The smaller the magnitude of the correlation, the weaker the correlation. The magnitude of -0.25 is 0.25, the smallest correlation magnitude.
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.
To make 5.6e12 standard you simply move the decimal place right 12 times
5,600,000,000,000
5 Trillion 600 billion
First, you want to put all the numbers in order from least to greatest.
21, 33, 33, 42, 67, 79, 89
Now, you can use this song to help remember how to do these problems.
Cross off the sides till you get to the center. 1 is good, 2 is bad. (If you get two numbers in the middle) add then divide by two.
Cross off 21, then 89, then 33, then 79, then 33, then 67. Now, you're left with 42 in the middle. That is your median. Hope this helps! :)