi) basic
and non- basic variables .
7
6.2 Solution of L.P.P. by
i) Graphical Method : convex set solution space , unique and non-unique
solutions , obtaining an optimal solution, alternate solution, infinite solution,
no solution, unbounded solution, sensitivity analysis.
ii) Simplex Method:
a) initial basic feasible solution (IBFS) is readily available : obtaining an
IBFS, criteria for deciding whether obtained solution is optimal,
criteria for unbounded solution , no solution , more than one solution .
b) IBFS not readily available: introduction of artificial variable, Big-M
method, modified objective function, modifications and application of
simplex method to L.P.P. with artificial variables.
6.3 Duality Theory: Writing dual of a primal problem, solution of a L.P.P. by using
its dual problem.
6.4 Examples and problems.
7. Transportation and assignment problems (16L)
7.1 Transportation problem (T.P.), statement of T.P., balanced and unbalanced T.P.
7.2 Methods of obtaining basic feasible solution of T.P. i) North-West corner rule ii)
Method of matrix minima (least cost method), iii) Vogel’s approximation method
(VAM).
7.3 u-v method of obtaing Optimal solution of T.P., uniqueness and non-uniqueness
of optimal solutions, degenerate solution.
7.4 Assignment problems: statement of an assignment, balanced and unbalanced
problem, relation with T.P., optimal solution of an assignment problem.
7.5 Examples and problems.
8. Sequencing (6L)
8.1 Statement of sequencing problem of two machines and n-jobs, three machines
and n- jobs (reducible to two machines and n-jobs).
8.2 Calculation of total elapsed time, idle time of a machine, simple numerical
problems.
8.3 Examples and problems.
8
9. Simulation (6L)
9.1 Introduction to simulation, merits, demerits, limitations.
9.2 Pseudo random number generators: Linear congruential generator, mid square
method.
9.3 Model sampling from normal distribution ( using Box- Muller transformation),
uniform and exponential distributions.
9.4 Monte Carlo method of simulation.
9.5 Applications of simulation in various fields.
9.6 Statistical applications of simulation in numerical integration, queuing theory
etc.
Note: Verify the solutions using TORA package.
Books Recommended
1. Federer, W.T. : Experimental Design : Oxford and IBH Publishing Co.,
New Delhi.
2. Cochran W.G. and Cox, C.M. : Experimental Design, John Wiley and
Sons, Inc., New York.
3. Montgomery , D.C.: Design and Analysis of Experiments, and sons, Inc.,
New York.
4. Dass, M.N. and Giri,N.C. : Design and Analysis of Experiments, Wiley
Eastern Ltd., New Delhi.
5. Goulden G.H. : Methods of Statistical Analysis, Asia Publishing House
Mumbai
6. Kempthhorne, O: Design Analysis of Experiments.
Wiley Eastern Ltd., New Delhi.
7. Snedecor, G.W. and Cochran, W.G. : Statistical Methods, Affiliated East –
West Press, New Delhi.
8. Goon Gupta,Dasgupta : Fundamentals Of Statistics, Vol.II, The world Press
Pvt. Ltd. Calcutta.
9. Gupta S.C. and Kapoor V.K.: Fundamentals of Applied Statistics, S.Chand
Sons, New Delhi.
10. C.F. Jeff Wu, Michael Hamda: Experiments, Planning, Analysis and
Parameter Design Optimization.
11. G.W. Snedecor , W.G. Cochran : Statistical Methods 8th edition, Eastern
Press, Delhi ( for 1.8)
9
12. Miller and Freund : Probability and Statistics for engineers, Pearson
Education, Delhi ( for 1.8)
13. Gass,E.: Linear programming method and applications,Narosa Publishing
House, New Delhi.
14. Taha, R.A.: Operation research, An Introduction, fifth edition, Prentice Hall
of India, New Delhi.
15. Saceini,Yaspan,Friedman : Operation Research methods and problems,
Willey International Edition.
16. Shrinath.L.S : Linear Programming ,Affiliated East-West Pvt. Ltd , New
Delhi.
