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Syllabus for P.G. Diploma in Statistics under CBCS
Department of Statistics
North-Eastern Hill University
P.G. Diploma in Statistics (Choice Based Credit System)
Department of Statistics, NEHU
Duration of the Course: Two Semester (Full Time)
Credit structure
MAXIMUM MARKS: 900
• No. of credit: 36
• Core credits: 30
• Open choice: 6
Credit:
• 1 credit = 25 marks.
• 4 credit = 100 marks.
Eligibility for admission: Any one who has passed the B.A/B.Sc/B.Com examination of
this University or any other University with Mathematics as a subject at 10+2 level.
First semester
Second semester
STA C 101(D) Descriptive Statistics: 4 credits
STA C 201(D) Statistical Inference: 4 credits
STA C 102(D) Numerical Analysis and Elements of
Probability Theory: 3 credits
STA C 202(D) Sample Survey Methodology: 2 credits
STA C 103(D) Distribution Theory: 3 credits
STA C 203(D) Analysis of Variance and Design of
Experiments: 2 credits
STA C 104(D) Practical 1: 2 credits
STA C 204(D) Economic Statistics: 2 credits
STA C 105(D) Practical 2: 2 credits
STA C 205(D) Linear Algebra: 2 credits
STA O 106(D) Mathematics : 2 credits (open)
STA C 206(D) Practical 1: 2 credits
STA O 107(D) Vital and Official Statistics: 2 credits
(open)
STA C 207(D) Practical 2: 2 credits
*
*
STA O 108(D) Biometry : 2 credits (open)
STA O 208(D) Linear Programming: 2 credits (open)
*
STA O 209(D) Introduction to Econometrics: 2 credits
(open)
Total Credits:
36
*
Courses offered under the Master’s programme of the Department
[1]
1.
1 credit is assigned for each 25 marks and 12 contact hours of teaching for theory or 24
contact hours of teaching for practical.
2.
For each course, 25 % marks are to be allotted for internal assessment
3.
A minimum of 10 practicals to be done in each practical course of 2 credits.
4.
For
Open
Courses,
practical
classes/questions
are
to
be
included
in
theory
classes/questions.
5.
For all theory courses, two questions are to be set from each unit and one question to be
attempted.
6.
For all practical courses, which are of 50 marks (2 credits) three questions of 15 marks to be
set from the list of practicals and two to be attempted. 7.5 marks for the viva voce.
7.
At most two open courses in first semester and at most one open course in second
semester can be taken.
[2]
SEMESTER I
STA C 101(D):
Descriptive Statistics
4 credits
Unit 1: Meaning of Statistics. Primary and secondary data. Collection of data and scrutiny of
data, Frequency and non – frequency data. Tabular and diagrammatic representation of
non – frequency data (line diagram. Ratio chart, bar diagram, pictograph and pie
diagram). Tabular representation of frequency data: Frequency, relative frequency,
cumulative frequency and frequency density. Column diagram, frequency polygon,
histogram and cumulative frequency diagram.
(10 Lectures)
Unit 2: Central tendency and dispersion. Measures of central tendency: mean, median and
mode. Measures of dispersion: range, mean deviation and standard deviation, coefficient
of variation and coefficient of concentration. Moments and quantiles, skewness and
kurtosis of a frequency distribution.
(15 Lectures)
Unit 3: Bivariate data, scatter diagram, concept of correlation and correlation coefficient. Simple
regression by least-squares method: its relationship with simple correlation. Correlation
ratio. Rank correlation: Spearman’s and Kendall’s coefficients. Theory of attributes.
(13 Lectures)
Unit 4: Multivariate data, multiple regression by least squares method ( up to two independent
variables), multiple and partial correlation coefficients.
(10 Lectures)
Text Books:
Bhattacharya, G.K. and Johnson, R.A. (1977) : Statistical Concepts and methods : Wiley
Eastern, New Delhi.
Hangal, David D. (2009), Introduction to Applied Statistics : A Non Calculus Based Approach,
Narosa Publishing House, New Delhi.
Hooda, R.P. (2002). Introduction to Statistics, Macmillan Publishers India Ltd., New Delhi.
Shenoy, G.V. (2000). Statistical Methods in Buisness and Social Science. Macmillan Publishers
India Ltd., New Delhi.
Goon, A.M., Gupta, M.K. and Das Gupta, B (1985) : Basic Statistics (for students of economics,
Commerce, accountancy and the biological sciences), World Press, Kolkata.
