a ) here we will use the chi-square test ins which step 1: we will introduce the null and alternative hypothesis
in step 2: we will define the rejection region
in step 3: we will find the computed value of tests statistic
in step 4: we will tell while we accept or reject the null hypothesis
in step 5 : we will find the conclusion
b ) let us consider H0: liking of sport associated with the country of origin
H1: liking of sport not associated with the country of origin
using excel we have
sport like most |
||||||||||
Observed Frequencies | ||||||||||
Column variable | Calculations | |||||||||
country of Origin | Football | Basketball | Other | None | Total | fo-fe | ||||
Namibia | 410 | 52 | 20 | 18 | 500 | 60 | -18 | -10 | -32 | |
Other SADC country | 220 | 58 | 12 | 10 | 300 | 10 | 16 | -6 | -20 | |
Rest of America | 57 | 15 | 10 | 18 | 100 | -13 | 1 | 4 | 8 | |
Elsewhere | 13 | 15 | 18 | 54 | 100 | -57 | 1 | 12 | 44 | |
Total | 700 | 140 | 60 | 100 | 1000 | |||||
Expected Frequencies | ||||||||||
Column variable | ||||||||||
country of Origin | Football | Basketball | Other | None | Total | (fo-fe)^2/fe | ||||
Namibia | 350 | 70 | 30 | 50 | 500 | 10.28571 | 4.628571 | 3.333333 | 20.48 | |
Other SADC country | 210 | 42 | 18 | 30 | 300 | 0.47619 | 6.095238 | 2 | 13.33333 | |
Rest of America | 70 | 14 | 6 | 10 | 100 | 2.414286 | 0.071429 | 2.666667 | 6.4 | |
Elsewhere | 70 | 14 | 6 | 10 | 100 | 46.41429 | 0.071429 | 24 | 193.6 | |
Total | 700 | 140 | 60 | 100 | 1000 | |||||
Data | ||||||||||
Level of Significance | 0.05 | |||||||||
Number of Rows | 4 | |||||||||
Number of Columns | 4 | |||||||||
Degrees of Freedom | 9 | |||||||||
Results | ||||||||||
Critical Value | 16.91898 | |||||||||
Chi-Square Test Statistic | 336.2705 | |||||||||
p-Value | 5.16E-67 | |||||||||
Reject the null hypothesis |
since the calculated value of the chi-square test is greater than critical value so we reject H0 and conclude that the liking of sport is not associated with the country of origin.
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