For Enhancement of Employability

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

For Enhancement of Employability

Appendix B B.O.S. 29.05.2015 DEPARTMENT OF STATISITICS & OPERATIONS RESEARCH

ALIGARH MUSLIM UNIVERSITY ALIGARH B.A. /B.Sc. (Honours) (Statistics)

I Semester Course Code - STB1P1

Lab. Course – Descriptive Statistics

Credit: 2 Max Marks: 40+60 =100

List of Practical

1. Graphical representation of data.

2. Problems based on measures of central tendency.

3. Problems based on measures of dispersion.

4. Problems based on combined mean and variance and coefficient of variation.

5. Problems based on moments, skewness and kurtosis.

6. Fitting of polynomials, exponential curves.

7. Karl Pearson correlation coefficient.

8. Correlation coefficient for a bivariate frequency distribution.

9. Lines of regression, angle between lines and estimated values of variables.

10.Spearman rank correlation with and without ties.

11.Partial and multiple correlations.

12.Planes of regression and variances of residuals for given simple correlations.

13.Planes of regression and variances of residuals for raw data.

14.To calculate price and quantity index numbers using simple and weighted average of price relatives.

15. To calculate the Chain Base index numbers.

16. To calculate consumer price index number.

(14)

For Enhancement of Employability

Appendix B B.O.S. 29.05.2015 DEPARTMENT OF STATISITICS & OPERATIONS RESEARCH

II Semester Course Code-STB2P1 Lab. Course - Probability

Credit: 2 Max Marks: 40+60 =100

1. Fitting of binomial distributions for n and p = q = ½ 2. Fitting of binomial distributions for n and p given

3. Fitting of binomial distributions computing mean and variance 4. Fitting of Poisson distributions for given value of lambda 5. Fitting of Poisson distributions after computing mean 6. Fitting of negative binomial distribution

7. Fitting of suitable distribution

8. Application problems based on binomial distribution 9. Application problems based on Poisson distribution

10.Application problems based on negative binomial distribution 11. Problems based on area property of normal distribution 12. To find the ordinate for a given area for normal distribution 13. Application based problems using normal distribution 14. Fitting of normal distribution when parameters are given Fitting of normal distribution when parameters are not given

(15)

For Enhancement of Employability

DEPARTMENT OF STATISTICS & OPERATIONS RESEARCH ALIGARH MUSLIM UNIVERSITY

B.A/B. Sc. Honours (Statistics) III Semester

Course Title – Lab. Course – Sampling Distributions Course Code - STB3P1

15 Credit 2

Max. Marks 40+60=100

PRACTICAL/LAB. WORK:

1. Testing of significance for single proportion and difference of two proportions 2. Testing of significance for single mean and difference of two means and paired tests.

3. Testing of significance for difference of two standard deviations.

4. Exact Sample Tests based on Chi-Square Distribution.

5. Testing if the population variance has a specific value and its confidence intervals.

6. Testing of goodness of fit.

7. Testing of independence of attributes.

8. Testing based on 2 x 2 contingency table without and with Yates’ corrections.

9. Testing of significance of an observed sample correlation coefficient.

10.Testing of equality of two population variances

(16)

For Enhancement of Employability

Appendix A B.O.S. 29.05.2015 DEPARTMENT OF STATISITICS & OPERATIONS RESEARCH

ALIGARH MUSLIM UNIVERSITY ALIGARH B.A./B.Sc.(Honours) (Statistics)

V Semester Course Code – STB5P1

Lab. I (Linear Models & Demography & Vit.)

Credit: 2 Max Marks: 40+60 = 100

Practical Based on the Courses:

STB552 STB554

DEPARTMENT OF STATISITICS & OPERATIONS RESEARCH ALIGARH MUSLIM UNIVERSITY ALIGARH

B.A. /B.Sc. (Honours) (Statistics) V Semester

Course Code – STB5P2 Lab. II (O.R.)

Credit: 2 Max Marks: 40+60 = 100

STB553

(17)

For Enhancement of Employability

B.A. /B.Sc. (Honours) (Statistics) V Semester

Course Code – STB5P3

Lab. III (Stat. Data Analysis Using R)

Credit: 2 Max Marks: 40+60 = 100

STB555

(18)

For Enhancement of Employability

Appendix B.O.S. 05.06.2017 DEPARTMENT OF STATISTICS & OPERATIONS RESEARCH

VI Semester Course Code – STB652

Statistical Computing Using C/C++ Programming

Credit: 4 Max Marks: 30+70 =100

Course objective: To introduce the basic elements statistical computing using C/C++

programming.

Course outcomes: On successful completion of this course the students will be able to

 Describe computer Programs in C/C++ related to statistical data analysis

 Write computer programs in C/C++ related to statistical data analysis.

