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Sagot :
Using the z-distribution, it is found that since the absolute value of the test statistic is less than the critical value, the difference in the sample proportions is not statistically significant at the 1% level.
At the null hypothesis, we test if the proportions are the same, that is, their subtraction is 0, hence:
[tex]H_0: p_1 - p_2 = 0[/tex]
At the alternative hypothesis, it is tested if they are different, that is, their subtraction is not 0, hence:
[tex]H_1: p_1 - p_2 \neq 0[/tex]
The proportions and their respective standard errors are given by:
[tex]p_1 = \frac{653}{1046} = 0.6343, s_1 = \sqrt{\frac{0.6343(0.3657)}{1046}} = 0.0149[/tex]
[tex]p_2 = \frac{791}{1327} = 0.5961, s_2 = \sqrt{\frac{0.5961(0.4039)}{1327}} = 0.0135[/tex]
For the distribution of the difference, the mean and the standard error are given by:
[tex]\overline{p} = p_1 - p_2 = 0.6343 - 0.5961 = 0.0382[/tex]
[tex]s = \sqrt{s_1^2 + s_2^2} = \sqrt{0.0149^2 + 0.0135^2} = 0.0201[/tex]
The test statistic is:
[tex]z = \frac{\overline{p} - p}{s}[/tex]
In which [tex]p = 0[/tex] is the value tested at the null hypothesis.
Hence:
[tex]z = \frac{\overline{p} - p}{s}[/tex]
[tex]z = \frac{0.0382 - 0}{0.0201}[/tex]
[tex]z = 1.9[/tex]
The critical value for a two-tailed test, as we are testing if two values are different, with a significance level of 0.01, is of [tex]|z^{\ast}| = 2.576[/tex]
Since the absolute value of the test statistic is less than the critical value, the difference in the sample proportions is not statistically significant at the 1% level.
A similar problem is given at https://brainly.com/question/25728144
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