Answer:
Discarding the influential outlying cases when detected is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset
Step-by-step explanation:
In a regression class ; If extremely influential outlying cases are detected in a Data set, discarding this influential outlying cases is the right way to go about it
Discarding the influential outlying cases when detected is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset
Y=3x+4.5 is NOT the equation the correct answer is what the person under me typed
Answer:
When conducting an analysis of variance analysis on a set of samples and the null hypothesis is rejected, we can test for the difference between treatment can be tested with the aid of a t-test. This is employed when 2 related groups are involved. Independent sample, paired sample or one-sample t-test can be conducted.
Step-by-step explanation:
A t-test is a test statistic used to make inferences that determine the differences that exist statistically between 2 related groups. It tells us if the 2 groups tested are from the same population. There are 3 types of t-test namely independent sample t-test, paired-sample t-test and one-sample t-test
Answer:
C.
Step-by-step explanation:
The coefficients of the expansion of (a + b)^2 are 1 2 1, which is the third row of Pascal's triangle.
For (a + b)^3 they are 1 3 3 1 which is in the 4th row, and so on
So those for the (a + b)^n are in the (n + 1)th row.
