Chi-Square

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CHI-SQUARE

Chi-Square



Chi Square Assignment

Introduction

In this assignment, we will calculate a Chi Square and provide interpretations for it using the terminology we have learned in this course. Since this involves the determination if the distribution of one variable is contingent on the second variable, this assignment requires an analysis of a contingency table. It is a statistical test to determine the significance of the difference in the observed frequencies.

Test of independence (Chi-square)

The independence test Chi-square allows us to determine whether there is a relationship between two categorical variables. It should be stressed that this test tells us whether there is a relationship between variables, but does not indicate the degree or type of relationship, that is, does not indicate the percentage of influence of one variable on another variable or causing influence (Plackett, 2008). A chi-square test is used with you have variables that are categorical rather than continuous.

The data I created is on the basis of questions ask by 54 respondents that how they enjoy the sports of tennis. Either by watching on TV, watching at the stadium or by playing themselves. After collecting the data, I used SPSS and run Chi-Square test. The result is as follows along with the questions of Exercise 1-4.

NPar Tests

Notes

Output Created

04-Nov-2011 11:57:17

Comments

Input

Data

E:\chi square 1.sav

Active Dataset

DataSet1

Filter



Weight

Number who preferred mode of enjoying tennis

Split File



N of Rows in Working Data File

3

Missing Value Handling

Definition of Missing

User-defined missing values are treated as missing.

Cases Used

Statistics for each test are based on all cases with valid data for the variable(s) used in that test.

Syntax

NPAR TEST

/CHISQUARE=number

/EXPECTED=EQUAL

/MISSING ANALYSIS.

Resources

Processor Timea

00:00:00.000

Elapsed Time

00:00:00.000

Number of Cases Allowed

196608

a. Based on availability of workspace memory.

[DataSet1] E:\ chi square 1.sav

Chi-Square Test

Frequencies

Number who preferred mode of enjoying tennis

Observed N

Expected N

Residual

Play

13

18.0

-5.0

Watch on TV

17

18.0

-1.0

Watch at stadium

24

18.0

6.0

Total

54

Test Statistics

Number who preferred mode of enjoying tennis

Chi-Square

3.444a

df

2

Asymp. Sig.

.179

a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected ...
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