Answer:
Step-by-step explanation:
X Y X² Y² XY
993 50 986049 2500 49650
995 60 990025 3600 59700
994 60 988036 3600 59640
1006 40 1012036 1600 40240
942 120 887364 14400 113040
1002 40 1004004 1600 40080
![\sum XY : 362350](https://tex.z-dn.net/?f=%5Csum%20XY%20%3A%20362350)
To determine the regression:
![Mean \ (X) = \dfrac{\sum X }{n} \\ \\ = \dfrac{5932}{6} \\ \\ = 988.67](https://tex.z-dn.net/?f=Mean%20%5C%20%28X%29%20%3D%20%5Cdfrac%7B%5Csum%20X%20%7D%7Bn%7D%20%5C%5C%20%5C%5C%20%3D%20%5Cdfrac%7B5932%7D%7B6%7D%20%5C%5C%20%5C%5C%20%3D%20988.67)
![Mean \ (Y) = \dfrac{\sum Y}{n} \\ \\ = \dfrac{370}{6} \\ \\ = 61.67](https://tex.z-dn.net/?f=Mean%20%5C%20%28Y%29%20%3D%20%5Cdfrac%7B%5Csum%20Y%7D%7Bn%7D%20%5C%5C%20%5C%5C%20%3D%20%5Cdfrac%7B370%7D%7B6%7D%20%5C%5C%20%5C%5C%20%3D%2061.67)
Intercept ![b_o = \dfrac{\sum YX *\sum X^2 - \sum X \sum Y}{n(\sum X^2) - (\sum X)^2}](https://tex.z-dn.net/?f=b_o%20%3D%20%5Cdfrac%7B%5Csum%20YX%20%2A%5Csum%20X%5E2%20-%20%5Csum%20X%20%5Csum%20Y%7D%7Bn%28%5Csum%20X%5E2%29%20-%20%28%5Csum%20X%29%5E2%7D)
![=\dfrac{370(5867514) -(5932)(370)}{6(5867514) - (5932)^2}](https://tex.z-dn.net/?f=%3D%5Cdfrac%7B370%285867514%29%20-%285932%29%28370%29%7D%7B6%285867514%29%20-%20%285932%29%5E2%7D)
= 131760.9563
Slope ![b_1 = \dfrac{n(\sum XY) -(\sum X *\sum Y) }{n(\sum X^2)-(\sum X)^2}](https://tex.z-dn.net/?f=b_1%20%3D%20%5Cdfrac%7Bn%28%5Csum%20XY%29%20-%28%5Csum%20X%20%2A%5Csum%20Y%29%20%7D%7Bn%28%5Csum%20X%5E2%29-%28%5Csum%20X%29%5E2%7D)
![b_1 = \dfrac{6(362350) -(5932*370) }{6(5867514)-(5932)^2}](https://tex.z-dn.net/?f=b_1%20%3D%20%5Cdfrac%7B6%28362350%29%20-%285932%2A370%29%20%7D%7B6%285867514%29-%285932%29%5E2%7D)
![b_1 = -1.2600](https://tex.z-dn.net/?f=b_1%20%3D%20-1.2600)
The regression line equation ![Y = b_o +b_1X](https://tex.z-dn.net/?f=Y%20%3D%20b_o%20%2Bb_1X)
![Y = 131760.96 -1.2600 X](https://tex.z-dn.net/?f=Y%20%3D%20131760.96%20-1.2600%20X)
We then make a comparison of the slope of the equation to y = mx+c
slope of the equation = -1.2600
the y-intercept corresponds to when X = 0, thus:
y-intercept = 131760.9563
Yes, it is reasonable to interpret the y-intercept of the regression line, Using atmospheric pressure as an explanatory variable due to the fact that:
X is the independent variable and Y exists as the dependent variable.
Answer:
-11.8
Step-by-step explanation:
<u>Given:</u>
-3.4 to 12.4 increased (difference = 15.8)
12.4 to 20.4 decreased (difference = 8)
20.4 to 3.2 increased (difference = 17.2)
<u>To find:</u>
Final temperature.
Solution:
As we noticed, this is a pattern of increase, decrease, increase... (and so on and so forth). From this alone, we have gotten the clue that the temperature is going to decrease. But here is the thing, How far will it decrease?
Use the difference of the starting result to find the ending result.
3.2 - 15
= -11.8
Therefore, the final temperature is -11.8.
the answer for this question is all are not polynomials
9514 1404 393
Answer:
9 units
Step-by-step explanation:
The top base has a length that is the difference between the x-coordinates of points A and B:
0 -(-11) = 11
The bottom base has a length that is the difference between the x-coordinates of points C and D:
-1 -(-8) = 7
The mid-segment has a length that is the average of the base lengths:
(11 +7)/2 = 18/2= 9 . . . units; midsegment length
Answer:
y = -1/2x -3
Step-by-step explanation:
The slope intercept form of a line is
y = mx+b where m is the slope and b is the y intercept
y = -1/2x -3