NewSyllabus 1520202012884172
NewSyllabus 1520202012884172
NewSyllabus 1520202012884172
2. Basic statistics
The engineering method and statistical thinking, collecting engineering data (Basic principles, retrospective study,
observational study, designed experiments, observing processes over time); mechanistic and empirical models; 10%
numerical summaries of data, stem-and-leaf diagrams, frequency distributions and histograms, box plots, time
sequence plots, scatter diagrams
probability and probability models, Bayes’ theorem, random variables, Discrete probability distributions; discrete
uniform distribution, binomial distribution, Poisson distribution, introduction to geometric and negative binomial
distributions, hypergeometric distribution; Continuous probability distributions; uniform distribution, normal
distribution, introduction to exponential distribution, Erlang and gamma distributions, Weibull distribution, lognormal
distribution and beta distribution
Estimation: Point estimation sampling distributions and the central limit theorem, Statistical intervals for a single
sample; confidence interval on the mean of a normal distribution, variance known confidence interval on the mean of a 15%
normal distribution, variance unknown (t distribution, t confidence interval on µ), confidence interval on the variance and
standard deviation of a normal distribution, large-sample confidence interval for a population proportion
Tests of hypotheses for a single sample; tests on the mean of a normal distribution, variance known and variance 20%
unknown cases, tests on a population proportion, Statistical inference for two samples; inference on the difference in
means of two normal distributions, variances known and variances unknown cases, paired t-test, inference on two
population proportions. Nonparametric procedures (the sign test, the wilcoxon signed-rank test, comparison to the t-
test), a nonparametric test for the difference in two means (description of the wilcoxon rank-sum test, large-sample
approximation, comparison to the t-test)
7 Module IV: Regression and Correlation (Using statistical software package only)
Simple linear regression and correlation; properties of the least squares estimators, hypothesis tests in simple linear
regression, adequacy of the regression model (residual analysis, coefficient of determination (R2), correlation, Multiple 20%
linear regression; least squares estimation of the parameters, hypothesis tests in multiple linear regression , prediction
of new observations, model adequacy checking, Aspects of multiple regression modeling; polynomial regression
models, categorical regressions and indicator variables, selection of variables and model building, multicollinearity.
Design and analysis of single-factor experiments: the ANOVA; designing engineering experiments, completely 20%
randomized single-factor experiment (analysis of variance, multiple comparisons following the ANOVA, residual
analysis and model checking), the random-effects model (fixed versus random factors, ANOVA and variance
components), randomized complete block design (design and statistical analysis, multiple comparisons, residual
analysis and model checking).
Design of experiments with several factors; introduction, factorial experiments, two-factor factorial experiments
(statistical analysis of the fixed-effects model, model adequacy checking, one observation per cell), general factorial
experiments, 2k factorial designs (22 design, 2k design for k ≥ 3 factors, single replicate of the 2k design, addition of
center points to a 2k design), blocking and confounding in the 2k design, fractional replication of the 2k design (one-half
fraction of the 2k design, smaller fractions; the 2k-p fractional factorial), response surface methods and designs
Module VI: Statistical Quality Control (Using statistical software package only)
Statistical quality control; quality improvement and statistics, introduction to control charts, X and R or S control charts, 15%
control charts for individual measurements, process capability, attribute control charts P chart, U chart, control chart
performance, time-weighted charts (cumulative sum control chart, exponentially weighted moving average control
chart).
Weightage (%)
10% 8% 7% 5% 70%
References
Devore, J. L. (2012). Probability and Statistics for Engineering and the Sciences. Cengage Learning.
Montgomery, D. C., & George, R. C. (2016). Applied Statistics and Probability for Engineers. Wiley.
Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability & Statistics for Engineers & Scientists. Prentice Hall.