Page 243 - DMGT209_QUANTITATIVE_TECHNIQUES_II

Quantitative Techniques-II

Notes (3) Observations must be independent i.e., selection of any one item should not affect the
chances of selecting any others be included in the sample.

Did u know? What is univariate/bivariate data analysis?
Univariate

If we wish to analyse one variable at a time, this is called univariate analysis. For example:
Effect of sales on pricing. Here, price is an independent variable and sales is a dependent
variable. Change the price and measure the sales.

Bivariate
The relationship of two variables at a time is examined by means of bi-variate data
analysis.

If one is interested in a problem of detecting whether a parameter has either increased or
decreased, a two-sided test is appropriate.

12.4.2 Non-parametric Test

Non-parametric tests are used to test the hypothesis with nominal and ordinal data.

(1) We do not make assumptions about the shape of population distribution.
(2) These are distribution-free tests.
(3) The hypothesis of non-parametric test is concerned with something other than the value
of a population parameter.
(4) Easy to compute. There are certain situations particularly in marketing research, where
the assumptions of parametric tests are not valid. For example: In a parametric test, we
assume that data collected follows a normal distribution. In such cases, non-parametric
tests are used. Examples of non-parametric tests are (a) Binomial test (b) Chi-Square test
(c) Mann-Whitney U test (d) Sign test. A binominal test is used when the population has
only two classes such as male, female; buyers, non-buyers, success, failure etc. All
observations made about the population must fall into one of the two tests. The binomial
test is used when the sample size is small.

Advantages

1. They are quick and easy to use.

2. When data are not very accurate, these tests produce fairly good results.
Disadvantages

Non-parametric test involves the greater risk of accepting a false hypothesis and thus committing
a Type 2 error.

12.5 P-values

A p-value, sometimes called an uncertainty or probability coefficient, is based on properties of
the sampling distribution. It is usually expressed as p less than some decimal, as in p < .05 or
p < .0006, where the decimal is obtained by tweaking the significance setting of any statistical

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