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2 years ago

15

Let be a circle

with centre $O$ . A second circle $L$ passes through $O$ and cuts $K$ at two other points $A$ and $B$ . Let $P$ be a point on arc $AOB$ and extend line segment $BP$ to intersect $K$ at a second point $Q$ . Prove that the bisector of angle $APQ$ passes through $O$ .

1 answer:

yan [13]2 years ago

8 0

The bisector of angle APQ passes through O and this is illustrated below.

<h3>How to illustrate the information?</h3>

From the information given, the center is O. and the circle passes through O and cuts at K.

In this case, it should be noted that the circles are equal according to the SAS test.

Here, AOB + APQ = 180° (Linear pair)

2AOB = 180

AOB = 90.

Therefore, the bisector of angle APQ passes through O.

Learn more about bisector on:

brainly.com/question/11006922

#SPJ1

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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:

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

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

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n= 40 represent the sample size

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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:

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And if we replace the values given we have:

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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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