Gun, A.M., Gupta, M.K. and Das Gupta, B (2008) :Fundamentals of Statistics, Vol.I, World Press,
Kolkata.
[3]
STA C 102(D)
Numerical Analysis and Elements of Probability Theory
3 credits
Unit 1: Numerical analysis :
Approximate numbers ; rounding off, significant figures, digits, errors of approximation.
Finite differences, interpolation, Newton’s Gregory formula for forward and backward
interpolation. Divided difference formulae
Inverse interpolation, numerical solution in one unknown – iterative methods. Numerical
differentiation: Newton’s backward, forward and Lagrange’s interpolation. Numerical
integration: Trapezoidal rule, Simpson’s one third rule. Summation of series – Euler
Meclaurin formula. Stirling’s approximation of factorials (without proof).
(12 Lectures)
Unit 2: Experiments and Events Randomness and Statistical regularity. Classical definition and
its limitation, Relative frequency, subjective and axiomatic approach to probability . Basic
results on probability, law of large numbers, central limit theorem (statements)
(12 Lectures)
UNIT 3: Conditional probability, theorem of compound probability and total probability.
Independence of events. Independent trials. Problems on probability involving the basic
theorem. Bayes theorem.
(12 Lectures)
Text Books
Mukhopadhyay, P.(2002). Theory of Probability. New Central Book Agency (P) Ltd. Kolkata
Ross, S.M. (2004), Introduction to probability and Statistics for Engineers and Scientists, Elsevier
Academic Press, USA.
Chandra, T.K. and Chatterjee, D.(2003). A First Course in Probability, Second Edition. Narosa
Publishing House, New Delhi.
Scarborough, J. B. (1966) Numerical Mathematical Analysis, (sixth edition), Oxford and IBH
Publishing Co., New Delhi.
Demidovich, B.P. and Maron, I.A., (1981) Computational Mathematics, Mir Publishers, Moscow
[4]
STA C 103(D)
Distribution Theory
3 credits
Unit 1: Random Variables – Concept, Discrete, Continuous and Mixed. Probability Functions and
Distribution Functions. Expectation and Variance. Sum and Product Laws of Expectation.
Independency of Random Variables.
Joint Distribution of Random Variables, Marginal and Conditional Distributions.
Transformation of Two-Dimensional Random Variables.
(12 Lectures)
Unit 2: Discrete Probability Distributions – Uniform,
Hypergeometric, Negative Binomial Distribution.
Binomial,
Poisson,
Geometric,
(12 Lectures)
Unit 3: Continuous Probability Distributions – Rectangular, Gamma, Beta (Fist and Second
Kinds), Normal, Log-Normal, Exponential and Bivariate Normal.
(12 Lectures)
Text Books:
Cooper, R. A. and Weekes, A. J. (1983), Data, Models and Statistical Analysis, Heritage
Publishers, New Delhi
Chao, L. L. (1974), Statistical Methods and Analysis, McGraw Hill Book Company, New York
Gupta, S. C. and Kapoor, V. K. (2006), Fundamentals of Mathematical Statistics, Sultan Chand
and Sons, New Delhi.
Hogg, R.V. and Craig, A.T. (2002). Introduction to Mathematical statistics, Pearson Education,
Delhi.
[5]
STA C 104(D)
Practical 1
2 credits
1. Plotting of Frequency Distribution : line, bar, pie, frequency polygon
2. Plotting Histogram
3. Computation of mean, median and mode for ungrouped and grouped frequency
distrbution
4. Computation of variance,standard deviation,mean deviation
5. Computing coefficient of variation and coefficient of concentration
6. Moments, quartiles, skewness and kurtosis of frequency distribution
7. Calculating correlation coefficient from ungrouped and grouped data
8. Fitting a straight line by least square method
9. Computing rank correlation
10. Problems on association of attributes
11. Computing partial and multiple correlation coefficient
12. Fitting a linear regression of Y on X1 and X2
Text Books:
Hooda, R.P. (2002). Introduction to Statistics, Macmillan Publishers India Ltd., New Delhi.
Shenoy, G.V. (2000). Statistical Methods in Buisness and Social Science. Macmillan Publishers
India Ltd., New Delhi.
Goon, A.M., Gupta, M.K. and Das Gupta, B (1985) : Basic Ststiatics (for students of economics,
Commerce, accountancy and the biological sciences), World Press, Kolkata.
Gun, A.M., Gupta, M.K. and Das Gupta, B (2008) :Fundamentals of Statistics, Vol.I, World Press,
Kolkata.
