The line of best fit is a straight line that can be used to predict the
average daily attendance for a given admission cost.
Correct responses:
- The equation of best fit is;

- The correlation coefficient is; r ≈<u> -0.969</u>
<h3>Methods by which the line of best fit is found</h3>
The given data is presented in the following tabular format;
![\begin{tabular}{|c|c|c|c|c|c|c|c|c|}Cost, (dollars), x&20&21&22&24&25&27&28&30\\Daily attendance, y&940&935&940&925&920&905&910&890\end{array}\right]](https://tex.z-dn.net/?f=%5Cbegin%7Btabular%7D%7B%7Cc%7Cc%7Cc%7Cc%7Cc%7Cc%7Cc%7Cc%7Cc%7C%7DCost%2C%20%28dollars%29%2C%20x%2620%2621%2622%2624%2625%2627%2628%2630%5C%5CDaily%20attendance%2C%20y%26940%26935%26940%26925%26920%26905%26910%26890%5Cend%7Barray%7D%5Cright%5D)
The equation of the line of best fit is given by the regression line
equation as follows;
Where;
= Predicted value of the<em> i</em>th observation
b₀ = Estimated regression equation intercept
b₁ = The estimate of the slope regression equation
= The <em>i</em>th observed value

= 24.625
= 960.625

Therefore;

Therefore;
- The slope given to the nearest tenth is b₁ ≈ -4.9

By using MS Excel, we have;
n = 8
∑X = 197
∑Y = 7365
∑X² = 4939
∑Y² = 6782675
∑X·Y = 180930
(∑X)² = 38809
Therefore;

- The y-intercept given to the nearest tenth is b₀ ≈ 1,042
The equation of the line of best fit is therefore;
The correlation coefficient is given by the formula;

Where;


Which gives;

The correlation coefficient given to the nearest thousandth is therefore;
- <u>Correlation coefficient, r ≈ -0.969</u>
Learn more about regression analysis here:
brainly.com/question/14279500
X=0.84....................
8 - 8x is already in its simplest form.
Without knowing the value of x, you cannot find what is equivalent to 8 - 8x, but you can it if is an equation, such as 8 - 8x = 88 (x would be -10).
However, you can still rewrite it as -8x + 8, which is equivalent to 8 - 8x.
Answer:
I'm not sure, but I could kinda help
Step-by-step explanation:
to find the slope using a graph, using the formula :

I hope this helps
The answer to 1 is common multiple. And the answer to 2 is common denominator