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3 years ago

Select the table that represents a linear functionplz help

Mathematics

1 answer:

S_A_V [24]3 years ago

4 0

The math point of the varibale can only be counted so it is c im pretty sure

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A line passes through the points (p, a) and (p, –a) where p and a are real numbers and p ≠ 0. Describe each of the following. Ex

Helen [10]

<span> line passes through the points (p, a) and (p, –a)
where p and a are real numbers and p ≠ 0
(p, a) and (p, –a) m = Cannot divide by ZERO, there is no slope to this Line.
slope of the line: no slope (does not exist)
equation of the line: x = p Perpendicular Line
</span><span>slope of a line perpendicular to the given line: would be m = 0, horizontal Line y= 3
</span>y- intercept: none

8 0

3 years ago

Find the equation of the line passing through the point (1, 7) with a slope of -2.

Mariulka [41]

Answer:

$y=-2x+9$

Step-by-step explanation:

we know that

The equation of a line in point slope form is equal to

$y-y1=m(x-x1)$

In this problem we have

$(x1,y1)=(1,7)\\m=-2$

substitute

$y-7=-2(x-1)$

Convert to slope intercept form

$y=mx+b$

$y-7=-2x+2$

$y=-2x+2+7$

$y=-2x+9$

4 0

3 years ago

What are the factors of 114?

sattari [20]

1,2,3,6,19,38,57,114

3 0

3 years ago

The data set below shows the temperature, in degrees Fahrenheit, of a mug of tea compared tothe time in minutes after being pour

Verizon [17]

Given the model of the Exponential Regression:

$y=174.4(0.987)^x$

By definition:

$Residual=Observed\text{ }y\text{ }value-Predicted\text{ }y\text{ }value$

You can see in the table the observed y-values (the temperature in Fahrenheit)

In order to find the Predicted y-values, you need to substitute all the x-values given in the table (the time in minutes) into the equation and then evaluate. You get:

$y=174.4(0.987)^5\approx163.35$ $y=174.4(0.987)^8\approx157.07$ $y=174.4(0.987)^{11}\approx151.02$ $y=174.4(0.987)^{15}\approx143.32$ $y=174.4(0.987)^{18}\approx137.80$ $y=174.4(0.987)^{22}\approx130.77$ $y=174.4(0.987)^{25}\approx125.74$ $y=174.4(0.987)^{29}\approx119.33$ $y=174.4(0.987)^{32}\approx114.73$ $y=174.4(0.987)^{35}\approx110.32$

Now you have these points:

$(5,163.35),(8,157.07),(11,151.02),(15.143.32)(18,137.800),(22,130.77),(25,125.74),(29,119.33),(32,114.73),(35,110.32)$

Therefore, you can plot them on the Coordinate Plane:

By definition, when the residual plot shows a pattern, a non-linear regression model is appropriate for the data. Therefore, the Exponential Regression Model is a good fit.

Hence, the answer is:

- Residual Plot:

- First option.