Chapter 5
Incomplete Block Designs
If the number of treatments to be compared is large, then we need large number of blocks
accommodate all the treatments. This requires more experimental material and
NPTEL Syllabus
Probability Methods in Civil Engineering - Web
course
COURSE OUTLINE
NPTEL
Concept of probability and statistics is very important to solve various civil engineering
problems. In this v
Module 7: Probability and Statistics
Lecture 6: Regression Analyses and Correlationcontd.
1. Introduction
In the previous lecture, formulations and problems on linear regression were discussed. The
ba
Module 7: Probability and Statistics
Lecture 4: Goodness of fit tests
1. Introduction
In the previous two lectures, the concepts, steps and applications of Hypotheses testing were
discussed. Hypothese
Module 7: Probability and Statistics
Lecture 3: Hypothesis Testing
1. Introduction
In the previous lecture, Hypothesis testing was introduced and the various types of sampling
errors were discussed. T
Module 7: Probability and Statistics
Lecture 1: Sampling Distribution and Parameter Estimation
1. Introduction
This lecture deals with Sampling distribution and Estimation of parameters. Random
sampli
Module 6: Common Probability Models
Lecture 4: Probability Models using Discrete Probability Distributions
1. Introduction
While the preceding three lectures of this module dealt with continuous proba
Module 7: Probability and Statistics
Lecture 1: Sampling Distribution and Parameter Estimation contd.
1. Introduction
In the previous lecture, Sampling distribution, Point estimation and Interval esti
M5L10
Functions of Multiple Random Variables-3
1. Introduction
So far in this module, the details about one function of two random variables are discussed.
However, there may exist more than one funct
Module 6: Common Probability Models
Lecture 1: Probability Models using Normal Distribution
1. Introduction
This module introduces some common probability models which find widespread application
in C
M5L11
Introduction to Copulas
1. Introduction
In this lecture, the theory of copula is introduced. The definition of copula function with
explanation, its properties, basic terminologies, Frchet-Hoeff
Module 6: Common Probability Models
Lecture 2: Probability Models using Log Normal and Exponential Distribution
1. Introduction
The previous lecture dealt with the pdf of Normal distribution, the norm
Module 7: Probability and Statistics
Lecture 5: Regression Analyses and Correlation
1. Introduction
This lecture presents the theory and formulations of linear regression with constant and non
constan
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August, 2014
Vol 3 Issue 8
ISSN 2278 0211 (Online)
EIA of Bandra Worli Sea Link
Charu Joshi
Department of Civil Engineering, College of Technology
G. B. Pant University of Agriculture an
Chapter 17
Simultaneous Equations Models
In any regression modeling, generally an equation is considered to represent a relationship describing a
phenomenon.
Many situations involve a set of relations
Chapter 4
Experimental Design and Their Analysis
Design of experiment means how to design an experiment in the sense that how the observations or
measurements should be obtained to answer a query in a
Chapter 16
Measurement Error Models
A fundamental assumption in all the statistical analysis is that all the observations are correctly measured. In
the context of multiple regression model, it is ass
Chapter 13
Asymptotic Theory and Stochastic Regressors
The nature of explanatory variable is assumed to be non-stochastic or fixed in repeated samples in any
regression analysis. Such an assumption is
Chapter 11
Specification Error Analysis
The specification of a linear regression model consists of a formulation of the regression relationships and of
statements or assumptions concerning the explana
Chapter 14
Stein-Rule Estimation
The ordinary least squares estimation of regression coefficients in linear regression model provides the
estimators having minimum variance in the class of linear and
Chapter 6
Balanced Incomplete Block Design (BIBD)
The designs like CRD and RBD are the complete block designs. We now discuss the balanced
incomplete block design (BIBD) and the partially balanced inc
MSO 201a: Probability and Statistics
2016-2017-II Semester
Assignment-IX
A. Illustrative Discussion Problems
1. Let X = (X1 , X2 , X3 )0 be a discrete random vector with p.m.f.
(
fX (x1 , x2 , x3 ) =
MSO 201a: Probability and Statistics
2016-2017-II Semester
Assignment-V
A. Illustrative Discussion Problems
1. (a) Find the moments of the random variable that has the m.g.f. M (t) = (1 t)3 ,
t < 1.
(
Chapter 15
Instrumental Variables Estimation
A basic assumption in analyzing the performance of estimators in a multiple regression is that the
explanatory variables and disturbance terms are independ
MSO 201A: PROBABILITY & STATISTICS
Summer Semester: 2017
Assignment 10
Instructor: Shalabh
1. Let
cfw_X n
be a sequence a random variables with E ( X n ) c and V ( X n ) 0 as n . Show
p
that X n c.
2.
Module 6: Common Probability Models
Lecture 3: Probability Models using Gamma and Extreme Value Distribution
1. Introduction
In the previous lecture, discussions on Lognormal and Exponential distribut
M5L12
Introduction to Copulas-2
1. Introduction
This is the second lecture on copula function. We have discussed on the definition of copula
function with explanation, its properties, basic terminolog
M5L9
Functions of Multiple Random Variables-2
1. Introduction
In this lecture, various statistical properties of one function of two random variables are
discussed. The sum and difference of independe
Module 3: Random Variables
Lecture 4: Descriptors of Random Variables (contd.)
Measure of Skewness
The skewness of a random variable indicates asymmetry of its probability distribution. A
measure of s