Answer:
The correlation between exam score and amount of time spent on the exam is an example of a <u>negative correlation</u>.
Step-by-step explanation:
Consider the provided statement.
College professor reports that students who finish exams early tend to get better grades than students who hold on to exams until the last possible moment.
That means the student who finish the exam early will get the highest marks. One who finish after the first student will get second highest mark and the one who finish in the end will get the least marks.
Now consider time as first variable and marks as second variable.
That means as the first variable increase the second variable decreases.
According to the definition of Negative correlation: It is a relationship between two variables in which one variable increases as the other decreases, and vice versa.
Hence, the correct answer is negative correlation.
Answer:
The answer is 8°
Step-by-step explanation:
2x + 62° = 5x + 38°
2x – 2x + 62° – 38° = 5x – 2x + 38° – 38°
24° = 3x
3x = 24°
3x/3 = 24°/3
x = 8°
Thus, The value of x is 8°
<u>-TheUnknownScientist 72</u>
Answer:
Where T is the hight temperature and t is the day
Explanation:
The table shows that every day the<em> High temperature, T (degrees F) </em>increases 1 unit.
Then, this is a linear function with slope = 1ºF / day.
You can use the point-slope equation to determine the <em>function of the high temperature:</em>

- Choose any point from the table and m = 1. I will use the first point (1,95)
Substitute:

Make the variables y = T, and x = t:

First multiply (-5/6) and (1/6) to get

then you subtract that from (8/9) to get

final answer is
this question is incomplete, the complete question is:
1. is this model effective
2. what is the correlation coefficient for this data.
3. for a student with a bmi of 25, what is the predicted number of hours under the influence.
Step-by-step explanation:
1. first of all this model is not effective because we have r² as 0.134. this tells us that only 13.4 percent of the of the variations that exist in this data has been explained by the model
1. we get the correlation coefficient by

the regression slope coefficient has a negative sign. this is what we would use in calculating the correlation coefficient.

= -√0.134
= -0.366
therefore the correlation coefficient is -0.366
2. to get the number of hours under the influence with a bmi of 25
the equation is
49.2-1.15bmi
= 49.2-1.15(25)
= 49.2-28.75
= 20.45