17. Phillips,D.T, Ravindra , A, Solberg, I.: Operation Research principles and
practice , John Willey and sons Inc.
18. Sharma, J.K.: Mathematical Models in Operation Research , Tata McGraw
Hill Publishing Company Ltd., New Delhi.
19. Kapoor,V.K.: Operations Research , Sultan Chand and Sons. New Delhi.
20. Gupta, P.K.and Hira , D.S.: Operation Research, S.Chand and company Ltd.,
New Delhi.
10
36 (B) STATISTICS (SPECIAL)
S-1 :PAPER I : DISTRIBUTION THEORY
FIRST TERM
1. Multinomial Distribution (10 L)
P(X1= x1, X2= x2, · · · ,Xk=xk) = ! ! !
!
1 2
1 2
1 2
k
x
k
x x
x x x
n p p p k
L
K
,
xi= 0,1,2 · · · , n; i=1,2 · · · ,k
x1+x2+· · · xk= n
p1+p2+· · · + pk= 1
0< pi< 1, i=1,2 · · · ,k
= 0 ,elsewhere.
Notation : (X1, X2, · · · ,Xk) ~ MD(n, p1, · · · , pk) , X~ MD (n, p )
where, X = (X1, X2, · · · , Xk) , P = ( k p , p , , p 1 2
K )
1.1 Joint MGF of (X1, X2, · · · , Xk)
1.2 Use of MGF to obtain means, variances, covariances, total correlation
coefficients, multiple and partial correlation coefficients for k= 3,
univariate marginal distributions, distribution of Xi+Xj, Conditional
distribution of Xi given Xi+Xj= r
1.3 Variance – covariance matrix, rank of variance – covariance matrix and its
interpretation.
1.4 Real life situations and applications.
2. Beta distribution (8L)
2.1 Beta distribution of first kind
p.d.f.
( , )
1
( )
B m n
f x = xm-1 ( 1-x)n-1, 0≤x ≤1, m,n >0
= 0 , elsewhere
Notation : X~ B1 (m,n)
11
Nature of probability curve, mean, variance, properties, rth raw moment,
harmonic mean.
2.2 Relation with U(0,1), if X and Y are iid B1(1,1) the probability
distributions of
,
1
X
X+Y, X-Y, XY, ,
Y
X
2.3 Beta distribution of second kind
p.d.f. m n
m
x
x
B m n
f x +
-
+
=
(1 )
.
( , )
1
( )
1
, x ≥ 0, m,n >0
= 0, elsewhere,
Notation : X~ B2 (m,n)
Nature of probability curve, mean, variance, properties, rth raw moment,
harmonic mean.
2.4 Interrelation between B1(m,n) and B2 (m,n).
2.5 Distribution of
X Y
X
Y
X
+
, etc. when X and Y are independent gamma
variates.
2.6 Relation between distribution function of B1(m,n) and binomial
distribution.
2.7 Real life situations and applications.
3. Weibull distribution (6 L)
p.d.f
1
( )
-
=
b
a a
b x
f x exp
-
b
a
x
x ≥ 0, α , β>0
= 0, elsewhere
Notation : X~ W(α , β).
Probability curve, location parameter, shape parameter, scale parameter,
Distribution function, quartiles, mean and variance, coefficient of variation,
relationship with gamma and exponential distribution, Hazard rate, IFR,DFR
property.
Real life situations and applications.
12
4. Order Statistics (10 L)
Order statistics for a random sample of size n from a continuous distribution,
definition, derivation of distribution function and density function of the i-th order
statistics X(i) particular cases for i=1 and i=n. Distribution of X(i) for random
sample from uniform and exponential distributions.
Derivation of joint p.d.f. of (X(i), X(j)), distribution function of the sample
range X(n)-X(1).
Distribution of the sample median.
If X1, X2 ……Xn are i.i.d. uniform r.v.s then Corr ( X(i), X(j)), distribution of
X(n) - X(1) and sample median. Comment on unbiased estimator of θ for U(0, θ) and
exponential(θ) based on order statistics.
5. Chebychev’s inequality (6L)
5.1 Chebychev’s theorem : If g (x) is a non – negative function of r.v. X such
that E[g(X)] < ∞, then P[g(X) ³ k]£ E[g(X)]/ k ,where k is positive
real number.
