<span>the answer is : 28.8604651163
( I Used A Calculator )
</span>
The best answer from the options that proves that the residual plot shows that the line of best fit is appropriate for the data is: ( Statement 1 ) Yes, because the points have no clear pattern
X Given Predicted Residual value
1 3.5 4.06 -0.56
2 2.3 2.09 0.21
3 1.1 0.12 0.98
4 2.2 -1.85 4.05
5 -4.1 -3.82 -0.28
The residual value is calculated as follows using this formula: ( Given - predicted )
1) ( 3.5 - 4.06 ) = -0.56
2) ( 2.3 - 2.09 ) = 0.21
3) ( 1.1 - 0.12 ) = 0.98
4) (2.2 - (-1.85) = 4.05
5) ( -4.1 - (-3.82) = -0.28
Residual values are the difference between the given values and the predicted values in a given data set and the residual plot is used to represent these values .
attached below is the residual plot of the data set
hence we can conclude from the residual plot attached below that the line of best fit is appropriate for the data because the points have no clear pattern ( i.e. scattered )
learn more about residual plots : brainly.com/question/16821224
Answer:
340 customers.
Step-by-step explanation:
1 out of 4 = 25% purchase rate
85 × 4 = 340
To check our work:
25% of 340 = 0.25 × 340 = 85
Answer:
48r
Step-by-step explanation:
6(8r)
=6×(8r)
=(48r)
Answer:

Step-by-step explanation:
The first step is to realize that this is a geometric sequence, where each next term in the sequence is 2.5 times larger than the previous. You can confirm this by the fact that 3=1.2*2.5, 7.5=3*2.5, and 18.75=7.5*2.5. Now, you can write the formula, where n is where the number is in the sequence, and a is the number itself. Since 1.2 is the first term in the sequence and the rate of expansion is 2.5, the formula is:

Hope this helps!