STATISTICS HONOURS SYLLABUS Semester Paper Code

STATISTICS HONOURS SYLLABUSSemesterPaper ic5Descriptive Statistics 1 & Probability Theory 12Real Analysis3Practical- Using C Programming and Minitab tive Statistics 2 & Probability Theory 2Linear Algebra 1Practical- Using C Programming and Minitab tive Statistics 3 & Probability Theory 3Linear Algebra 2Practical- Using R and Minitab 100555Multivariate Analysis & Economic and OfficialStatisticsSampling distribution & Statistical Inference 1Statistical Quality Control & Population StatisticsPractical-Using R and Minitab 1005555Linear Statistical Model & Time SeriesStatistical Inference 2 & Non Parametric InferenceStatistical Inference 3 & Sample Survey 1Practical-Using R and Minitab 04010010010041555Econometrics- TheoryEconometrics- PracticalDesigns of Experiment & Sample Survey 2Practical- Using R and Minitab SoftwareDissertation1456

Semester: IPaper Code: ST 31011TGroup:ATopic:Descriptive Statistics – 1Lecture hrs: 39 6 (tutorials) 45Introduction: Nature and scope of Statistics, Classification of data, Concept of population andsample, primary and secondary data, experimental design and survey data, observational studies,quantitative and qualitative data, discrete and continuous data, cross-sectional and time seriesdata.(8L)Scales of measurement: Nominal, Ordinal, Ratio and Interval.(2L)Collection and scrutiny of data: Methods of collection of primary data, scrutiny of data forinternal consistency and detection of errors in recording.(4L)Presentation of data: Textual and tabular presentation, stem and leaf display, diagrammaticpresentation - Line diagrams, bar diagrams, pie diagram and sub-divided bar diagram. Frequencydistribution of discrete and continuous variables. Column diagram, frequency polygon,histogram step diagram, ogive and frequency curves.(8L)Analysis of quantified data: Characteristics of Univariate frequency distribution – location,dispersion, skewness and kurtosis. Moment and quantile measures. Sheppard’s correction(without derivation). Relative dispersion. Box-plot and detection of outliers. Trimmed mean andWinsorised mean.(17L)References:1. Goon A.M, Gupta M.K. Dasgupta B. : Fundamentals of Statistics, Volume 1.2. Yule G.U. and Kendall M.G. : An Introduction to the theory of Statistics.3. Hogg and Tanis. : Probability and Statistical Inference.Course Objective: This course introduces a student to different types of data and the art of datahandling. Throughout the course the students will develop the techniques of summarization andidentification of the salient features of the data through graphical displays and other descriptivemeasures. The emphasis will be on metric data corresponding to a single variable.Paper Structure:No. of ques to be setNo of ques to hort ques)(long ques)43225x2 1015x2 3040Semester: IPaper Code: ST 31011TGroup:BTopic:Probability Theory – 1

Lecture hrs: 39 6 (tutorials) 45Random Experiments: Trials, Sample points, Sample space, Events, Class of events.(5L)Definition of Probability: Classical definition, its application (using combinatorial analysis) andlimitations, Stability of long-run relative frequency, Kolmogorov’s Axiomatic definition.Probability of union and intersection of events. Probability of occurrence of exactly m & at leastm events out of n events (proof for finite sample space only). Conditional probability &Independence of events. Bayes’ theorem & its applications. Examples based on classicalapproach – illustrations using difference equation (of 1st order only). Repeated trials, productspace and independent trials.(34L)References:1.2.3.4.S.M. Ross : A First Course in Probability.K.L. Chung : Elementary Probability Theory with Stochastic Process.W. Feller : An Introduction to Probability Theory and its Application (Vol. 1).A.M Gun, M.K. Gupta, B. Dasgupta : An Outline of Statistical Theory (Vol. 1).Course Objective: At the end of the course a student should Understand different definitions and meaning of Probability. Know different laws of probability and the theorems connecting them. Be able to apply the laws of probability.Paper Structure:No of Ques to be setshort4long3No of Ques to beansweredshortLong22MarksMarks(short ques)5x2 10(long ques)15x2 : IPaper Code: ST 31022TGroup:ATopic:Real AnalysisLecture hrs: 26{concepts, statements (without proof) of major results and their applications}Sets and Functions. Sequence & Series of real numbers. Convergence, Limits, Absoluteconvergence. Concepts of on & On. Comparison, Ratio & Root tests.(9L)Continuity & Differentiability of real-valued functions. Riemann integration. FundamentalTheorem of integration. Differentiation under integration.(6L)Sequence & Series of functions. Pointwise & Uniform convergence. Simple tests, Power Series,Taylor’s series expansion, Differentiation & Integration of series.(6L)

Double integration. Evaluation of double integrals-repeated integrals & change of variables. (5L)References:1. Bertle, D. Sherbert : Introduction to Real Analysis2. 2. R Goldberg : Methods of Real Analysis3. K B Sinha, R L Karandikar et al. : Understanding MathematicsCourse Objective: At the end of the semester, a student should have learnt to define theimportant concepts of analysis of real numbers and real functions as a prerequisite for thedevelopment of further statistical studies.Paper Structure:No of Ques to be setshort2long2No of Ques to beansweredShortLong11MarksMarks(short ques)5x1 5(long ques)15x1 ***************************************Semester: IPaper Code: ST 31022PGroup:BTopic:C Programming & Data analysis using MinitabComponents of C: Introduction to C Program. Constants, Variables & Key Words. LoopStructures -For loop. Conditional Statements – If, If-Else. Break, Exit and Continue functions.Single Dimensional Array.Paper Structure: All questions in this paper are compulsory.ModuleTopicMarksModule 1C Programming :15Data Analysis through MINITAB35Module 2Viva-Voce10Total60Semester:IIPaper Code: ST32031TGroup:A

