Answer:
it's a yes
Step-by-step explanation:
X/6 ≤ 30
to find the value of X we have to move the numerical to the other side
in this case the number is 1/6
X * 1/6 ≤ 30
so to balance the both side and get rid of the number on the left side we multiple with 6.
1/6*6 = 1 on the left side
30*6 = 180 on the right side
so the full equation will be
X * 1/6 * 6 ≤ 30 * 6
X * 1 ≤ 180
X ≤ 180
Answer:
76, 76 104
Step-by-step explanation:
Angle 3 is vertical so it is congruent to Angle 2.
Angle 6 is corresponding, so it is congruent to Angle 2.
Angle 8 is linear to Angle 6, so they add to 180
Answer:
40 more kids chose baseball over basketball.
Step-by-step explanation:
The best way to go about this is to use benchmark percentages.
For baseball, 25% of 800 people chose that, so we can do 800/4 to get 200, so 200 students chose baseball
For basketball, we could do 800/5 but that isn't mental math, so we can do 10% of 800 is 80 and 80*2=160. So 40 more kids chose baseball over basketball.
Answer:
m= -4
Step-by-step explanation:
the question is asking you to make the variable 'm' by its self and find what it is equal to. you divide 8 by both sides. this gets 'm' alone and -32 divided by 8 is -4.
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.
