Answer:
The answer is Option E; both B and C are correct
Step-by-step explanation:
Both (b) and (c) are correct. Simpson's paradox is a paradox in which a trend that appears in different groups of data disappears when these groups are combined, and the reverse trend appears for the aggregate data. This result is often encountered in social-science and medical-science statistics, and is particularly confounding when frequency data are unduly given causal interpretations. Simpson's Paradox disappears when causal relations are brought into consideration.
Now, this question is an example of Simpsons paradox because the groups of collected data over a period of time from five major cities showed a trend that StatsAir does better overall, but this trend is reversed when the groups are studied separately to show that air median does better.
So, option B is correct.
Also, City is a variable that influences both the dependent variable and independent variable, causing a spurious association. That is it is the cause of why the 2 results are biased. Thus, city is a lurking variable.
So, option C is also correct
Answer:
Step-by-step explanation:
The first question is asking you how many visitors there were, using the function model provided, when x = 0. Filling that in gives you:

Anything to the 0 power = 1, so
y = 18,582(1) and
y = 18,582
The second question is asking you how many visitors there were on the 9th weekend, when x = 9:
and
so
y = 7199
The last question is asking on what weekend (unknown x) are there
y = 15.051 visitors. That requires using a log to solve for x. Set it up first:

Start by dividing both sides by 18,582 to get:

Now we need to take the log of both sides to get that x out from its exponential position. By taking the log of the right side, we are given the ability to bring the x down in front:
log(.8099773975) = x log(.90)
Now divide both sides by log(.90) to get
x = 2.000
That means that on the second weekend, there were approximately 15,051 visitors.
Answer:
4 miles 30 8 miles 60 10 miles 75 minutes
The critical values corresponding to a 0.01 significance level used to test the null hypothesis of ρs = 0 is (a) -0.881 and 0.881
<h3>How to determine the critical values corresponding to a 0.01 significance level?</h3>
The scatter plot of the election is added as an attachment
From the scatter plot, we have the following highlights
- Number of paired observations, n = 8
- Significance level = 0.01
Start by calculating the degrees of freedom (df) using
df =n - 2
Substitute the known values in the above equation
df = 8 - 2
Evaluate the difference
df = 6
Using the critical value table;
At a degree of freedom of 6 and significance level of 0.01, the critical value is
z = 0.834
From the list of given options, 0.834 is between -0.881 and 0.881
Hence, the critical values corresponding to a 0.01 significance level used to test the null hypothesis of ρs = 0 is (a) -0.881 and 0.881
Read more about null hypothesis at
brainly.com/question/14016208
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