5.2 Chebychev’s inequality for discrete and continuous distributions in the
forms
[ ] 2
2
k
P X k
-m ³ £ s , k>1 and [ ] 2
1
k
P X -m ³ ks £
where μ= E(X) and σ2= Var (X) <∞.
5.3 Applications of Chebychev’s inequality in control charts, statistical
inference.
6. Central Limit Theorem and Weak Law of Large Numbers (8 L)
6.1 Sequence of r.v.s. , convergence of sequence of r.v. in a) probability
b) distribution with simple illustrations.
6.2 Statement and proof of the central limit theorem for i.i.d. r.v.s. (proof
based on MGF).
6.3 Weak law of large numbers (WLLN).
6.4 Applications of CLT and WLLN.
13
Second Term
7. Cauchy distribution (6L)
7.1 p.d.f. 2 2 ( )
1
( )
p l m
l
+ -
=
x
f x - ∞<x<∞, - ∞<μ<∞,λ>0,
= 0, elsewhere
Notation : X~ C (μ,λ).
7.2 Nature of the probability curve.
7.3 Distribution function, quartiles, non – existence of moments, distribution
of aX + b. Distribution of
X
1 , X2 for X~ C (0,1)
7.4 Additive property for two independent Cauchy variates (statement only),
statement of distribution of the sample mean, comment on limiting
distribution of
-X
.
7.5 Relationship with uniform , Student’s t and normal distribution.
7.6 Applications of C(μ,λ).
8. Laplace ( double exponential) distribution (6 L)
8.1 p.d.f. exp ( )
2
f (x) = l -l x -m , - ∞ < x < ∞, - ∞<μ<∞, λ>0,
= 0, elsewhere
Notation : X~ L (μ,λ).
8.2 Nature of the probability curve.
8.3 Distribution function, quartiles, comment on MLE of μ, λ.
8.4 MGF, CGF, moments and cumulants, β1, β2, γ1, γ2
8.5 Laplace distribution as the distribution of the difference of two i.i.d.
exponential variates with mean
l
1 .
8.6 Applications and real life situations.
9. Lognormal distribution (8L)
9.1 p.d.f.
( )s 2p
1
( )
x a
f x
-
= [ ]
- - - 2
2 log ( )
2
1
exp m
s
x a e , a <x , -¥ < μ < ∞, σ > 0,
= 0 , elsewhere
Notation : X~ LN (a ,μ,σ2).
9.2 Nature of the probability curve.
9.3 Moments (r- th moment of X-a), first four moments , β1 and γ1
14
coefficients, quartiles, mode.
9.4 Relation with N (μ,σ2) distribution.
9.5 Distribution of P Xi , Xi’s independent lognormal variates.
9.6 Applications and real life situations
10. Truncated distributions (8L)
10.1 Truncated distribution, truncation to the right, left and on both sides.
10.2 Binomial distribution B(n,p) left truncated at X=0, (value zero is
discarded), its p.m.f. , mean, variance.
10.3 Poisson distribution P(m), left truncated at X=0 ,(value zero is
discarded),its p.m.f. mean, variance.
10.4 Normal distribution N (μ,σ2) truncated i) to the left below a ii) to the
right above b iii) to the left below a and to the right above b , (a < b) its
p.d.f. and derivation of mean and statement (without derivation) of
variance in all the three cases.
10.5 Real life situations and applications.
11. Bivariate normal distribution (10L)
11.1 p.d.f. of a bivariate normal distribution.
2
1 2 2 1
1
( )
ps s - r
f x =
-
-
-
-
+
-
-
-
2
2
1
1
2
2
2
2
1
1
2 2
2(1 )
1
exp
s
m
s
m
r
s
m
s
m
r
x y x y
- ∞<x, y < ∞,
- ∞ < μ1, μ2 < ∞,
σ1, σ2 > 0, -1 < ρ < +1
= 0 , elsewhere
Notation : (X,Y)~ BN (μ1, μ2 , , 2 ,
2
2
1 s s ρ ), X ~ Np (μ , Σ), use of matrix
algebra is recommended.