[6]
STA C 105(D)
Practical 2
2 credits
1. Fitting of Binomial Distribution
2. Fitting of Poisson Distribution
3. Fitting of Normal Distribution
4. Fitting of Negative Binomial Distribution
5. Fitting of Log-Normal Distribution
6. Difference table: location and correction of error
7. Interpolation by Newton’s forward, backward and Lagrange’s formula
8. Inverse interpolation
9. Numerical solutions of equations in one unknown by iterative methods
10. Numerical differentiation using Newton’s and Lagrange’s interpolation formula
11. Numerical Integration by trapezoidal and Simpson’s 1/3 rule
12. Use of Euler – Maclaurin formula for summation of series and numerical integration.
Text Books:
Chao, L. L. (1974), Statistical Methods and Analysis, McGraw Hill Book Company, New York
Gupta, S. C. and Kapoor, V. K. (2006), Fundamentals of Mathematical Statistics, Sultan Chand
and Sons, New Delhi
Scarborough, J. B. (1966) Numerical Mathematical Analysis, (sixth edition), Oxford and IBH
Publishing Co., New Delhi.
[7]
STA 0 106(D)
Mathematics
2 credits (open)
Unit I: Real valued functions and their graphs.
Limits of algebraic and trigonometric functions, standard limits based an logarithmic and
exponential functions (without proof, application only).
Continuity and differentiability of exponential, logarithmic and trigonometric functions.
Derivatives ; sum, product and quotient rule, chain rule, differentiation of implicit and
parametric forms. Maxima, Minima, Mean Value theorem. Rolle’s theorem.
(12 Lectures)
Unit 2: Indefinite integral as inverse process of differentiation. Methods of integration:
substitution method, partial fraction and integration by parts.
Fundamental theorem of calculus, evaluation and properties of definite integrals,
Applications of definite integrals. Definite integrals expressed as the limit of the sum.
Area under curves.
Differential Equation: Basic concepts. General and particular solution, Formation of
differential equations. Equations of first order and first degree.
(12 Lectures)
Text Books
Lang, S.(2006). A First Course in Calculus, Fifth Edition, Springer.
Apostol, T. M.(2006). Calculus, Volume I, Second Edition, Wiley India Edition.
Thomas and Finney :(2008). Calculus and Analytic Geometry , Narosa Publications, New Delhi
[8]
STA O 107(D)
Vital and Official Statistics
2 credits (open)
Unit 1: Vital Statistics: Concept of Demography and population Sciences, Measures of Mortality
– Crude, Specific and Standardized Death Rates, Comparative Mortality Index and
Maternal Mortality Rate. Measures of Fertility – Crude birth, General Fertility, Specific
Fertility, Total Fertility, Gross Reproduction and Net Reproduction Rates. Computation of
Rate of Population Growth – Arithmetic, Geometric and Exponential. Description of a
complete Life table.
(12 Lectures)
Unit 2: Official Statistics: Role of DSO, NSSO, Office of Registrar-General and State Statistical
Bureaus. Evaluation of Family Planning Programmes – Concept and definition.
Evaluation of Fertility Impact of Family Planning Programmes – Introduction and Basic
Concepts only.
(12 Lectures)
Text Books:
Gupta, S. C. and Kapoor, V. K. (2006), Fundamentals of Applied Statistics, Sultan Chand and Sons,
New Delhi
Gun, A. M., Gupta, M. K. and Dasgupta, B. (2008), Fundamental of Statistics Volume II. The
World Press, Kolkata
[9]
STA O 108(D)
Biometry
2 credits (open)
Unit 1 An introduction to Biometry and Statistics : data collection and data presentation,
frequency distribution, graphical representation, measures of central tendency,
dispersion, skewness and kurtosis. Probability distribution : Binomial, Poisson and
Normal distribution.
(6 Lectures)
Unit 2 Introduction to bivariate frequency data and its measurement : covariance, correlation,
scatter diagram. Regression analysis : Linear regression, regression coefficient, fitting of
regression equation by least square method.
(7 Lectures)
Unit 3 Population, sample. Statistic, standard error, estimation, confidence interval and
confidence level, confidence interval estimate of proportion and mean. Hypothesis and its
types, errors, level of significance. Test statistics : Student’s Chi-square, F and ZStatistics and their applications in testing of hypothesis.
(7 Lectures)
Unit 4 An introduction to Analysis of Variance (ANOVA),its definition, assumptions and uses.
