I will attach google sheet that I used to find regression equation.
We can see that linear fit does work, but the polynomial fit is much better.
We can see that R squared for polynomial fit is higher than R squared for the linear fit. This tells us that polynomials fit approximates our dataset better.
This is the polynomial fit equation:

I used h to denote hours. Our prediction of temperature for the sixth hour would be:

Here is a link to the spreadsheet (
<span>https://docs.google.com/spreadsheets/d/17awPz5U8Kr-ZnAAtastV-bnvoKG5zZyL3rRFC9JqVjM/edit?usp=sharing)</span>
Answer:

Step-by-step explanation:
put them both in exponential form and then since they are both base 6, you can add the exponents.

First we need to write the null and alternate hypothesis for this case.
Let x be the average number of text message sent. Then
Null hypothesis: x = 100
Alternate hypothesis: x > 100
The p value is 0.0853
If p value > significance level, then the null hypothesis is not rejected. If p value < significance level, then the null hypothesis is rejected.
If significance level is 10%(0.10), the p value will be less than 0.10 and we reject the null hypothesis and CAN conclude that:
The mean number of text messages sent yesterday was greater than 100.
If significance level is 5%(0.05), the p value will be greater than 0.05 and we cannot reject the null hypothesis and CANNOT conclude that:
The mean number of text messages sent yesterday was greater than 100.
Answer:
2/5, 4/7, 5/2
Step-by-step explanation:
5/2=2.5 2/5=.40 4/7= .59 that's aproximately for 4/7
Answer:
(x, y) = (- 3, 4)
Step-by-step explanation:
Given the 2 equations
5x + 2y = - 7 → (1)
4x - y = - 16 → (2)
multiply all terms in (2) by 2
8x - 2y = - 32 → (3)
Add (1) and (3) term by term
(5x + 8x) + (2y - 2y) = (- 7 - 32)
13x = - 39 ( divide both sides by 13 )
x = - 3
Substitute x = - 3 into either (1) or (2) and solve for y
substituting in (1) gives
(5 × - 3) + 2y = - 7
- 15 + 2y = - 7 ( add 15 to both sides )
2y = 8 ( divide both sides by 2 )
y = 4
solution is (- 3, 4 )