11.2 Nature of surface of p.d.f. ,marginal and conditional distributions,
identification of parameters, regression of Y on X and of X on Y,
independence and uncorrelated- ness, MGF and moments. Distribution
of aX +bY +c , X/Y.
11.3 Applications and real life situations
12. Finite markov chains (10L)
12.1 Definition of a sequence of discrete r.v.s. , Markov property, Markov
chain, state space and finite Markov chain (M.C.), one step and n step
transition probability, stationary transition probability,
stochastic matrix P, one step and n step transition probability matrix
15
(t.p.m.) Chapman – Kolmogorov equations, t.p.m. of random walk and
gambler’s ruin problem.
12.2 Applications and real life situations.
Books Recommended
1. H. Cramer : Mathematical Methods of Statistics, Asia Publishing House,
Mumbai.
2. Mood, A.M. Graybill, F.Bose, D.C : Introduction to Theory(IIIrd Edition )
Mc-Graw Hill Series.
3. B.W. Lindgren : Statistical Theory (IIIrd Edition) Collier Macmillan
International Edition, Macmillan Publishing Co.Inc. NewYork.
4. Hogg, R.V. and Craig A.T. : Introduction to Mathematical Statistics ( IIIrd
Edition), Macmillan Publishing Company, Inc.866 34d Avenue, New York,
10022.
5. Sanjay Arora and Bansi Lal : New Mathematical Statistics (Ist Edition ),
Satya Prakashan16/17698, New Market, New Delhi,5(1989).
6. S.C. Gupta and V. K. Kapoor : Fundamentals of Mathematical Statistics,
Sultan
Chand and Sons, 88, Daryaganj, New Delhi, 2.
7. V.K. Rohatgi : An Introduction to Probability Theory and Mathematical
Statistics, Wiley Eastern Ltd. New Delhi.
8. J. Medhi: Stochastic Processes, Wiley Eastern Ltd…. New Delhi.
9. Hoel, Port and Stone, Introduction to Stochastic Processes, Houghton
Miffin.
10. Feller W. : An Introduction of Probability Theory and Its Applications, Vol.
I, Wiley Eastern Ltd. Mumbai.
11. Sheldon Ross: A first course in probability ( 6th edition) : Pearson
Education, Delhi.
16
36 (B) STATISTIC (SPECIAL)
S2 : PAPER II : PRACTICALS
Sr No. Title of the Experiment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Fitting of lognormal distribution.
Fitting of lognormal distribution using MS- EXCEL/SPREAD SHEET
Fitting of truncated binomial and truncated Poisson distributions
Fitting of truncated binomial and truncated Poisson distributions
using MS-EXCEL/SPREAD SHEET
Applications of truncated binomial, truncated Poisson
and truncated normal distributions
Model sampling from Laplace and Cauchy distributions
(also using MS-Excel / Spread sheet)
Applications of bivariate normal distribution
Maximum likelihood estimation
Testing of hypotheses (UMP test)
Construction of confidence interval for proportions, difference of
proportions
Construction of confidence interval for mean, difference of two
means from normal population
Construction of confidence interval for variance, ratio of
two variances from normal population
Nonparametric Tests I (Mann-Whitney. Run test (one
sample and two samples) , Median test)
Nonparametric Tests II (Kolmogorov - Smirnov Test)
Sequential probability ratio test for Bernoulli and Poisson distributions
(Graphical and Tabular procedure)
Sequential probability ratio test for normal and exponential distributions
(Graphical and tabular procedure)
17
17 Solution of LPP by simplex method
18 Solution of LPP by using its dual
19 Transportation problem
20 Analysis of CRD and RBD
21 Analysis of LSD and efficiency
22 Analysis of 23 factorial experiments arranged in RBD
23 A n a l y s i s o f t o t a l a n d p a r t i a l c o n f o u n d i n g i n 2 3factorial experiments
in RBD
24 Analysis of covariance in CRD and RBD
Note :
(1) Computer printouts are to be attached with journal for Experiment
Nos. 2,4 and 6.
