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Leokris [45]
3 years ago
9

Question 18 (1 point)

Mathematics
2 answers:
ZanzabumX [31]3 years ago
7 0
The answer Is fixed expense



A variable is a letter replacing a number
Naddik [55]3 years ago
6 0

Answer:

A) Fixed Expense

Step-by-step explanation:

Trust me

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Answer:

<em>a</em><em>)</em><em>=</em><em> </em><em>5x</em><em>+</em><em>10</em>

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<em>c</em><em>)</em><em>=</em><em> </em><em>6x-6</em>

<em>d</em><em>)</em><em>=</em><em> </em><em>2x</em><em>^</em><em>2</em><em>+</em><em>8x-5</em>

<em>e</em><em>)</em><em>=</em><em> </em><em>6x</em><em>+</em><em>9</em>

<em>f</em><em>)</em><em>=</em><em> </em><em>4xy-4y</em><em>^</em><em>2</em><em>+</em><em>8y</em>

<em>g</em><em>)</em><em>=</em><em> </em><em>-12y</em><em>^</em><em>2</em><em>+</em><em>10y</em>

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<em>i</em><em>)</em><em>=</em><em> </em><em>6x</em><em>+</em><em>21</em>

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What is the equation of the best-fitting regression line? Find this in two ways. First, using the regression analysis part of th
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Step-by-step explanation:

Regression analysis is used to infer about the relationship between two or more variables.

The line of best fit is a straight line representing the regression equation on a scatter plot. The may pass through either some point or all points or none of the points.

<u>Method 1:</u>

Using regression analysis the line of best fit is: y=\alpha +\beta x+e

Here <em>α </em>= intercept, <em>β</em> = slope and <em>e</em> = error.

The formula to compute the intercept is:

\alpha =\bar y-\beta \bar x

Here<em> </em>\bar y and \bar x are mean of the <em>y</em> and <em>x</em> values respectively.

\bar y=\frac{\sum y_{i}}{n} \\\bar x=\frac{\sum x_{i}}{n}

The formula to compute the slope is:

\beta =\frac{\sum (x-\bar x)(y - \bar y)}{\sum (x=\bar x)^{2}}

And the formula to compute the error is:

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<u>Method 2:</u>

The regression line can be determined using the descriptive statistics mean, standard deviation and correlation.

The equation of the line of best fit is:

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\sigma_{x} and \sigma_{y} are standard deviation of <em>x</em> and <em>y</em> respectively.

\sigma_{x}=\frac{1}{n}\sum (x-\bar x)^{2} \\\sigma_{y}=\frac{1}{n}\sum (y-\bar y)^{2}

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