Syllabus UNIT I

History and importance of C/C++. Components, basic structure programming, character set, C/C++ tokens, Keywords and Identifiers and execution of a C/C++ program. Data types: Basic data types, Enumerated data types, derived data types. Constants and variables: declaration and assignment of variables, Symbolic Constants, overflow and underflow of data.

Operators and Expressions: Arithmetic, relational, logical, assignment, increment/decrement, operators, precedence of operators in arithmetic, relational and logical expression. Implicit and explicit type conversions in expressions, library functions. Managing input and output operations:

reading and printing formatted and unformatted data.

UNIT II

Decision making and branching - if…else, nesting of if…else, else if ladder, switch, conditional (?) operator. Looping in C/C++: for, nested for, while, do…while, jumps in and out of loops.

Arrays: Declaration and initialization of one-dim and two-dim arrays. Character arrays and strings: Declaring and initializing string variables, reading and writing strings from Terminal (using scanf and printf only).

UNIT III

User- defined functions: A multi-function program using user-defined functions, definition of functions, return values and their types, function prototypes and calls. Category of Functions : no arguments and no return values, arguments but no return values , arguments with return values, no arguments but returns a value, functions that return multiple values. Recursion function. Passing arrays to functions, Storage class of Variables.

(19)

UNIT IV

Pointers: Declaration and initialization of pointer variables, accessing the address of a variable, accessing a variable through its pointer, pointer expressions, pointer increments/decrement and scale factor. Pointers and arrays, arrays of pointers, pointers as function arguments, functions returning pointers Structure: Definition and declaring, initialization, accessing structure members, copying and comparison of structure variables, array of structures, structure pointers. Dynamic memory allocation functions :malloc, calloc and free. Pre processors: Macro substitution, macro with argument File inclusion in C/C++: Defining and opening a file (only r, w and a modes), closing a file,I/O operations on files-fscanf and fprintf functions.

Suggested Reading:

1. Kernighan, B.W. and Ritchie, D. (1988): C Programming Language, 2ndEdition,Prentice Hall.

2. Balagurusamy, E. (2011): Programming in ANSI C, 6th Edition, Tata McGraw Hill.

3. Gottfried, B.S. (1998): Schaum’s Outlines: Programming with C, 2nd Edition, Tata McGraw Hill

For Enhancement of Employability

Appendix A B.O.S. 29.05.2015 DEPARTMENT OF STATISITICS & OPERATIONS RESEARCH

VI Semester Course Code – STB6P1

Lab. I (Econometrics & Design of Expt.)

Credit: 2 Max Marks: 40+60 = 100

STB65 STB654

B.A. /B.Sc. (Honours) (Statistics) VI Semester

Course Code – STB6P2

Lab. II (Stat. Comp. Using C/C++Prog.)

Credit: 2 Max Marks: 30+70 = 100

(20)

For Enhancement of Employability

Appendix A B.O.S. 05.06.2017 DEPARTMENT OF STATISTICS & OPERATIONS RESEARCH

VI Semester Course Code – STB6S1

Project

Credit: 4 Max Marks: 40+60 =100

Objective: The aim of the course is to initiate students to write and present a statistical report, under the supervision of a faculty, on some area of human interest. The project work will provide hands on training to the students to deal with data emanating from some real life situation and propel them to dwell on some theory or relate it to some theoretical concepts. For instance designing and conducting a sample survey and presenting a survey report.

(21)

For Enhancement of Employability

Appendix B/1 B.O.S--30-05-2019 DEPARTMENT OF STATISTICS AND OPERATIONS RESEARCH

ALIGARH MUSLIM UNIVERSITY, ALIGARH M.A/ M.Sc. (Statistics)

I-Semester Course Code-STM1022 Data Analysis with Python

Credit: 2 Max. Marks: 30+70=100

Objective of the course:

The main objective is to train the students for programming and data analysis with Python.

Unit I: Introduction to Python- Python data structures, data types, indexing and slicing, vectors, arrays, developing programs, functions, modules and packages, data structures for statistics, tools for statistical modeling, data visualization, data input and output.

Unit II: Display of Statistical data with Python- Univariate and multivariate data, discrete and continuous distributions: binomial, Poisson, normal, Weibull. Sampling distributions: t, chi- square and F.

Unit III: Hypothesis testing with Python- Test for means: t test for single and two samples, Wilcoxon and Mann-Whitney test, test for categorical data, one proportion and frequency tables, chi-square test for independence, relation between hypothesis and confidence intervals, one- and two -way ANOVA.

Unit IV: Statistical Modeling with Python-Correlation and Regression coefficients, simple and multiple regression analyses, model selection criteria, bootstrapping, generalized linear models.

