You are not correct. Here's an explanation as to why: First of all, the triangle is isosceles, since it has two congruent sides that leads to the conclusion of two congruent angles, one opposite each side. This means that all three angle measurements of the triangle are x degrees, x degrees, and 40 degrees. To solve for x, add all three values together and set them equal to 180 degrees, the sum of three angles in any triangle. Your mistake is adding only one x to 40, which isn't inclusive of all three triangles. I hope this helped!
Answer:
The distance of the point from the origin = 9.29 units.
Step-by-step explanation:
Given point:
(7,-6)
The angle lies such that the terminal side of the angle contains the given point.
To draw the angle and find the distance from the origin to the given point.
Solution:
The terminal side of the angle is where the angle ends with the initial side being the positive side of the x-axis.
So, we can plot the point (7,-6) by moving 7 units on the x-axis horizontally and -6 units on the y-axis vertically.
We can find the distance of the point from the origin by find the hypotenuse of the triangle formed.
Applying Pythagorean theorem.
Taking square root both sides :
Thus, the distance of the point from the origin = 9.29 units.
The figure is shown below.
Answer:
All of them because its four sides which is a quadrilateral and all of the above shapes have four sides
Step-by-step explanation:
The answer is 6.875 ish. not exact
Answer:
Explained below.
Step-by-step explanation:
Consider the variables height and weight.
It is usually seen that taller people are heavier than shorter people.
So a regression analysis can be used to specify this belief.
The statistical questions that are being asked here are:
- What the independent and dependent variables?
- Are there any other factor influencing the dependent variable other than the independent variable?
The variable <em>Y</em> is considered as the dependent variable and the variable <em>Y</em> is considered as the independent variable. And the main purpose of the regression analysis is to predict the value of <em>Y</em> when the value of <em>X</em> is given.
The linear regression model can be used to predict the past and future value of the dependent variables provided that the independent variables for those times are provided.