(2) MS-EXCEL/SPREAD SHEET examination is to be conducted using computer
as per note no. (5).
Laboratory Equipments: Laboratory should be well equipped with sufficient
number of electronic calculators and computers along with necessary software,
printers and UPS.
18
37 ) MATHEMATICAL STATISTICS (GENERAL)
STATISTICAL INFERENCE
Note:
1) Mathematical Statistics can be offered only as a general level subject.
2) A student of three Year B.A. Degree Course offering Mathematical Statistics
will not be allowed to offer Applied Statistics in any of the three years of the
course.
First Term
THEORY OF ESTIMATION
1. Point Estimation (4L)
Notion of a parameter, parameter space , general problem of estimating an
unknown parameter by point and interval estimation.
Point Estimation : Definition of an estimator, distinction between estimator
and estimate , illustrative examples.
2. Methods of Estimation (12L)
Definition of likelihood as a function of unknown parameter for a random
sample from i) discrete distribution ii) continuous distribution, distinction
between likelihood function and p.d.f./ p.m.f.
Method of maximum likelihood: Derivation of maximum likelihood
estimator (M.L.E.) for parameters of only standard distributions ( case of two
unknown parameters only for normal distribution). Use of iterative procedure to
derive M.L.E. of location parameter μ of Cauchy distribution. Invariance property
of M.L.E.
a) M.L.E. of θ in uniform distribution over i) (0, θ) ii)(- θ, θ) iii) (mθ, nθ)
b) M.L.E. of θ in f(x ; θ)= Exp {-(x- θ)}, x > θ.
19
Method of Moments : Derivation of moment estimators for standard
distributions. Illustrations of situations where M.L.E. and moment estimators are
distinct and their comparison using mean square error.
3. Properties of Estimator (20L)
Unbiasedness : Definition of an unbiased estimator, biased estimator, positive
and negative bias, illustrations and examples( these should include unbiased and
biased estimators for the same parameters). Proofs of the following results
regarding unbiased estimators:
a) Two distinct unbiased estimators of j (θ) give rise to infinitely many
estimators.
b) If T is an unbiased estimator of θ , then j (T) is unbiased estimator of
j (θ ) provided j (.) is a linear function.
Notion of the Best Linear Unbiased Estimator and uniformly minimum
variance unbiased estimator (UMVUE), uniqueness of UMVUE whenever it
exists.
Sufficiency: Concept and definition of sufficiency, statement of Neyman’s
factorization theorem (Proof for discrete probability distribution). Pitmann –
Koopman form and sufficient statistic; Exponential family of probability
distributions and sufficient statistic.
Proofs of the following properties of sufficient statistics.
i) If T is sufficient for θ, then j (T) is also sufficient for θ provided j
is a one to one and onto function.
ii) If T is sufficient for θ then T is also sufficient for j (θ).
iii) M.L.E. is a function of sufficient statistic.
Fisher information function: amount of information obtained in a
statistic
T = T(x1,x2, …, xn). Statement regarding information in (x1,x2,…., xn) and
in a sufficient statistics T.
Cramer- Rao Inequality : Statement and proof of Cramer-Rao inequality,
Cramer – Rao Lower Bound(CRLB), definition of minimum variance
bound unbiased estimator (MVBUE) of ø (θ) . Proofs of following results:
a) If MVBUE exists for θ then MVBUE exists for j (θ) where j (.) is a
linear function.
b) If T is MVBUE for θ then T is sufficient for θ.
3.6 Efficiency : Comparison of variance with CRLB, relative efficiency of T1
w.r.t. T2 for (i) unbiased (ii) biased estimators. Efficiency of unbiased
estimator T w.r.t. CRLB.
20
4. Asymptotic Behaviour of an Estimator (6L)
4.1 Consistency : Definition , proof of the following theorems:
a. An estimator is consistent if its bias and variance both tend to
zero as the sample size tends to infinity.
b. If T is consistent estimator of θ and φ (.) is a continuous function,
then φ (T) is a consistent estimator of φ (θ).