References

1. Haslwanter, T. (2016): An Introduction to Statistics with Python: with Applications in the Life Sciences, Springer.

(22)

2. Sheppard, K. (2018): Introduction to Python for Econometrics, Statistics and Data analysis, Oxford University press.

For Enhancement of Employability

DEPARTMENT OF STATISITICS & OPERATIONS RESEARCH ALIGARH MUSLIM UNIVERSITY, ALIGARH

M.A/M.Sc. (Statistics) I-Semester Course Code-STM1071

Lab. Course – Based on STM1002, STM1012

Credit: 2 Max Marks:10+30+60=100

(23)

For Enhancement of Employability

Appendix B/2 BOS 30.05.2019 DEPARTMENT OF STATISITICS & OPERATIONS RESEARCH

ALIGARH MUSLIM UNIVERSITY, ALIGARH M.A/M.Sc. Statistics I-Semester

Course code-STM-1072

Lab. Course – Data Analysis with SPSS

Objective of course: Main objective of the course is to train the students in statistical data analysis using SPSS software package.

Credit: 2 Max Marks: 40+60=100

Unit I: Basics: Import and export of data files, recoding, computing new variables, selection of cases, splitting and merging of files. levels of measurement (types of data), summarizing variables using frequencies and descriptive statistics, bar charts, histograms and box plots, computation of simple, multiple, partial and rank correlation coefficients.

Unit II: Regression analysis: fitting of linear, parabolic, cubic and exponential models, multiple linear regression, variable selection, residual analysis for model adequacy, detection of outliers and influential observations.

Unit III: Testing of Hypothesis: Parametric tests; Tests based on t, F and chi square statistics.

Nonparametric tests; run test for randomness, sign test for location, median test, Mann-Whitney- Wilcoxon test, Kolmogorov-Smirnov test - one and two sample problems.

Unit IV: Analysis of variance: Analysis of one way and two way data, analysis of CRD, RBD and LSD, analysis 2³, 2⁴, 3² and 3³ factorial experiments, multiple comparison tests.

(24)

Books Recommended:

1. John MacInnes, An Introduction to Secondary Data Analysis with IBM SPSS Statistics, Sage 2017.

2 Marija Norusis, The SPSS Guide to Data Analysis, 1991.

3. Stephen A. Sweet, and Karen Grace-Martin, Data Analysis with SPSS: A First Course in Applied Statistics, 4th Edition, Pearson, 2012.

4. Pallant, Julie,SPSS Survival Manual, 4th Ed, McGraw-Hill, 2010.

5. Cronk, Brian, How to Use SPSS: A Step-By-Step Guide to Analysis and Interpretation,5th Ed., 2008

For Enhancement of Employability

Appendix: B BOS. 03.04.2018

Department of Statistics and Operations Research Aligarh Muslim University, Aligarh

M.A./M.Sc. II Semester (Statistics) Course Code:

STM-2022

Data Analysis with R

Credit: 2 Maximum Marks: 10 + 30 + 60 = 100 Unit I: R language and environment:

Basics of R, naming a data object, R is a functional language, creation of data objects including vectors, factors, matrices, list and data frames. Extraction from a data object. Input and output facilities.

Unit II: Univariate analysis:

Descriptive statistics and graphics, probability distributions in R, one -sample and two-sample tests, power and computation of sample size.

Unit III: Regression modeling:

Analysis of simple and multiple regression models, analysis of variance and analysis of deviance.

Fitting with optim ().

Unit IV: Documentation with R:

Interface of LaTex and R, basics of LaTex, concept of document class, using knitr with LaTex, Markdown tips, using knitr and Markdown.

(25)

Dalgaard P. (2008). Introductory Statistics with R, Springer.

Kleiber C and Zeileis A (2008) Applied Econometrics with R. Springer New York.

Lander J. P. (2014). R for Everyone: Advanced Analytics and Graphics, Pearson.

Xie, Y. (2015). Dynamic Documents with R and knitr (2nd edition), CRC Press.

For Enhancement of Employability

MA/MSc (Statistics) III-Semester Course Code-STM-3072

Lab. Course – Project

Credit: 4 Max Marks:10+30+60=100

(26)

For Enhancement of Employability

Appendix C BOS 30.05.2019 DEPARTMENT OF STATISTICS & OPERATIONS RESEARCH

ALIGARH MUSLIM UNIVERSITY, ALIGARH M.A./ M.Sc. I-Semester (Operations Research)

Course Code-ORM1021 Computing-I

Credit: 4 Max Marks: 30+70 =100

Unit I: Programming with FORTRAN 90/95 : Arithmetic Statements: Constants and Variable, names and types of constants and variables-Real and integers, arithmetic operators, arithmetic expressions-real and integer, mixed mode expressions, scalar relational operators, scalar logical expressions and assignments, built -in- mathematical functions, Input and output statements Specification statements, Format definition, unit numbers, internal files, formatted input, formatted output, list-directed I/O, carriage control, Edit descriptors, unformatted I/O, direct file and its processing.

