Answer:
According to triangle theorems, we know that the interior angles of a triangle equals 180 degrees. Since we know that angle A and angle B are 55 and 30 degrees, all we need to do is add them up and subtract it from 180 degrees to find out the measure of angle C.
55 + 30 = 85 degrees
180 degrees - 85 degrees = 95 degrees (this is the measure for angle C)
False if y=f(x) then x= inverse of f(x) or f^-1(x)
Answer:
Second class have higher marks and greater spread.
Step-by-step explanation:
First box plot represents class first. From the first box plot, we get
Second box plot represents class second. From the second box plot, we get
First class has greater minimum value, first quartile of both classes are same, second class has greater median, first class has greater third quartile and first class has greater maximum value. It means second class have higher marks but class first have less variation.
Second class has greater range and greater inter quartile range. It means data of second class has greater spread.
Therefore, second class have higher marks and greater spread.
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.
