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Kay [80]
1 year ago
14

Which line is perpendicular to the line 3y - 4x = 6

Mathematics
1 answer:
Scilla [17]1 year ago
6 0

The equation of the line that is perpendicular to the line 3y - 4x = 6 is: c. 4y + 3x = 8.

<h3>How to Determine the Equation of Lines that are Perpendicular?</h3>

The lines that are perpendicular to each other have slope value of their equation, m, that are negative reciprocals of each other or the product of their slopes is equal to -1.

Given the line 3y - 4x = 6, rewrite the equation in slope-intercept form, y = mx + b, and determine the value of m:

3y = 4x + 6

Divide both sides by 3

3y/3 = 4x/3 + 6/3

y = 4/3x + 2

The slope (m) = 4/3.

The slope of the line that would be perpendicular to the line 3y - 4x = 6, will have a slope of -3/4, which is the negative reciprocal of 4/3.

Rewrite 4y + 3x = 8 in slope-intercept form:

4y = -3x + 8

4y/4 = -3/4x + 8/4

y = -3/4x + 2

The slope of 4y + 3x = 8 is -3/4.

Therefore, the perpendicular line is: c. 4y + 3x = 8.

Learn more about perpendicular lines equation on:

brainly.com/question/7098341

#SPJ1

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Consider the given data. x 0 2 4 6 9 11 12 15 17 19 y 5 6 7 6 9 8 8 10 12 12 Use the least-squares regression to fit a straight
levacccp [35]

Answer:

See below

Step-by-step explanation:

By using the table 1 attached (See Table 1 attached)

We can perform all the calculations to express both, y as a function of x or x as a function of y.

Let's make first the line relating y as a function of x.

<u>y as a function of x </u>

<em>(y=response variable, x=explanatory variable) </em>

\bf y=m_{yx}x+b_{yx}

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\bf b_{yx} is the y-intercept

In this case we use these formulas:

\bf m_{yx}=\frac{(\sum y)(\sum x)^2-(\sum x)(\sum xy)}{n\sum x^2-(\sum x)^2}

\bf b_{yx}=\frac{n\sum xy-(\sum x)(\sum y)}{n(\sum x^2)-(\sum x)^2}

n = 10 is the number of observations taken (pairs x,y)

<u>Note:</u> <em>Be careful not to confuse  </em>

\bf \sum x^2 with \bf (\sum x)^2

Performing our calculations we get:

\bf m_{yx}=\frac{(83)(95)^2-(95)(923)}{10*1277-(95)^2}=176.6061

\bf b_{yx}=\frac{10*923-(95)(83)}{10(1277)-(95)^2}=0.3591

So the equation of the line that relates y as a function of x is

<h3>y = 176.6061x + 0.3591 </h3>

In order to compute the standard error \bf S_{yx}, we must use Table 2 (See Table 2 attached) and use the definition

\bf s_{yx}=\sqrt{\frac{(y-y_{est})^2}{n}}

and we have that standard error when y is a function of x is

\bf s_{yx}=\sqrt{\frac{39515985}{10}}=1987.8628

Now, to find the line that relates x as a function of y, we simply switch the roles of x and y in the formulas.  

So now we have:

x as a function of y

(x=response variable, y=explanatory variable)

\bf x=m_{xy}y+b_{xy}

where

\bf m_{xy} is the slope of the line

\bf b_{xy} is the x-intercept

In this case we use these formulas:

\bf m_{xy}=\frac{(\sum x)(\sum y)^2-(\sum y)(\sum xy)}{n\sum y^2-(\sum y)^2}

\bf b_{xy}=\frac{n\sum xy-(\sum x)(\sum y)}{n(\sum y^2)-(\sum y)^2}

n = 10 is the number of observations taken (pairs x,y)

<u>Note:</u> <em>Be careful not to confuse  </em>

\bf \sum y^2 with \bf (\sum y)^2

Remark:<em> </em><em>If you wanted to draw this line in the classical style (the independent variable on the horizontal axis), you would have to swap the axis X and Y) </em>

Computing our values, we get

\bf m_{xy}=\frac{(95)(83)^2-(83)(923)}{10*743-(83)^2}=1068.1072

\bf b_{xy}=\frac{10*923-(95)(83)}{10(743)-(83)^2}=2.4861

and the line that relates x as a function of y is

<h3>x = 1068.1072y + 2.4861 </h3>

To find the standard error \bf S_{xy} we use Table 3 (See Table 3 attached) and the formula

\bf s_{xy}=\sqrt{\frac{(x-x_{est})^2}{n}}

and we have that standard error when y is a function of x is

\bf s_{xy}=\sqrt{\frac{846507757}{10}}=9200.5856

<em>In both cases the correlation coefficient r is the same and it can be computed with the formula: </em>

\bf r=\frac{\sum xy}{\sqrt{(\sum x^2)(\sum y^2)}}

Remark: <em>This formula for r is only true if we assume the correlation is linear. The formula does not hold for other kind of correlations like parabolic, exponential,..., etc. </em>

Computing the correlation coefficient :

\bf r=\frac{923}{\sqrt{(1277)(743)}}=0.9478

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Step-by-step explanation:

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