Answer:
135°
Step-by-step Explanation:
==>Given:
An inscribed quadrilateral ABCD with,
m<A = (3x +6)°
m<C = (x + 2)°
==>Required:
measure of angle A
==>Solution:
First, let's find the value of x.
Recall that the opposite angles in any inscribed quadrilateral in a circle are supplementary.
Therefore, this means m<A + m<C = 180°
Thus, (3x+6) + (x+2} = 180
3x + 6 + x + 2 = 180
Collect like terms:
3x + x + 6 + 2 = 180
4x + 8 = 180
Subtract 8 from both sides:
4x + 8 - 8 = 180 - 8
4x = 172
Divide both sides by 4:
4x/4 = 172/4
x = 43
We can now find m<A = (3x + 6)°
m<A = 3(43) + 6
= 129 + 6
measure of angle A = 135°
Answer:
64
Step-by-step explanation:
A square has equal side measurements meaning that each side is 8. To find the area, multiply the length by the width which would be 8 * 8 = 64.
Answer:
The answer is -14.
Step-by-step explanation:
Just divide -7 by 0.5
Answer:
9 packages of chocolate bars
Step-by-step explanation:
Let he bought c packages of chocolate bars and t packages of toffee bars,
Since, he bought 1 fewer package of chocolate bars than toffee bars.
⇒ c = t - 1 -----(1)
Also, he handed out out 
of the chocolate bars and 
of the toffee bars,
If he handed out the same number of each kind of candy bar.
( By cross multiplication )
( Division property of equality )
From equation (1),
Hence, he bought 9 packages of chocolate bars.
Complete question is;
Regarding the violation of multicollinearity, which of the following description is wrong?
a. It changes the intercept of the regression line.
b. It changes the sign of the slope.
c. It changes the slope of the regression line.
d. It changes the value of F-tests.
e. It changes the value of T-tests
Answer:
a. It changes the intercept of the regression line
Step-by-step explanation:
Multicollinearity is a term used in multiple regression analysis to show a high correlation between independent variables of a study.
Since it deals with independent variables correlation, it means it must be found before getting the regression equation.
Now, looking at the options, the one that doesn't relate with multicollinearity is option A because the intercept of the regression line is the value of y that is predicted when x is 0. Meanwhile, multicollinearity from definition above does in no way change the intercept of the regression line because it doesn't predict the y-value when x is zero.
