1. It is the subset of a group - Group sample.
2. It equally favors all members of a group sample
- Random sample.
3. It collects data on members of a group - Survey.
4. It does not equally favor all members of a group - Biased sample.
5. It includes all members of a group
- Population.
6. It analyzes data collected from a group - Mean.
I have matched all concepts in accordance with statistical use, hope it helps.
Answer: 5 Pens!
Step-by-step explanation: 5 Pens Would Be The Answer.
Answer:
If the bisectors of two adjacent angles are perpendicular to each other, are the angles then supplementary angles?
Suppose two angles ABC and CBD are x and y.
x+y = 180 deg.
The bisector of angle ABC (BE) and the bisector of angle CBD (CF) will form angle EBF = (x/2)+(y/2) = 180/2 = 90 deg.
Conclusion: If the angle bisectors of two adjacent angles are perpendicular to eaxh other, the adjacent angles are supplementary angle
Adjacent angles are when the 2 angles have a common vertex and a common arm.
if the exterior sides of 2 adjacent angles are perpendicular, then the angles are complementary angles.
Then the sum of the 2 adjacent angles is a right angle - 90°.
When 2 angles add up to 90°, they are called a pair of complementary angles.
Step-by-step explanation:
I do not understand your question please explain further for me to answer
Answer:
Residual = -2
The negative residual value indicates that the data point lies below the regression line.
Step-by-step explanation:
We are given a linear regression model that relates daily high temperature, in degrees Fahrenheit and number of lemonade cups sold.

Where y is the number of cups sold and x is the daily temperature in Fahrenheit.
Residual value:
A residual value basically shows the position of a data point with respect to the regression line.
A residual value of 0 is desired which means that the regression line best fits the data.
The Residual value is calculated by
Residual = Observed value - Predicted value
The predicted value of number of lemonade cups is obtained as

So the predicted value of number of lemonade cups is 23 and the observed value is 21 so the residual value is
Residual = Observed value - Predicted value
Residual = 21 - 23
Residual = -2
The negative residual value indicates that the data point lies below the regression line.