1. Scatterplots and a line of best fit

2. Such a line is called “line of best fit”.
When data is displayed with a scatterplot, it is often useful to attempt tp represent the data with a straight line so we could predict values not displayed on a scatterplot.
Such a line is called “line of best fit”.
A line of best fit is a straight line that best represents the data on a scatterplot.
This line may pass through some of the points, none of the points or all of the points.
Its best to try to get your line to pass through at least two of the points, but in the middle of all of them.

3. Is there a relationship between the fat grams and the total calories in fast food?
             
Sandwich
Total Fat (g)
Total Calories
Hamburger 9
260
Cheeseburger 13
320
Quarter Pounder 21
420
Quarter Pounder with Cheese 30
530
Big Mac 31
560
Arch Sandwich Special
550
Arch Special with Bacon 34
590
Crispy Chicken 25
500
Fish Fillet 28
Grilled Chicken 20
440
Grilled Chicken Light 5
300

4. First, create a scatterplot of the data on a piece of graph paper.
Can we predict the number of total calories based upon the total fat grams? Lets see!
First, create a scatterplot of the data on a piece of graph paper.

5. 1.  Prepare a scatter plot of the data.
2.  Using a strand of spaghetti, position the spaghetti so that the plotted points are as close to the strand as possible.
3.  Find two points that you think will be on the "best-fit" line. 
4. We’re going to choose the two point (9,260) & (30,530)

6. 5.  Calculate the slope of the line through your two points.                                                            
rounded to three decimal places.
We calculate slope using the slope formula
y2 – y1
x2 – x1
(9 , 260) & (30 , 530) x1 y1 x2 y2
Remember a linear equation is written y = mx + b or y = a + bx
slope
y - intercept

7. 6.  Write the equation of the line.                                                 
Remember we write equations as y =mx + b or y = a + bx
Use the point-slope formula
y – y1 = slope (x – x1)
Our 2 points
( 9 , 260) & ( 30 , 530) x1 y1 x2 y2

8. Now we have the equation:
y = 12.87(x – 9) + 260
Multiply by both x and –9 to get this equation
y = 12.87x –
y = 12.87x
Now add 260 with – to get this equation
This is the equation of the line we just made to fit the data!
7.  This equation can now be used to predict information that was not plotted in the scatter plot.

9. Using your best fit line, predict the total calories based upon 22 grams of fat.
y = 12.87x
y = (22)
Substitute 22 for x
y =
This means that if you have a sandwich that has 22 grams of fat, it will have calories.
Predicting:  - If you are looking for values that fall within the plotted values, you are interpolating.
 - If you are looking for values that fall outside the plotted values, you are extrapolating.  Be careful when extrapolating.  The further away from the plotted values you go, the less reliable is your prediction.

10. So who has the Correct line of best fit
In step 4 above, we chose two points to form our line-of-best-fit.  It is possible, however, that someone else will choose a different set of points, and their equation will be slightly different.  Your answer will be considered CORRECT, as long as your calculations are correct for the two points that you chose.