Given the table below representing the number of hours of television nine Math II class students watched the night before a big test on
triangles
along with the grades they each earned on that test.
![\begin{center} \begin{tabular} {|c|c|} Hours Spent Watching TV & Grade on Test (out of 100) \\ [1ex] 4 & 71 \\ 2 & 81 \\ 4 & 62 \\ 1 & 86 \\ 3 & 77 \\ 1 & 93 \\ 2 & 84 \\ 3 & 80 \\ 2 & 85 \end{tabular} \end{center}](https://tex.z-dn.net/?f=%5Cbegin%7Bcenter%7D%0A%5Cbegin%7Btabular%7D%0A%7B%7Cc%7Cc%7C%7D%0AHours%20Spent%20Watching%20TV%20%26%20Grade%20on%20Test%20%28out%20of%20100%29%20%20%5C%5C%20%5B1ex%5D%0A4%20%26%2071%20%5C%5C%20%0A2%20%26%2081%20%5C%5C%20%0A4%20%26%2062%20%5C%5C%20%0A1%20%26%2086%20%5C%5C%20%0A3%20%26%2077%20%5C%5C%20%0A1%20%26%2093%20%5C%5C%20%0A2%20%26%2084%20%5C%5C%20%0A3%20%26%2080%20%5C%5C%20%0A2%20%26%2085%0A%5Cend%7Btabular%7D%0A%5Cend%7Bcenter%7D)
Let the number the number of hours of television each of the students watched the night before the test be x while the grades they each earned on that test be y.
We use the following table to find the equation of the line of best fit of the regression analysis of the data.
![\begin{center} \begin{tabular} {|c|c|c|c|} x & y & x^2 & xy \\ [1ex] 4 & 71 & 16 & 284 \\ 2 & 81 & 4 & 162 \\ 4 & 62 & 16 & 248 \\ 1 & 86 & 1 & 86 \\ 3 & 77 & 9 & 231 \\ 1 & 93 & 1 & 93 \\ 2 & 84 & 4 & 168 \\ 3 & 80 & 9 & 240 \\ 2 & 85 & 4 & 170 \\ [1ex]\Sigma x=22 & \Sigma y=719 & \Sigma x^2=64 & \Sigma xy=1,682 \end{tabular} \end{center}](https://tex.z-dn.net/?f=%5Cbegin%7Bcenter%7D%20%5Cbegin%7Btabular%7D%20%7B%7Cc%7Cc%7Cc%7Cc%7C%7D%20x%20%26%20y%20%26%20x%5E2%20%26%20xy%20%5C%5C%20%5B1ex%5D%204%20%26%2071%20%26%2016%20%26%20284%20%5C%5C%202%20%26%2081%20%26%204%20%26%20162%20%5C%5C%204%20%26%2062%20%26%2016%20%26%20248%20%5C%5C%201%20%26%2086%20%26%201%20%26%2086%20%5C%5C%203%20%26%2077%20%26%209%20%26%20231%20%5C%5C%201%20%26%2093%20%26%201%20%26%2093%20%5C%5C%202%20%26%2084%20%26%204%20%26%20168%20%5C%5C%203%20%26%2080%20%26%209%20%26%20240%20%5C%5C%202%20%26%2085%20%26%204%20%26%20170%20%5C%5C%20%5B1ex%5D%5CSigma%20x%3D22%20%26%20%5CSigma%20y%3D719%20%26%20%5CSigma%20x%5E2%3D64%20%26%20%5CSigma%20xy%3D1%2C682%20%5Cend%7Btabular%7D%20%5Cend%7Bcenter%7D)
Recall that the equation of the line of best fit of a regression analysis is given by

where:

and


Thus, the equation of the line of best fit is given by y = 97.95 - 7.391x
<span>A student that watched 1.5 hours of TV will have a score given by
y = 97.95 - 7.391(1.5) = 97.95 - 11.0865 = 86.8635
Therefore, </span><span>a student’s score if he/she watched 1.5 hours of TV to the nearest whole number is 87.</span>
Answer:


Step-by-step explanation:
Given: See Attachment
Required
Determine the length of the legs
To do this, we apply Pythagoras theorem.

In this case:

Open Bracket



Collect Like Terms


Solving using quadratic formula:

So:
or 
Since, x can't be negative, then:

One of the leg is:






Answer:
11/18
Step-by-step explanation:
The desired probability is the sum of ...
... (probability of choosing a coin) × (p(heads) on that coin)
Since the coins are chosen at random, we assume the probability of choosing a given coin is 1/3. Then ...
... p(heads) = (1/3)·(1/2) + (1/3)·1 + (1/3)·(1/3) = 1/6 + 1/3 + 1/9 = (3 +6 + 2)/18
... p(heads) = 11/18
Answer:
hello, This can be solved using your standard rise over run slope calculation. Think of the number of years after 1990 as your input variable. 2007 would be 17 years after 1990, so the "y" variable would be 17. This is your "run", and it goes in the denominator of your slope calculation. Then the difference between the 2007 population and the 1990 population would be the "p" variable difference, the "rise", which is 92500. Dividing, you will get 5441.17. Since we're talking about humans, we can't have a decimal, so let's round up to 5442.
Using the slope-intercept form of a linear equation (y = ax + b), we will say that p = 5442y + 123000. Do you know why we add the 123000? It's because that's the starting value, which is equivalent to the "intercept" in the slope-intercept form.
Hope this helps.
Step-by-step explanation:
Separate it into two different shapes