This equation has infinite solutions, any real number makes it true.
It could be 400,000 or 383,000
Answer:
Step-by-step explanation:
To find the population variance, we first need the mean:
so the mean is 7.2. To find the population variance (which is almost exactly the same as the sample variance except for a small difference in the denominators of the formula) we have to take each number minus the mean, and then square the difference. Add together all these squared numbers and then divide by the number of numbers. Like this:
Add together those numbers and divide them by 5:
Answer:
So you take the 172 miles and you divide it by the 3cm that you have and that gives you 57.3 miles. And therefore 1cm would be equal to 57.3
Step-by-step explanation:
Answer:
C) The Spearman correlation results should be reported because at least one of the variables does not meet the distribution assumption required to use Pearson correlation.
Explanation:
The following multiple choice responses are provided to complete the question:
A) The Pearson correlation results should be reported because it shows a stronger correlation with a smaller p-value (more significant).
B) The Pearson correlation results should be reported because the two variables are normally distributed.
C) The Spearman correlation results should be reported because at least one of the variables does not meet the distribution assumption required to use Pearson correlation.
D) The Spearman correlation results should be reported because the p-value is closer to 0.0556.
Further Explanation:
A count variable is discrete because it consists of non-negative integers. The number of polyps variable is therefore a count variable and will most likely not be normally distributed. Normality of variables is one of the assumptions required to use Pearson correlation, however, Spearman's correlation does not rest upon an assumption of normality. Therefore, the Spearman correlation would be more appropriate to report because at least one of the variables does not meet the distribution assumption required to use Pearson correlation.
