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lyudmila [28]
3 years ago
15

The measure of each exterior angle of a regular polygon is 72 degrees. how many sides does the polygon have?

Mathematics
1 answer:
zaharov [31]3 years ago
6 0
If the measure of each exterior angle is 72, the measure of each interior angle is then 180-72=108
the sum of the interior angles of a polygon is (n-2)*180, n is the number of sides
(n-2)*180=108*n
180n-360=108n
180n-108n=360
n=5
the polygon has 5 sides. 
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How much is the number 727731000 in percent form
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72773100000%

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727731000 = 72773100000%

Simplified Conversion:

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In a study relating the degree of warping, in mm, of a copper plate (y) to temperature in °C (x), the following summary statisti
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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

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3 years ago
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