What is the slope intercept equation of the line that includes (0,7) and (3,10) ?

Mathematics

2 answers:

nekit [7.7K]3 years ago

7 0

Answer:

y = 1x + 7

simplifies to:

y = x + 7

Step-by-step explanation:

first we need to find the slope or m:

y2 - y1 divided by x2 - x1

10-7 divided by 3-0 =

3/3 = 1

slope is 1

then we need to find the y intercept or b:

Usually you would choose one of the points and find the y intercept but the first point is the y intercept (0,7). 7 is the y intercept

plug it into the equation:

y = mx + b

y = 1x +7

y = x + 7

kompoz [17]3 years ago

6 0

7 is the y intercept, it is given as one of the points to is our b.
The slope can be found by 7-10 over 0-3 which is -3/-3 which is 1. 1 is our m.
so with the slope and intercept we plug it into the formula to get:
y = x + 7

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7 0

3 years ago

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In Applied Life Data Analysis (Wiley, 1982), Wayne Nelson presents the breakdown time of an insulating fluid between electrodes

ale4655 [162]

Answer:

The sample mean is $\bar{x}=14.371$ min.

The sample standard deviation is $\sigma = 18.889$ min.

Step-by-step explanation:

We have the following data set:

$\begin{array}{cccccccc}0.15&0.82&0.81&1.44&2.70&3.28&4.00&4.70\\4.96&6.49&7.25&8.03&8.40&12.15&31.89&32.47\\33.79&36.80&72.92&&&&&\end{array}$

The mean of a data set is commonly known as the average. You find the mean by taking the sum of all the data values and dividing that sum by the total number of data values.

The formula for the mean of a sample is

$\bar{x} = \frac{{\sum}x}{n}$

where, $n$ is the number of values in the data set.

$\bar{x}=\frac{0.15+0.82+0.81+1.44+2.7+3.28+4+4.7+4.96+6.49+7.25+8.03+8.4+12.15+31.89+32.47+33.79+36.80+72.92}{19}\\\\\bar{x}=14.371$

The standard deviation measures how close the set of data is to the mean value of the data set. If data set have high standard deviation than the values are spread out very much. If data set have small standard deviation the data points are very close to the mean.

To find standard deviation we use the following formula

$\sigma = \sqrt{ \frac{ \sum{\left(x_i - \overline{x}\right)^2 }}{n-1} }$