4.2 Consistency of M.L.E.( Statement only).
5. Interval Estimation (6L)
5.1 Notion of interval estimation, definition of confidence interval (C.I.), length
of C.I., confidence bounds. Definition of pivotal quantity and its use in
obtaining confidence intervals.
5.2 Interval estimation for the following cases.
i) Mean (μ) of normal distribution (σ2 known and σ2 unknown).
ii) Variance (σ2) of normal distribution (μ known and μ unknown).
iii) Median, quartiles using order statistics.
Second Term
TESTING OF HYPOTHESES
6. Parametric Tests (15 L)
6.1 (a) Statistical hypothesis, problem of testing of hypothesis.
Definition and illustrations of (1) simple hypothesis, (2) composite
hypothesis, (3) test of hypothesis, (4) critical region, (5) type I and
type II errors. probabilities of type I error and type II error.
Problem of controlling the probabilities of errors of two kinds.
(b)Definition and illustrations of (i) level of significance, (ii)
observed level of significance (p-value), (iii) size of a test, (iv)
power of a test.
6.2 Definition of most powerful(M.P.) level α test of simple null
hypothesis against simple alternative. Statement of Neyman -
Pearson (N-P) lemma for constructing the most powerful level α
test of simple null hypothesis against simple alternative hypothesis.
Illustrations of construction of most powerful level α test.
21
6.3 Power function of a test, power curve, definition of uniformly most
powerful (UMP) level α test for one sided alternative.
Illustrations.
7. Likelihood ratio test : (9 L)
Notion of likelihood ratio test (LRT), λ (x)=Sup L(θ0|x) / Sup L(θ|x)
Construction of LRT for H0 : θ ε Q against H1: θ Ï Q for the
mean of normal distribution for i) known σ2 ii) unknown σ2 with
one tailed and two tailed hypotheses, LRT for variance of normal
distribution for i) known μ ii) unknown μ with one tailed and two
tailed hypotheses, LRT for parameter of binomial, of exponential etc.
two tailed alternative hypotheses, distribution. LRT as a function of
sufficient statistics , statement of asymptotic distribution of
-2 loge λ (x).
8. Sequential Tests (9 L)
Sequential test procedure for simple null hypothesis against simple
alternative hypothesis and its comparison with fixed sample size N-P
test procedure. Definition of Wald's SPRT of strength (α, β).
Illustration for standard distributions like Bernoulli, Poisson, Normal
and Exponential. SPRT as a function of sufficient statistics. Graphical
and tabular procedure for carrying out the test.
9. Nonparametric Tests (15 L)
9.1 Idea of non parametric problems. Distinction between a parametric and
a non-parametric problem. Concept of distribution free statistic. One
tailed and two tailed test procedure of (i) Sign test, ii) Wilcoxon
signed rank test (iii) Mann- Whitney U test, (iv) Run test, one sample
and two samples problems.
9.2 Empirical distribution function Sn (x). Properties of Sn (x) as estimator
of F (.). Kolmogorov – Smirnov test for completely specified
univariate distribution (one sample problem only) with two sided
alternative . Comparison with chi-square test.
22
Books recommended
1. Lindgren, B.W.: Statistical Theory (third edition) collier Macmillan
International Edition, Macmillan publishing Co., Inc. New York.
2. Mood, A.M., Graybill, F. and Bose, D.C. : Introduction to the theory of
Statistics (third edition) International Student Edition, McGraw Hill Kogakusha
Ltd.
3. Hogg, P.V. and Craig, A.J. : Introduction to Mathematical Statistics (fourth
edition), Collier Macmillan International Edition, Macmillan Publishing Co.
Inc., New York.
4. Siegel, S. : Nonparametric methods for the behavioural sciences, International
Student Edition, McGraw Hill Kogakusha Ltd.
5. Hoel, Port, Stone : Introduction to statistical Theory, Houghton Mifflin
Company (International) Dolphin Edition.
6. J.D. Gibbons : Non parametric Statistical Interence, McGraw Hill Book
Company, New York.
7. Daniel : Applied Nonparametric Statistics, Houghton Mifflin Company,
Roston.
8. V.K. Rohatgi : An introduction to probability theory and mathematical
statistics, Wiley Eastern Ltd., New Delhi.
