SOLUTION
Set the hypothesis:
I will conduct a chi-square goodness of fit test of the null hypothesis.
Applying the chi-square goodness of fit test to sample data, we compute the degrees of freedom, the expected frequency counts, and the chi-square test statistic and the determine the p-value.
"DF=k-1=3-1=2(E_i)=n*p_i"
"E_1=100*0.30=30"
"E_2=100*0.60=60"
"E_3=100*0.10=10"
"X^2=\\sum [\\frac{(O_i -E_i)^2}{E_I}]"
"X^2=[\\frac{(50 -30)^2}{30}]+[\\frac{(45 -60)^2}{60}]+[\\frac{(5 -10)^2}{10}]"
"=\\frac{400}{30}+\\frac{225}{60}+\\frac{25}{10}"
"=13.33+3.75+2.50"
"=19.58"
The P-value is the probability that a chi-square statistic having 2 degrees of freedom is more extreme than 19.58.
We use the Chi-Square Distribution Calculator to find "P(X^2\\gt 19.58)=0.00005592=0.0001"
ANSWER: Since the P-value (0.0001) is less than the significance level (0.05), we reject the null hypothesis.
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