Topic:Descriptive Statistics – 2Lecture hrs: 39 6 (tutorials) 45Correlation & Regression Analysis: Bivariate data and scatter plot. Theory of Regression, leastsquare method and related results. Coefficient of determination. Pearson’s product momentcorrelation coefficient and its properties.(15L)Non-linear regression: Fitting of polynomial and exponential curves. Correlation Index andCorrelation Ratio. Transformations to linearity: log-linear and power transformations.(8L)Regression diagnostics: Residual plots, outliers, leverage and influential data points, Cook’sdistance.(6L)Some other types of correlation: Intra class correlation with equal and unequal class sizes, rankcorrelation – Spearman & Kendall (tied and untied cases), Grade correlation, Bi-Serialcorrelation.(10L)References:1. A.M. Gun, M.K.Gupta, B. Dasgupta : Fundamentals of Statistics, Volume 1.2. G.U. Yule and M.G. Kendall: An Introduction to the theory of Statistics.3. Hogg and Tanis. : Probability and Statistical InferenceCourse Objective: This course is a continuation to the descriptive Statistics course in SemesterI. Throughout this course student will handle bivariate data. The primary objective of this coursewould be to study the nature of association between two variables. The two problems in bivariatedata analysis namely “Correlation and Regression” would be introduced. The focus will beprimarily on metric data. Some special types of correlation measures would also be developed tohandle clustered /array data, ordinal/nominal data. Regression diagnostics would be introducedto identify any unusual cases.Paper Structure:No of Ques to be setshort4Semester :long3No of Ques to beansweredShortlong22IIPaper Code: ST32031TGroup:BMarksMarks(short ques)5x2 10(long ques)15x2 30TotalMarks40

Topic:Probability Theory - 2Lecture hrs: 39 6 (tutorials) 45Random Variables (Univariate case): Definition of discrete & continuous random variables,Cumulative distribution function (cdf) & its properties (with proof), Probability mass function(pmf) & Probability density function (pdf), Expectation and Variance, Moments, Skewnss andKurtosis. Quantiles of random variable. Probability generating function (pgf) & Momentgenerating function (mgf).(18L)Probability Inequalities: Markoff’s, Chebyshev’s and Chernoff’s inequalities.(5L)Random Variables (Bivariate case): Cdf, pmf & pdf. Marginal & Conditional distributions,Independence. Bivariate moments, mgf and pgf Theorems on sum and product of two randomvariables, Theorems on Conditional Expectation & Variance, Correlation & Regression. (16L)References:1.2.3.4.S.M. Ross : A First Course in Probability.K.L. Chung : Elementary Probability Theory with Stochastic Process.V.K. Rohatgi : Introduction to Probability Theory and Mathematical Statistics.A.M Goon, M.K. Gupta, B. Dasgupta : An Outline of Statistical Theory (Vol. 1).Course Objective: At the end of the course a student should1. Understand what is a random variable and its probability distribution.2. Understand different aspects of Bivariate probability distribution.3. Know different probability inequalities.Paper Structure:No of Ques to be setshort4Semester:Paper Code:Group:Topic:long3No of Ques to beansweredShortlong22IIST32042TALinear Algebra – 1MarksMarks(short ques)5x2 10(long ques)15x2 30TotalMarks40

Lecture hrs: 26Vector Algebra: Vector spaces defined on field of real numbers. Euclidean space and subspaces.Linear independence of vectors. Concepts of Spanning Set, Basis & Dimension of a vectorspace. Orthogonal vectors, Gram-Schmidt orthogonalization, ortho-complement space.(16)Matrix Algebra: Matrices (definition and types). Matrix inverse. Inverse and determinant ofPartitioned matrices. Orthogonal matrix. Row space & column space of a matrix, Null space andnullity.(10)References:1. Linear Algebra, G. Hadley2. An Introduction to Vectors and Matrices, A.M. Gun3. A text book of Matrices, Shanti Narayan4. Linear Algebra, Searle.Course Objective: After completion of the course a student is expected to have (i)through ideaof vector spaces, subspaces, their dimensions and basis (ii) matrix algebra, determinants & thesubspaces associated with a matrix. This course is expected to lay the foundations to learn thecourses like Multivariate Analysis and ANOVA.Paper Structure:No of Ques to be setshort2long2No of Ques to beansweredShortlong11MarksMarks(short ques)5x1 5(long ques)15x1 :Paper Code:Group:Topic:IIST32042PBC Programming & Data Analysis using MinitabComponents of C: Loop Structures – While, Do-While. Two Dimensional Arrays, Functions(passing values only).Paper Structure: All questions in this paper are compulsoryModuleTopicMarksModule 1C Programming :15Module 2Data Analysis through MINITAB35Viva-Voce10Total60Semester: IIIPaper Code: ST33051TGroup:ATopic:Descriptive Statistics – 3