Unit II: Control Constructs: Branching, IF Statements and Constructs, the Case Constructs.

Looping: Do While, Do and nested DO Constructs, Cycle and Exit statements. The GOTO statement.

Arrays Features, Elementary operations, where and forall statements and constructs. Functions and Subroutines: Statement Function, Function Subprograms and Subroutines, Calling a subprograms and subroutines.

(27)

Unit III: Introduction to Python: Python data structures, data types. indexing and slicing, vectors, arrays, developing programs, functions, modules and packages, data structures for statistics, tools for statistical modeling, data visualization, input and output.

Unit -IV: Software Packages: TORA and LINGO-Solution of simultaneous linear equations, Linear programming Problems, finding feasible and optimal solutions to primal and dual using Simplex and other Methods. Obtaining feasible and optimal solutions to Transportation and assignment models and finding optimal strategies in Zero-sum games

Suggested Readings:

1 Rajaraman V. (2015):Computer Programming in Fortran 90 And 95, PHI Learning Pvt Ltd.

Delhi

2 Metcalf, M. and Reid, J. (2000): FORTRAN 90/95 Explained, Oxford University Press.

3 Chapman, S.J. (1999): Introduction to FORTRAN 90/95, Tata McGraw Hill Publishing Company.

4 Salaria, R.S. (1999): A Modern Approach to Programming in Fortran, Khanna Book Publishing, Delhi

5 Haslwanter, T. (2016):An Introduction to Statistics with Python: With Applications in the Life Sciences, Springer.

6. Sheppard, K. (2018): An Introduction to Python for Econometrics, Statistics and Data Analysis, Oxford University Press.

7 H.A.Taha (2013) An Introduction to Operations Research,9th Edition, Prentice Hall, NJ.

8 LINGO User’s Guide (2013), LINDO systems Inc. U.S.

(28)

For Enhancement of Employability

Appendix A BOS 23.12.2015 DEPARTMENT OF STATISTICS & OPERATIONS RESEARCH ALIGARH MUSLIM

UNIVERSITY ALIGARH

M.A./ M.Sc. II Semester (Operations Research): CBCS Course Code-ORM2021

Data Analysis with Minitab, LINGO and R

Credit: 2 Max Marks: 40+60=100

UNIT I: Data Analysis-Concept of data types, scales of measurement. Meaning, purpose and method of data analysis. Classification and cross tabulation of data. Determination of sample size. Basic steps to design a questionnaire. Concept of hypothesis testing, level of confidence and significance and p value, Inferential analysis using t-test and chi-square, One way and two way ANOVA. Correlation and regression analysis. Screening of data. Statistical data analysis software. Issues to consider when choosing statistical software.

UNIT II: Minitab – Introduction to Minitab, Accessing Minitab, Minitab Worksheet, Menu and Session Commands, Entering Data, Doing Arithmetic’s, Generating Random numbers, Types of data and levels of measurement, Presenting Data in Tables and Charts, Histogram and normal probability curve, Stem-and-leaf, Box plots, Bar charts, Pie charts and scatter diagrams, Descriptive Measures and Measures of dispersion, Correlation coefficient, Regression Analysis, (simple and multiple), fitted line plots, stepwise regression, forward selection and backward elimination, logistic regression (Binary, Ordinal and Nominal), One Sample and Two Sample Tests of Hypothesis, Analysis of Variance, Chi-Square Test.

UNIT III-LINGO: Introduction, Using sets, set looping functions, set based modelling examples, variable domain functions, data, INIT and CALC Functions, Window Commands, Line commands, Operators and Functions, Interfacing with external Files and spreadsheets and developing models.

UNIT IV: R - Introduction to R language. Creation of data object, vector, factor and data frame.

Extraction operators in R, data import/export. Summary of data and statistical graphics with R.

The function curve. Linear Programming with R, Optimization with R Common distributions in R. Common statistical tests. Correlation and regression analysis.

 MINITAB Handbook – Jonathan D.Cryer, Barbara F.Ryan and Brian L. Joiner -Amazon, 2012

 Braun W.J.and Murdock D.J. (2007):A First Course in Statistical Programming with R, Cambridge University

 LINGO User Manual (Vol.I-III), LINDO Systems Inc.2011

(29)

For Enhancement of Employability

DEPARTMENT OF STATISTICS & OPERATIONS RESEARCH ALIGARH MUSLIM UNIVERSITY ALIGARH

M.A./ M.Sc. II Semester (Operations Research):

Course Code-ORM2072 Lab course based on (ORM2021)

M.A./M.Sc. (Operations Research) III-Semester

Course Code-ORM-3072 Lab. Course – Project

Credit: 4 Max Marks: 10+30+60=100