Answer: y=2x-1
We know that the equation of a line is y = mx + b, where m is the gradient and b is the y-intercept.
First to find the gradient, we use the formula y2-y1/x2-x1.
x1 and y1 being the first set of coordinates (2,3) and x2 and y2 being the second set (4,7)
Now we sub in:
m=7-3/4-2
m=4/2
m=2
This is our gradient and we can put it into the equation: y=2x+b
Now we find the y-intercept (b). Since the y-intercept isn’t shown, we can sub in the coordinates to find it.
y=2x+b
Sub (2,3)
3=2(2)+b
3=4+b
-1=b
Sub (4,7)
7=2(4)+b
7=8+b
-1=b
Subbing in both coordinates gives us -1 as the y-intercept, so the finished equation is: y=2x-1
Answer:
1) y = 2(x+3)^2 -4
2) x+6/x-4
3) 2x^2 + 6x +20 = 0
Step-by-step explanation:
Answer:
Step-by-step explanation:
If A, B, and C are in the ratio of 2:3:5 and they are each getting some amount of money in that ratio, then to find the total amount of money they split is found in Ax + Bx + Cx and we need to solve for x. What we are told is that the smallest share = 210 and obviously A is the smallest share. Therefore,
2x = 210 and x = 105.
The total sum = 210 + 3(105) + 5(105) so
Total sum = 210 + 315 + 525
Total sum = 1050 and
C's share is 525 and
B's share is 315
The math I used is probably not the quickest way, but for me, the easiest.
25*5=125 25+20=45 45*7=315 125+315=440 25*4=100 440+100=540 5+7+4=16
B is your answer.
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.
