CHLH%20244%20L19%20Chapter%2012%20Part%20I%20S08_NQ

CHLH%20244%20L19%20Chapter%2012%20Part%20I%20S08_NQ -...

Info iconThis preview shows pages 1–6. Sign up to view the full content.

View Full Document Right Arrow Icon
Chi-Square Test I
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Parametric test and non-parametric test All the statistical tests we have studied thus far are designed to test the hypotheses about specific population parameters. These tests all concern parameters and require assumptions about parameters . They are called parametric test. Parameter tests require data from an interval or a ratio scale . Often we can be faced with the situations that do not meet the assumptions or requirements of parametric tests. In these situations, parametric tests are not appropriate tests. There are alternatives to parametric test, called non- parametric tests. No hypothesis statement or assumption on parameters (population distribution) distribution-free and parameter-free
Background image of page 2
Chi-Square test (non-parametric test) When assumptions concerning parameters and population distribution are not met When the scale of data are not interval and ratio Non-parametric tests are alternatives to parametric test. Chi-Square test is a commonly used non-parametric test.
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Types of tests and hypothesis statement in chi-square test Independence test between two variables H0: there is no association (relationship) between two variables H1: there is association between two variables Homogeneous test among subgroups H0: subgroups are homogeneous. H1: subgroups are not homogeneous. Goodness-of-fit test ( proportion difference between groups) Test hypothesis about the shape or proportions of a population distribution No preference H0: the population is divided equally among the categories No difference from a comparing population H0: the frequency distribution for one population is not different from the freq. distribution for another population.
Background image of page 4
Find critical values Degree of freedom = (Number of rows – 1) X (Number of columns – 1) In case of independence tests between two
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 6
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 29

CHLH%20244%20L19%20Chapter%2012%20Part%20I%20S08_NQ -...

This preview shows document pages 1 - 6. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online