Answer:
a) 0.0082
b) 0.9987
c) 0.9192
d) 0.5000
e) 1
Step-by-step explanation:
The question is concerned with the mean of a sample.
From the central limit theorem we have the formula:

a) 
The area to the left of z=2.40 is 0.9918
The area to the right of z=2.40 is 1-0.9918=0.0082

b) 
The area to the left of z=3.00 is 0.9987

c) The z-value of 1200 is 0
The area to the left of 0 is 0.5

The area to the left of z=1.40 is 0.9192
The probability that the sample mean is between 1200 and 1214 is

d) From c) the probability that the sample mean will be greater than 1200 is 1-0.5000=0.5000
e) 
The area to the left of z=-112.65 is 0.
The area to the right of z=-112.65 is 1-0=1
Answer:
The correct answer is:
A. 27,465 +52,534 = 79,899
Step-by-step explanation:
Given are the options for addition
We have to check each addition one by one
So,
A. 27,465 +52,534 = 79,899
This option is the example of error of addition by one. The answer should have been 79,999. Hence there is an error in addition ..
Hence, option a: 27,465 +52,534 = 79,899 is the correct answer ..
Answer:
11
Step-by-step explanation:
Also u can search up mean, median, mode calculator and type in any numbers u want and it will give u the mean, median, mode, range and more
Answer:
j,jzkzkKkskaoaosiososososiosuxuxuux
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.