9. Kendall and stuart : The advanced Theory of Statistics, Vol 1, Charles and
company Ltd., London.
10. Dudeweitz and Mishra : Modern Mathematical Statistic, John Wiley and Sons,
Inc., New York.
11. Kale, B.K. : A First Course In parametric Inference.
12. Kunte, S ., Purohit, S.G. and Wanjale, S.K. : Lecture Notes On Nonparametric
Tests.
13. B.L. Agarwal : Programmed Statistics: New Age International Publications ,
Delhi.
14. Sanjay Arora and Bansi Lal : New Mathematical Statistics (Ist Edition ), Satya
Prakashan16/17698, New Market, New Delhi,5(1989).
23
(38) APPLIED STATISTICS (GENERAL)
APPLICATIONS OF STATISTICS
Note :
(1) Applied Statistics can be offered only as a General Level subject.
(2) A Student of the Three Year B.A. Degree course offering
Applied Stat ist ics wi l l not be al lowed to of fer Mathemat ical
Stat ist ics and/or Statistics in any of the three years of the course.
First term
1. Continuous type distributions (12L)
1.1 Definition of continuous type of r.v. through p.d.f.,
Definition of distribution function of continuous type r.v.
Statement of properties of distribution function of continuous type r.v.s
1.2 Normal distribut ion p.d. f .
Standard normal distribut ion, s tatement of propert ies of normal
di s t r ibut ion, the graph of p.d.f., nature of probability curve.
Computat ion of probabi l i t ies.
1.3 Examples and problems
2. Tests of significance (18L)
2.1 Notion of a statistic as a function T(X1, X2 ,..., Xn) and its illustrations.
2.2 Sampling distribution of T(X1, X2 ,..., Xn). Notion of standard error of
a statistic.
2.3 Notion of hypothesis, critical region, level of significance.
24
2.4 Tests based on normal distribution for
(i) H0 : μ= μ 0 against H1 : μ ≠ μ 0 , μ<μ 0 , μ> μ 0
(ii) H0 : μ1= μ2 H1 : μ 1≠ μ 2 , μ 1 < μ2 , , μ 1 > μ2
(iii) H0 : P= P0 against H1 : P ≠ P 0 , P < P 0 , P > P 0
(iv) H0 : P1= P2 against H1 : P1 ≠ P 2 ,P1< P 2 , P1> P 2
(v) H0 : r= r 0 against H1 : r ≠ r 0 , r<r 0 , r> r 0
2.5
Examples and problems.
3. Tests based on t, chi-square and F distributions (18L)
3.1 t tests for
(i) H0 : μ= μ 0 against H1 : μ ≠ μ 0 , μ<μ 0 , μ> μ 0
(ii) H0 : μ1= μ2 H1 : μ 1≠ μ 2 , μ 1 < μ2 , , μ 1 > μ2
(iii) Paired observations
3.2 Tests for H0 : σ2= σ0
2against H1 : σ2 ≠ σ0
2 , σ2 <σ0
2 , σ2 >σ0
2
3.3 Chi square test of goodness of fit.
3.4 Chi square test for independence of attributes: Chi square test for
independence of 2 x 2 contingency- table (without proof).
Yate's correction not expected.
3.5 Tests for H0 : σ1
2 = σ2
2 against H1 : σ1
2 ≠ σ2
2 , σ1
2 < σ2
2 , σ1
2 >σ2
2
Second Term
4. An a l y s i s o f v a r i a n c e t e ch n i qu e s ( 1 2L)
4.1 Concept of analysis of variance.
4.2 One-way and two –way classification : break up of total sum of squares, analysis
of variance table, test of hypotheses of (i) equality of several means,
(ii) equality of two means.
4.3 Numerical problems.
25
5.
No n - p a r ame t r i c t e s t s ( 1 0 L)
5.1 Distinction between a parametric and non-parametric problem-
5.2 Concept of distribution free statistic.
5.3 One tailed and two tailed test procedure of
(a) Sign test, (b) Wilcoxon's signed rank test.
