Answer:
Sampling bias
Step-by-step explanation:
Bias refers a prominent problem in statistical analysis whereby one or more analytical factor are favored than the other during an analysis which should be made random. The problem. With Graham's dissertation study is the fact that he failed to randomlyvplace his subjects or observation in the study groups, favoring a particular group with non random subset. When randomization is ejected or missing from an analysis or study, it becomes less and less representative. Here, allotting early Arrivals Into the treatment group has introduced a sampling bias as those who came later, this will also leads to less reproducibility of experiment.
Answer:
The first answer is 4 and so is the next box. The last one is 10.
Step-by-step explanation:Use your formula. It gave you y=x-3, so every Y point in the able will be 3 less than its corresponding x point.
Answer:
B
Step-by-step explanation:
Let's call our estimate x. It will be the average of n IQ scores. Our average won't usually exactly equal the mean 97. But if we repeated averages over different sets of tests, the mean of our estimate the average would be the same as the mean of a single test,
μ = 97
Variances add, so the standard deviations add in quadrature, like the Pythagorean Theorem in n dimensions. This means the standard deviation of the average x is
σ = 17/√n
We want to be 95% certain
97 - 5 ≤ x ≤ 97 + 5
By the 68-95-99.7 rule, 95% certain means within two standard deviations. That means we're 95% sure that
μ - 2σ ≤ x ≤ μ + 2σ
Comparing to what we want, that's means we have to solve
2σ = 5
2 (17/√n) = 5
√n = 2 (17/5)
n = (34/5)² = 46.24
We better round up.
Answer: We need a sample size of 47 to be 95% certain of being within 5 points of the mean
