She catched as bill 1 time
Sadly is right. You must have practiced this stuff in middle school
until it was coming out of your ears. Fortunately, you have a chance
to relearn it now. You should do that ... it'll be important in any math
course you ever take.
'm' is the slope of the line on the graph.
The slope is
(the change in 'y' between any two points on the line)
divided by
(the change in 'x' between the same two points).
You can choose any two points on the line, and the slope is always the same.
To make it easy, look at the two points on this graph where the line crosses
the x-axis and the y-axis.
Going between these two points ...
-- the line goes up, from y=0 to y=4. The change in 'y' is 4 .
-- the line goes to the right, from x=-2 to x=0. The change in 'x' is 2 .
'm' = the slope = (4)/(2) = 2 .
The "y-intercept" is the place where the line crosses the y-axis.
On this graph, that's the point where y=4 .
The equation of EVERY straight line on ANY graph is:
Y = (the slope) times 'x' + (the y-intercept) .
So the equation of THIS line on THIS graph is
Y = 2x + 4 .
Frequency tables are used to represent datasets and their frequencies
The average number of miles he drove per month is 1358.3 miles
<h3>How to determine the average number of miles</h3>
From the frequency table, the total number of miles travelled in a year is:
Total = 16300 miles
There are 12 months in a year.
So, the average number of miles is:
![Average = \frac{16300}{12}](https://tex.z-dn.net/?f=Average%20%3D%20%5Cfrac%7B16300%7D%7B12%7D)
Divide
![Average = 1358.3](https://tex.z-dn.net/?f=Average%20%3D%201358.3)
Hence, the average number of miles he drove per month is 1358.3 miles
Read more about average at:
brainly.com/question/20118982
1. the pristine data set,makes it different from the residual plot in points from an arbitrary scatter.
2.a linear patter shows some data of numerical bivariate of a scatter plot, the Residual plot would look homogeneous to, If the points in a residual plot are arbitrarily dispersed around the horizontal axis, a linear regression model is felicitous for the data; otherwise, a nonlinear model is more congruous.
The slope is going from left to right instead of right to lift.(opposite direction)