5.4 Test for randomness.
6. St a t i s t i c a l qua l i t y co n t r o l ( 2 6L)
6.1 Meaning and purpose of statistical quality control.
6.2
Control chart: Chance and assignable causes of quality variations,
statistical basis of control chart (connection with test of hypothesis is
NOT expected). Control limits (3-sigma limits only). Criteria for
judging lack of control:
(i) One or more points outside the control limits and
(ii) Non-random variations within the control limits : such as a run
of seven or more points on either side of the control line, presence of trend
or cycle.
6.3
Control charts for variables: Purpose of R-chart and Xchart,
construction of R-chart, X-chart when standards are not given. Plotting the
simple mean and ranges on Xand R charts respectively. Necessity for
plotting R-chart. Revision of R-chart. Drawing conclusion about state of
process. Revision of Xchart. Control limits for future production.
6.4
Control chart for fraction defective (p-chart) only for fixed sample
size. Determination of central line, control limits on p-chart, plotting
of sample fraction defectives on p-chart. Revision of p-chart,
determination of state of control of the process and interpretation of high
and low spots on p-chart. Estimation of central line and control limits
for future production.
6.5 Control chart for number of defects per unit (c-chart)
Construction of c chart when standards are not given. Plotting of number
of defects per unit on c-chart, determination of state of control of the
process, revision of control limits for future production.
6.6 Numerical problems based on control charts.
26
6.7 Identification of real life situations where these charts can be used.
Note: (i) Proof or derivations of results are not expected.
(ii) Stress should be given on numerical problems.
Books Recommended
1. Larson H.J.: Introduction to Probability Theory and Statistical
Applications, A Wiley International Edition.
2. Meyer, P. L. Introductory Probabi lity Theory and Statistical
Applications, Addison-Wesley Publishing Company.
3. Hoel, P. G.: Introduction. of Mathematical Statistics, John Wiley and
Sons Co. New York.
4. Walpole Introduction to Statistics, Macmillan Publishing Co. New
York.
5. Lipschutz: Probability and Statistics.
6. Goon, Gupta and Dasgupta : Fundamental’s of Statistics, Vol. I, The
World Press Pvt. Ltd. Calcutta.
7. New mark, J. : Introduction to Statistics.
8. Miller and Freund: Modern Elementary Statistics.
9. Gupta, S. P. : Statistical Methods, Sultan Chand and Sons, Delhi.
10. Gupta and Kapoor : Fundamental s of Appl ied Stat i st ics ,
Sultan Chand and Sons, Delhi.
27
(40) STATISTICAL PRE-REQUISITES
The course in Statistical Pre-requisites may be offered only by candidates offering
one of the social Sciences as their special subject at the B.A. Degree Examination.
The course in Statistical Pre-requisites can not be offered by those who offer any of
the Courses in Statistics Groups for their B.A. Examination
(1)
First Term
Correlation and Regression
(2) Partial correlation
(3) Multiple correlation
(4) Index numbers
(5) Time series
Second Term
(6) Statistical process control
(7) Acceptance sampling
(8) Data analysis for production function estimation
(9) Economic specialization of the production function
(10) Miscellaneous empirical problems relating to the estimation of production
function
Books Recommended
1. Larson7 H.J.: Introduction to Probability Theory and Statistical
Applications, A Wiley International Edition.
2. Meyer, P. L. Introductory Probabili ty Theory and Statistical Applications,
Addison-Wesley Publishing Company.
3. Hoel, P. G.: Introduction. of Mathematical Statistics, John Wiley and
Sons Co. New York.
4. Walpole Introduction to Statistics, Macmillan Publishing Co. New
York.
5. Goon, Gupta and Dasgupta : Fundamental’s of Statistics, Vol. I, The World
Press Pvt. Ltd. Calcutta.
6. New mark, J. : Introduction to Statistics.
7. Miller and Freund: Modern Elementary Statistics.
8. Gupta, S. P. : Statistical Methods, Sultan Chand and Sons, Delhi.
9. Gupta and Kapoor : Fundamental s of Appl ied Stat is t ics ,
Sultan Chand and Sons, Delhi.
28
29