One way classification and statistical analysis of the model involved in it.
(4 Lectures)
Text Books
Hogg, R. V. & Tanis, E. A. (2002): Probability and Statistical Inference Pearson Education, Asia.
Mood, A. M., Graybill, F. A. and Boes D. C (1999): Introduction to the theory of Statistics.
McGraw –Hill, New York.
Additional Reference
Arora, P. N and Malhan, P. K (2001): Biostatistics, Himalaya Publishing House, New Delhi.
Goon, A. M., Gupta, M. K. and Das Gupta, B. (2006): Basic Statistics, World Publication, Kolkata.
[10]
SEMESTER II
STA C 201(D)
Statistical Inference
4 credits
Unit 1: Statistic and parameter. Random sampling from a distribution. Sampling distribution of a
statistic and its standard error under random sampling. Sampling distributions of the
sample sum in sampling from a binomial and Poisson distribution. Sampling distribution
of sample mean and sample variance in sampling from a univariate normal distribution.
Definitions of chi-square, t and F statistics and their probability density functions and
basic properties (without derivations).
(14 Lectures)
Unit 2: Point and interval estimation. Intuitive considerations underlying the principles of
unbiasedness, minimum variance and consistency. Basic principles of hypothesis testing:
test, critical region, type I error and type II error, level of significance and power of a test.
(12 Lectures)
Unit 3: Tests for a single mean, for the equality of two means, for a single variance and for the
equality of two variances, for the significance of correlation coefficient (under normal
population models).
(8 Lectures)
Unit 4: Large-sample tests: for binomial proportion and for the equality of two binomial
proportions, for a single mean and for difference of means of two independent
populations and related confidence intervals.
Pearsonian chi-square and its application in testing for goodness of fit, for independence
and for homogencity. Yates’ correction for continuity in 2 x 2 table.
(12 Lectures)
Text Books
Casella. G and Berger R.L. (1990) Statistical Inference, Wordsworth and Brooks, California.
Hogg, R.V. and Craig, A.T. (2002). Introduction to Mathematical statistics, Pearson Education,
Delhi.
Kale, B.K. (1999). A First Course on Parametric Inferences, Narosa Publishing House, New
Delhi.
Rohatgi V. (1998). An Introduction to Probability and Mathematical Statistics. Wiley Eastern.
[11]
STA C 202(D)
Sample Survey Methodology
2 credits
UNIT 1: Complete enumeration survey and sample survey and their relationship. Need for
sampling. Sampling and Non-sampling errors.
Basic concepts : Unit, population and parameters. Sampling unit and sampling frame.
Random sample and sampling design. Sample estimators and their properties.
Measures of error – Mean Square Error (MSE) and Standard Error (SE). Sampling and
cost efficiency. Interpenetrating sub samples (IPNSS)
Basic Sampling Techniques : Simple random sampling with and without replacement
(SRSWR & SRSWOR) – point and interval estimation of population mean/total and
proportion, determination of sample size.
(7 lectures)
UNIT 2: Basic Sampling Techniques (Continued) : Linear (LSS) and Circular (CSS) systematic
sampling, Unbiased variance estimation – use of IPNSS.
Probability proportional to size with replacement (PPSWR) sampling. Sample selection –
cumulative total and Lahiri’s methods. Estimation of population total/mean and unbiased
variance estimation.
Stratified sampling, allocation problem.
(9 lectures)
UNIT 3: Ratio, difference and regression methods of estimation under SRS only.
Non-sampling error – dealing with non response error. Planning and execution of
sample surveys, methods of data collection – questionnaire vs schedule. Problems of
sampling frame, choice of sampling design, pilot survey, field work etc. NSSO rounds –
sampling design and field operations.
(8 lectures)
Text Books
Cochran, W.G. (1997) : Sampling Techniques, Wiley Eastern, New Delhi.
Murthy, M.N. (1967) : Sampling Theory and Methods, Statistical Publishing Society, Kolkata.
Sukhatme, P.V., Sukhatme, B.V. Sukhatme, S. and Asok, C. (1984) : Sampling Theory of
Surveys with Applications, Indian Society of Agricultural Statistics, C/o IASRI, Library Avenue,
New Delhi.
Additional Books
Mukhopadhyay, P. (1998) : Theory and Methods of Survey Sampling, Prentice Hall of India, New
Delhi.
Raj, D and Chandhok, P. (1998) : Sample Survey Theory, Narosa Publishing House, New Delhi.
