Answer: D) The linear model shows a strong fit to the data
The actual strength of the relationship is unknown unless we have the actual values of each data point (so we can compute the correlation coefficient r), but the residuals are randomly scattered about both above and below the horizontal axis. This means we have a fairly good linear fit. If all of the points were above the line, or all below the line, or all residuals fit a certain pattern (eg: parabola), then it wouldn't be a good linear fit.
1.085*10^3 scientific notation is a number between 1-10 multiplied by 10 to a certain power.
Answer:
A.
Step-by-step explanation:
Answer:
Try to search up euphemisms to help you in an argument where your forced to say no
Step-by-step explanation: In this situation apologize to them and say
I can't do that or no
Answer:
Step-by-step explanation:
m = 
y - 
= m( x - 
)
y = mx + b
~~~~~~~~~~~
(4, 5)
( - 6, 15)
m = (15 - 5) / ( - 6 - 4 ) = - 1
y - 5 = ( - 1)( x - 4 )
y =<em> (- 1) </em>x + <em>9</em>
