This document contains information about an assignment for a Quantitative Techniques course. It includes instructions to attempt one question from Section A and 10 questions from Section B. Section A focuses on decision making under certainty and uncertainty, while Section B includes short notes on various statistical topics such as classification of data, determinants, primary and secondary data, and double sampling. Students are provided contact information to get help from professionals in completing solved assignments.

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Xaviers Institute of Business Management Studies
Quantitative Techniques
Please attempt any one question out of section A and any 10 questions out of Section B.
The section A is for 20 Marks and Section B is for 60 Marks (6 Marks X 10 Questions)
Total Marks - 80
Section A
Question. 1. Distinguish between decision making under certainty and decision making under
uncertainty. Mention certain methods for solving decision problems under uncertainty. Discuss
how these methods can be applied to solve decision problems.
Question. 2. Distinguish between probability and non-probability sampling. Elucidate the
reasons for the use of non-probability sampling in many situations in spite of its theoretical
weaknesses.
Answer: What is non-probability sampling?
Non-probabilitysampling(sometimesnonprobabilitysampling) is a branch of sample selection that
uses non-random ways to select a group of people to participate in research.
Unlike probabilitysampling and its methods, non-probability sampling doesn’t focus on accurately
representing all members of a large

2. Question. 3. What are models? Discuss the role of models in decision-making. How can you
classify models on the basis of behavior characteristics?
Question. 4. What are matrices? How are determinants different from matrices? Discuss few
applications of matrices in business.
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Section B
Write short notes on any ten of the following:
(a) Concept of Maxima and Minima
(b) Types of classification of data
Answer: Classification of data
The methodof arranging data intohomogeneousclassesaccordingtothe commonfeatures present
in the data is known as classification.
A planneddataanalysissystemmakesthe fundamental dataeasytofind and recover. This can be of
particularinterestforlegal discovery,riskmanagement,and compliance. Written methods and sets
of guidelinesfordataclassificationshoulddetermine whatlevelsandmeasuresthe companywill use
to organise dataand define the rolesof employeeswithinthe businessregardinginputstewardship.
(c) Pascal Distribution
(d) Multi-stage sampling & Multi-phase sampling
(e) Box-Jenkins Models for Time Series
Answer:Box-Jenkins Methodology and autoregressive integrated moving-average (ARIMA) are
synonymous models applied to time series analysis and forecasting. The methodology for
identifying, fitting and checking appropriate ARIMA model studied extensively by Box and
Jenkins(1976). For this reason, ARIMA models of forecasting often referred to as Box-Jenkins
Methodology. ARIMA models are a class of linear models that capable of representing stationary
(without trend) and nonstationary (with

3. (f) Determinant of a Square Matrix
Answer: The determinant of a matrix is a number that is specially defined only for square
matrices. Determinantsare mathematical objects that are very useful in the analysis and solution of
systems of linear equations. Determinants also have wide applications in engineering, science,
(g) Primary and Secondary Data
Answer: What is Primary Data?
Primarydata isthe kindof data that iscollecteddirectlyfromthe datasource withoutgoing through
any existingsources.Itismostlycollectedspeciallyforaresearchprojectandmay be sharedpublicly
to be used for other
(h) Bernoulli Process
Answer: Bernoulli process: A sequence of Bernoulli trials is called a Bernoulli process. Among
other conclusions that could be reached, for n trials, the probability of n successes is pⁿ.
Bernoulli trial: A Bernoulli trial is an instantiation of a Bernoulli event. It is one of the simplest
experiments that can be
(i) The Student's t Distribution
Answer: The T distribution (also called Student’s T Distribution) is a family of distributions that
look almost identical to the normal distribution curve, only a bit shorter and fatter. The t
distribution is used instead of the normal distribution when you have small samples (for more
on this, see: t-score vs. z-score). The larger the sample size, the more the t distribution looks like
the normal distribution. In fact, for
(j) Use of Auto-correlations in identifying Time Series
Answer:A time series,asthe name suggests,isaseriesof data pointsthatare listedinchronological
order. More often than not, time series are used to track the changes of certain things over short
and long periods — with the price of stocks or even other commodities being a prime example.
Regardless, you’re taking a closer
(K) Absolute value function
(l) Quantiles

4. Answer: The word “quantile” comes from the word quantity. In simple terms, a quantile is where
a sample is divided into equal-sized, adjacent, subgroups (that’s why it’s sometimes called a
“fractile“). It can also refer to dividing a probability distribution into areas of equal probability.
The medianisa quantile;the medianisplacedina probability distribution sothat exactly half of the
data is lower than the median and half of the
(n) Cluster vs. Stratum
Answer: Differences Between Stratified and Cluster Sampling
The differences between stratified and cluster sampling can be drawn clearly on the following
grounds:
1. A probability sampling procedure in which the population is separated into different
homogeneous segments called ‘strata’, and then the sample is chosen from the each
stratum randomly, is called Stratified
(o) Moving average models
(p) Step function
(q) More than type ogive
(r) Subjectivist's criterion in decision making
(s) Double sampling
Answer:Double samplingisatwo-phase methodof samplingforanexperiment,researchproject, or
inspection.Aninitial samplingrunisfollowedbypreliminaryanalysis, after which another sample is
taken and more analysis is run.
It is used in three main
(t) Auto regressive models
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