This is statistics course, sampling distributions, estimation and tests of significance units.I need help with this. I attached formula sheet in case you need it.

II. Probability P(AUB) = P(A) + P(B) - P(An B) P(AB) = P(An B) P(B) E(X) = Ux = Expi Var(X) = 0; = _(x - ux)2pi If X has a binomial distribution with parameters n and p, then: P(X = k) = k) p* (1 - p)" -* HI = np Or = Vnp(1 - p) Ho = P Op = P(1- p n If x is the mean of a random sample of size n from an infinite population with mean u and standard deviation O, then: MY = HIII. Inferential Statistics Standardized test statistic: statistic - parameter standard deviation of statistic Confidence interval: statistic + (critical value) . (standard deviation of statistic) Single-Sample Statistic Standard Deviation of Statistic Sample Mean Sample Proportion p(1 - p) n Two-Sample Statistic Standard Deviation of Statistic Difference of sample means 722 Special case when 61 =02 ol+1 n2 Difference of PI (1 - PI) + P2 (1 - P2) sample proportions n1 n2 Special case when P1 = P2 Jp(1 - P) 1 + 1 Vn, n2 Chi-square test statistic = \\ (observed - expected) expectedD Question 8 1 pts Consider the distribution for x shown below. Which of these best describes the shape of the sampling distribution of the sample means when n = 4 and when n = 35? O When n = 4 the sampling distribution will have some left skewness but will be more mound shaped than the given distribution and when n = 35 the sampling distribution will be approximately normal. O When n = 4 the sampling distribution will have some right skewness but will be more mound shaped than the given distribution and when n = 35 the sampling distribution will be approximately normal. O When n = 4 the sampling distribution will be uniform and when n = 35 the sampling distribution will be approximately normal. O When n = 4 and when n = 4 the sampling distributions will be approximately normal. O When n = 4 the sampling distribution will be approximately normal and when n = 35 the sampling distribution will have some right skewness but will be more mound shaped than the given distribution.