Sampath, S. (2001) : Sampling Theory and Methods, Second Edition, Narosa Publishing House,
New Delhi.
[12]
STA C 203(D)
Analysis of Variance and Design of Experiments
2 credits
Unit 1: Analysis of variance: Analysis of data for a one-way classification : fixed effects and
random-effects models. Analysis of data for a two way classification with the same
number of observations per cell : main effects and interaction effects for fixed-effects
model only and estimation of error variance.
(12 Lectures)
Unit 2: Design of Experiment: Controlled experiments. Basic principles of designs:
randomization, replication and local control. Description and method of analysis of
experiments conducted according to a completely randomized, a randomized block and a
latin square design.
2
3
Factorial experiments: 2 and 2 experiments. Confounding: complete and partial.
(12 Lectures)
Text Books:
Searle, S. R. (1971). Linear Models. Wiley, New York.
Cochran, W.G. and Cox, G.M. (1959). Experimental Designs, Asia Publishing House, Singapore.
Montgomery, C.D. (2001). Design and Analysis of Experiments, John Wiley, New York.
Gun, A.M., Gupta, M.K. and Das Gupta, B (2008) :Fundamentals of Statistics, Vol.II, World Press,
Kolkata.
[13]
STA C 204(D)
Economic Statistics
2 credits
Unit 1: Time Series: Introduction, Components. Measurement of Trend – Graphic
Method, Method of Semi-Averages, Method of Moving Averages and Method of Curve
fitting by the Principle of Least Squares (Straight line, Second Degree Polynomial &
Exponential Curve) and Spencer’s 15-point Formula. Measurement of Seasonal
Fluctuations – Method of Simple Averages, Ratio to Trend method, Ratio to Moving
average and Link Relative Method. Measurement of Cyclic Fluctuation. Autocorrelation
and Correlelogram (Concept only).
(12 Lectures)
Unit 2: Index Number: Introduction, Problem Involved in its Construction, Calculation of Index
Numbers, Criteria of Good Index Numbers, Classification of Index Numbers, Cost of
Living Index Number and its Construction. Base Shifting, Splicing & Deflating of Index
Numbers.
Demand Analysis: Laws of Supply and Demand, Price Elasticity of Demand & Supply,
Pareto Law of Income Distribution.
(12 Lectures)
Text Books:
Cooper, R. A. and Weekes, A. J. (1983), Data, Models and Statistical Analysis, Heritage
Publishers, New Delhi
Chao, L. L. (1974), Statistical Methods and Analysis, McGraw Hill Book Company, New York
Gupta, S. C. and Kapoor, V. K. (2006), Fundamentals of Applied Statistics, Sultan Chand and
Sons, New Delhi
Gun, A. M., Gupta, M. K. and Dasgupta, B. (2008), Fundamental of Statistics Volume II. The
World Press, Kolkata
[14]
STA C 205(D)
Linear Algebra
2 credits
Unit 1: Linear equations and matrices, square matrix, equality of matrices, Addition, scalar
multiplication, product of matrices, transpose, conjugate transpose, Inverse of a matrix,
nilpotent, idempotent, symmetric and skew symmetric matrices: row vectors and column
vectors of a matrix. Linear independence of row/column vectors.
(12 Lectures)
Unit 2: Rank and basis of a set of vectors, rank of a matrix, determination of rank by elementary
operations. Determinants, properties of determinant. Adjoint of a matrix, inverse ;
determinental rank of matrix, system of linear equations ; Homogeneous and nonhomogeneous, Cramer’s rule.
(12 Lectures)
Text Books:
Kolman, B. and Hill, B.R.Lay, David C. (1997) Linear Algebra and its Applications, Addison
Wesley.
Hadley, G. (2002) Linear Algebra, Narosa Publishing House, New Delhi.
Mittal, R.K. & Shanti Nararyan.(2008): Matrices, S. Chand, New Delhi.
[15]
STA C 206(D)
Practical 1
2 credits
1. Large Sample Tests for mean and proportion
2. Large Sample Tests for Equalities of means and proportions
3. Exact Test for Sample Correlation Coefficient
4. Tests for equality of means and variances for small samples
5. Test of goodness of fit
6. Test of independence of attributes in Contingency table
7. Analysis of Variance for One-way classification
8. Two-way classification (one observation per cell)
9. Analysis of CRD, RBD and LSD
2
3
10. Analysis of 2 and 2 experiments
11. Computation of Index numbers
12. Computation of Trend by Curve Fitting (Straight Line) and Moving Averages
13. Computation of Seasonal Fluctuation by Link Relative Method
Text Books
Casella. G and Berger R.L. (1990) Statistical Inference, Wordsworth and Brooks, California.
