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AlekseyPX
2 years ago
12

Find the measure of angle m

Mathematics
1 answer:
Anettt [7]2 years ago
6 0

Answer:

12 answer

Step-by-step explanation:

(8x-34)=(5x+2) (correspounding angle)

8x-5x=34+2

3x=36

x=36/3

x=13

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What are the side lengths of a 45-45-90 triangle with a hypotenuse of 10root2?
scoundrel [369]

Answer:

10

Step-by-step explanation:

let the lengths of sides=x (each)

x²+x²=(10√2)²

2x²=100×2

x²=100

x=√100=10

6 0
3 years ago
What is the domain and range of the relation [(1, -8), (-7,8), (-3, 7). (-3, -5]?​
Nadusha1986 [10]

Step-by-step explanation:

domain = {-7, -3, 1}

range ={-8, -5, 7, 8}

6 0
3 years ago
In a study relating the degree of warping, in mm, of a copper plate (y) to temperature in °C (x), the following summary statisti
kipiarov [429]

Answer:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

Step-by-step explanation:

Linear regression is a way "to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables)".

Regression estimates are "used to describe data and to explain the relationship between one dependent variable and one or more independent variables"

The linear model is given by the following equation Y=mx +b, where y is the dependent variable, x the independent variable, m the slope and b the intercept.

For this case we have the following info given:

\sum_{i=1}^n (x_i -\bar x)^2 =98775

\sum_{i=1}^n (y_i -\bar y)^2 =19.4

\bar x =26.36

\bar y =0.5188

n= 40  represent the sample size

\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y) =796.94

The total sum of squares is given by this formula:

SST= \sum_{i=1}^n (y_i -\bar y)^2 =19.4

And from the formulas for a simple regression, we need to calculate the slope for the regression like this:

m=\frac{\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)}{\sum_{i=1}^n (x_i -\bar x)^2}

And if we replace the values given we have:

m=\frac{796.94}{98775}=0.008068

We have another useful equation in order to find the sum of squares for the regression, given by:

SSR=m* \sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)=0.008068*796.94=6.430

And we know this equivalence:

SST= SSR+SSE

And solving for the sum of squares for the error we have:

SSE=SST-SSR=19.4-6.430=12.97

So then we have the final solutions:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

6 0
3 years ago
The pre–image, A, was dilated about the origin. It was then transformed in another way to get A'.
Elenna [48]

Answer:

reflection over the x-axis

Step-by-step explanation:

i hope this helps

6 0
3 years ago
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How old am I if 500 reduced by 2 Times my age is 250​
Gekata [30.6K]

Answer:Yes it is 250


Step-by-step explanation:


7 0
2 years ago
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