MAT2001 Statistics for Engineers L 3 T 0 P 2 J 0 C 4 Prerequisites MAT1011 Calculus for Engineers Syllabus Version 1 1 Course Objectives 1 To provide students with a framework that will help them choose the appropriate descriptive methods in various data analysis situations 2 To analyse distributions and relationship of real time data 3 To apply estimation and testing methods to make inference and modelling techniques for decision making Expected Course Outcome At the end of the course the student should be able to 1 Compute and interpret descriptive statistics using numerical and graphical techniques 2 Understand the basic concepts of random variables and find an appropriate distribution for analysing data specific to an experiment 3 Apply statistical methods like correlation regression analysis in analysing 4 Make appropriate decisions using statistical inference that is the central to interpreting experimental data experimental research 5 Use statistical methodology and tools in reliability engineering problems 6 demonstrate R programming for statistical data Student Learning Outcome SLO 1 2 7 9 14 Module 1 Introduction to Statistics 6 hours Introduction to statistics and data analysis Measures of central tendency Measures of variability Moments Skewness Kurtosis Concepts only Module 2 Random variables 8 hours Introduction random variables Probability mass Function distribution and density functions joint Probability distribution and joint density functions Marginal conditional distribution and density functions Mathematical expectation and its properties Covariance moment generating function characteristic function Module 3 Correlation and regression 4 hours Correlation and Regression Rank Correlation Partial and Multiple correlation Multiple regression Module 4 Probability Distributions 7 hours Binomial and Poisson distributions Normal distribution Gamma distribution Exponential distribution Weibull distribution Module 5 Hypothesis Testing I 4 hours Testing of hypothesis Introduction Types of errors critical region procedure of testing hypothesis Large sample tests Z test for Single Proportion Difference of Proportion mean and difference of means Module 6 Hypothesis Testing II 9 hours Small sample tests Student s t test F test chi square test goodness of fit independence of attributes Design of Experiments Analysis of variance one and two way classifications CRD RBD LSD Module 7 Reliability 5 hours Basic concepts Hazard function Reliabilities of series and parallel systems System Reliability Maintainability Preventive and repair maintenance Availability Module 8 Contemporary Issues 2 hours Total Lecture hours 45 hours Industry Expert Lecture Text book s Probability and Statistics for engineers and scientists R E Walpole R H Myers S L Mayers and K Ye 9th Edition Pearson Education 2012 Applied Statistics and Probability for Engineers Douglas C Montgomery George C Runger 6th Edition John Wiley Sons 2016 Reference books Reliability Engineering E Balagurusamy Tata McGraw Hill Tenth reprint 2017 Probability and Statistics J L Devore 8th Edition Brooks Cole Cengage Learning Probability and Statistics for Engineers R A Johnson Miller Freund s 8th edition 2012 Prentice Hall India 2011 Probability Statistics and Reliability for Engineers and Scientists Bilal M Ayyub and Richard H McCuen 3rd edition CRC press 2011 Mode of Evaluation Digital Assignments Continuous Assessment Tests Quiz Final Assessment Test List of Experiments Indicative Introduction Understanding Data types 3 hours importing exporting data Computing Summary Statistics plotting and visualizing 3 hours data using Tabulation and Graphical Representations Applying correlation and simple linear regression model to real dataset computing and interpreting the coefficient of determination 3hours Applying multiple linear regression model to real dataset 3 hours computing and interpreting the multiple coefficient of determination Fitting the following probability distributions Binomial 3 hours distribution Normal distribution Poisson distribution Testing of hypothesis for One sample mean and proportion from real time problems 3 hours 3 hours Testing of hypothesis proportion from real time problems for Two sample means and 3 hours Applying the t test for independent and dependent samples 2 hours Applying Chi square test for goodness of fit test and 2 hours Contingency test to real dataset Performing ANOVA for Completely randomized design Randomized Block design Latin square Design for real dataset 2 hours Total laboratory hours 30 hours Mode of Evaluation Weekly Assessment Final Assessment Test Recommended by Board of Studies 25 02 2017 Approved by Academic Council 47 Date 05 10 2017