Hogg, R.V. and Craig, A.T. (2002). Introduction to Mathematical statistics, Pearson Education,
Delhi.
Rohatgi V. (1998). An Introduction to Probability and Mathematical Statistics. Wiley Eastern.
Gupta, S. C. and Kapoor, V. K. (2006), Fundamentals of Applied Statistics, Sultan Chand and
Sons, New Delhi
Gun, A. M., Gupta, M. K. and Dasgupta, B. (2008), Fundamental of Statistics Volume II. The
World Press, Kolkata
Montgomery, C.D. (2001). Design and Analysis of Experiments, John Wiley, New York.
[16]
STA C 207(D)
Practical 2
2 credits
1. Evaluation of determinant by pivotal condensation method.
2. Matrix Inversion – Gaussian method
3. Solution of simultaneous linear equations by Cramer’s Rule
4. SRSWR – all possible samples – demonstration of unbiasedness or otherwise of
Estimators
5. SRSWOR – all possible samples – demonstration of unbiasedness or otherwise of
Estimators
6. SRSWR – sample selection and point and interval estimation of population total/mean
along its s.e.
7. SRSWOR – sample selection and point and interval estimation of population total/mean
along its s.e.
8. Estimation of population proportion in SRSWR and SRSWOR.
9. LSS – all possible samples and estimation of population mean/total verification of
estimators.
10. PPSWR – selection of samples and estimation of parameters.
11. Stratified sampling – estimation
12. Stratified sampling – allocation of sampling to strata
Text Books
Murthy, M.N. (1967) : Sampling Theory and Methods, Statistical Publishing Society, Kolkata.
Singh, D and Chaudhury (1996) : Theory and Analysis of Sample Survey Designs, New Age
International Ltd. New Delhi/Guwahati.
Kolman, B. and Hill, B.R.Lay, David C. (1997) Linear Algebra and its Applications, Addison
Wesley.
Hadley, G. (2002) Linear Algebra, Narosa Publishing House, New Delhi.
Mittal, R.K. & Shanti Nararyan.(2008): Matrices, S. Chand, New Delhi.
[17]
STA O 208(D)
Linear Programming
2 credits (open)
Unit 1 Introduction to Linear Programming (LP). Mathematical Formulation of Linear
Programming Problem (LPP). Graphical solution to LPP.
(4 Lectures)
Unit 2 General LPP, Canonical and Standard forms of General LPP, Duality in LPP , Simplex
Method. Big-M method and Two-phase method.
(14 Lectures)
Unit 3 Transportation and Assignment problems. (Including Traveling Salesman’s Problem).
(6 Lectures)
Text Book
Gass, S. I. (1975) Linear Programming: Method and Application, Mc Graw – Hill, New York.
Shevoy, G. V. (1992), Linear Programming: Methods and Applications, Wiley Eastern, New Delhi.
[18]
STA O 209(D)
Introduction to Econometrics
2 credits (open)
Unit 1 Nature of econometrics. The general linear model (GLM) and its extension. Ordinary
least squares (OLS) estimation and prediction. Use of dummy variables and seasonal
adjustment. Generalized least squares (GLS) estimation and prediction. Heteroscedastic
disturbances. Pure and mixed estimation. Grouping of observations and of equations.
(8 Lectures)
Unit 2 Auto correlation, its consequences and tests. Theil BLUS procedure. Estimation and
prediction. Multicollinearity problems, its implications and tools for handling the problem.
Ridge regression.
(8 Lectures)
Unit 3 Linear regression with stochastic regressors. Instrumental variable estimation. Errors in
variables. Autoregressive linear regression. Distributed lag models. Use of principal
components, canonical correlations and discriminant analyses in econometrics.
(8 Lectures)
Text Books
Apte PG (1990). Test book of Econometrics, Tata McGraw Hill, New Delhi.
Johnston, J. (1984). Econometric methods, McGraw Hill, New York.
Ray, Devraj (1998). Development Economics, Oxford University Press, Oxford.
Additional References
CSO (1980). National Accounts Statistics – Sources and Health, New Delhi.
Intrulligator, M.D. (1980). Econometric models-Techniques and Applications, Prentice Hall of
India, New Delhi.
Nagar, A.L. (1983). Basic Statistics, Oxford University Press, Oxford.
[19]