Answer:
1) The calculated statistic is in the acceptance region so the decision is to not reject the null hypothesis. This means that at a 5% level the population proportion of couples that lean to right while kissing is 2/3.
2) P(84 ≤ X ≤ 87) = 0.21226
Step-by-step explanation:
Hello!
1)
The study variable is:
X: Number of couples that lean to the right while kissing out of 130 kissing couples.
This variable is discrete and has a binomial distribution, the success of the binomial experiment is "lean to the right" and the study parameter is the population proportion of couples that lean to the right while kissing (p)
The hypothesis is that 2/3 of people leans to the right while kissing, symbolically: p= 2/3 (or p= 0.67)
So the statistical hypothesis is:
H₀: p= 2/3
H₁: p≠2/3
α: 0.05
This test has a two-tailed critical region and the distribution to use is the standard normal.
If Z≤ -1.96 or if Z ≥ 1.96 you will reject the null hypothesis.
Data:
Sample n= 135
Sample proportion 'p= 84/135= 0.62 (28/45)
≈ N(0;1)
Z= -1.10
The calculated statistic is in the acceptance region so the decision is to not reject the null hypothesis. This means that at a 5% level the population proportion of couples that lean to right while kissing is 2/3.
2)
If 2/3 of all kissing couples exhibit this right-leaning behavior, what is the probability that the number in a sample of 130 who do so differs from the expected value by at least as much as what was actually observed? (Round your answer to three decimal places.)
The expected number of right kissers is calculated as:
'x= n*p= 130*2/3= 86.67≅ 87
You need to calculate the probability of the kissers being between the expected (86) and observed (84) coumples:
Remember using CLT we approximated the distribution of the sample proportion to normal: 'p ≈ N(np, np(1-p))
Mean= np= 86.67≅ 87
Standard deviation: √np(1-p)= √130*2/3(1-2/3)= 5.37
P(84 ≤ X ≤ 87) = P(X ≤ 87) - P(X ≤ 84)
P(Z ≤ (87-87)/5.37) - P(Z ≤ (84-87)/5.37) = P(Z ≤ 0) - P(Z ≤ -0.56) = 0.5 - 0.28774= 0.21226