Answer:
Correct.
Step-by-step explanation:
This is the perimeter formula for a rectangle.
Perimeter is l + l + w + 2
p = 2l + 2w
Using correlation coefficients, it is found that the -0.63 correlation between number of absences and final exam score means that there is a strong negative correlation between number of absences and final exam score.
<h3>What is a correlation coefficient?</h3>
It is an index that measures correlation between two variables, assuming values between -1 and 1.
If it is positive, the relation is positive, that is, they are direct proportional. If it is negative, they are inverse proportional.
If the absolute value of the correlation coefficient is greater than 0.6, the relationship is strong.
In this problem, the correlation is of -0.63, hence:
It means that that there is a strong negative correlation between number of absences and final exam score.
To learn more about correlation coefficients, you can take a look at brainly.com/question/25815006
Answer:
The nearest ten thousand is 80,000.
Answer:
Step-by-step explanation:
The two x intercepts are
x - 7 = 0
x = 7
x + 3= 0
x = - 3
The y intercept is just - 7 * 3 = - 21
To prove this more formally
f(x) = (x - 7)(x + 3)
f(x) = x^2 - 7x + 3x - 21
f(x) = x^2 - 4x - 21
Answer:
Since the p value is lower than the significance level we have enough evidence to reject the null hypothesis and is not enough evidence to conclude that the claim is true.
Step-by-step explanation:
Data given
represent the sample mean
represent the sample standard deviation
sample size
represent the value that we want to test
represent the significance level for the hypothesis test.
t would represent the statistic (variable of interest)
represent the p value for the test (variable of interest)
System of hypothesis
We need to conduct a hypothesis in order to check if the true mean exceed 29.9 or no, the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
The statistic is given by:
(1)
Calculate the statistic
We can replace in formula (1) the info given like this:
P-value
The degrees of freedom are given by:
Since is a one sided test the p value would be:
Conclusion
Since the p value is lower than the significance level we have enough evidence to reject the null hypothesis and is not enough evidence to conclude that the claim is true.