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I need help with performing the \"Two Proportion Z-Test\". I dont have much expe

ID: 3174744 • Letter: I

Question

I need help with performing the "Two Proportion Z-Test". I dont have much experience with statistics, so please explain the variables, etc. I'm comparing the number of students in 2016 Mainstream classes vs the number of students in 1990 Mainstream classes. My hypothesis is that p1 (2016) > p2 (1990). The number of students in 1990 Mainstream was zero, and the number of students in 2016 Mainstream was 404, so p1 is definitely greater than p2. I need help with performing the calculation/formula. Thanks for the help.

Explanation / Answer

Solution:-

For performing the "Two Proportion Z-Test we need following data

n2016 = sample size in 2016

n1990 = sample size in 1990

x2016 = number of students in 2016 Mainstream

x1990 = number of students in 1990 Mainstream

'After this we find out

p2016 = x2016 / n2016

p1990 = x1990 / n1990

State the hypotheses. The first step is to state the null hypothesis and an alternative hypothesis.

Null hypothesis: P2016 = P1990

Alternative hypothesis: P2016 > P1990

Formulate an analysis plan. For this analysis, the significance level is 0.01. The test method is a two-proportion z-test.

Analyze sample data. Using sample data, we calculate the pooled sample proportion (p) and the standard error (SE). Using those measures, we compute the z-score test statistic (z).

p = (p1 * n1 + p2 * n2) / (n1 + n2)

p =

SE = sqrt{ p * ( 1 - p ) * [ (1/n1) + (1/n2) ] }

SE =

z = (p1 - p2) / SE

where p1 is the sample proportion in sample 1, where p2 is the sample proportion in sample 2, n1 is the size of sample 1, and n2 is the size of sample 2.

We use the Normal Distribution Calculator to find P(z < ) = ____ OR P(z > )

Interpret results. If the P-value (___) is greater than the significance level (0.05), we cannot reject the null hypothesis.

OR

If the P-value (___) is less than the significance level (0.05), we have to reject the null hypothesis. that means we accept the alternate hypothesis.

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