Answer:
see below
Step-by-step explanation:
All of the given data sets have x-values that are sequential with a difference of 1. That makes it easy to determine the sort of sequence the y-values make.
<u>first choice</u>: the y-values have a common difference of -2. This will be matched by a linear model.
<u>second choice</u>: the y-values have a common difference of +2. Again, this will be matched by a linear model.
<u>third choice</u>: the y-values have a common ratio of -2. This will be matched by an exponential model.
<u>fourth choice</u>: the y-value differences are 3, 5, 7, increasing by a constant amount (2). This is characteristic of a sequence that has a quadratic model.
Answer:
16
Step-by-step explanation:
125=
16 days
1) -149, -1; -148, -2; -147, -3
2) No, according to the commutative property of addition, it does not matter the order they are put in.
Hope this helps!
Answer: a. The correlation coefficient of the data is positive.
Step-by-step explanation:
Estimated slope of sample regression line = 
Here , confidence interval : (-0.181, 1.529)
Estimated slope of sample regression line = 
![=\dfrac{1.348}{2}\\\\=0.674\ \ \ \ [\text{ positive}]](https://tex.z-dn.net/?f=%3D%5Cdfrac%7B1.348%7D%7B2%7D%5C%5C%5C%5C%3D0.674%5C%20%5C%20%5C%20%5C%20%5B%5Ctext%7B%20positive%7D%5D)
⇒Correlation coefficient(r) must be positive, So a. is true.
But, d. and e. are wrong(0.674 ≠ 0 or 1.348).
We cannot check residuals or its sum from confidence interval of slope of a regression line, so b is wrong.
We cannot say that scatterplot is linear as we cannot determine it from interval, so c. is wrong
So, the correct option : a. The correlation coefficient of the data is positive.
