Sandwich BK Hamburger BK Whopper BK Chicken Sandwich McD Hamburger McD Quarter Pounder McD Chicken Sandwich 5 Guys Little Burger 5 Guys Burger Wendy's Jr. Hamburger Wendy's Quarter Pound Burger Wendy's Chicken Sandwich Chick-Fil-A Chicken Sandwich Culver's Butterburger Single Culver's Butterburger Double Sums Fat Grams x 12 37 36 9 26 33 26 43 8 31 19 18 17 30 Calories y 290 650 640 250 520 510 480 700 230 580 350 440 390 560 x² xy Use linear regression to find a linear model y=mx+b that relates the number of fat grams x to the number of calories y in a fast-food sandwich. Round coefficients to two decimals.
Sandwich BK Hamburger BK Whopper BK Chicken Sandwich McD Hamburger McD Quarter Pounder McD Chicken Sandwich 5 Guys Little Burger 5 Guys Burger Wendy's Jr. Hamburger Wendy's Quarter Pound Burger Wendy's Chicken Sandwich Chick-Fil-A Chicken Sandwich Culver's Butterburger Single Culver's Butterburger Double Sums Fat Grams x 12 37 36 9 26 33 26 43 8 31 19 18 17 30 Calories y 290 650 640 250 520 510 480 700 230 580 350 440 390 560 x² xy Use linear regression to find a linear model y=mx+b that relates the number of fat grams x to the number of calories y in a fast-food sandwich. Round coefficients to two decimals.
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
Related questions
Question
Use linear regression to find a linear model
y mx b = +
that relates the number of fat grams
x
to the
number of calories
y
in a fast-food sandwich. Round coefficients to two decimals.
Expert Solution
Step 1: Description of given data
We have given data that relates the number of fat grams x to the number of calories y in a fast food sandwich.
Fat Grams, X | Calpries, Y |
12 | 290 |
37 | 650 |
36 | 640 |
9 | 250 |
26 | 520 |
33 | 510 |
26 | 480 |
43 | 700 |
8 | 230 |
31 | 580 |
19 | 350 |
18 | 440 |
17 | 390 |
30 | 560 |
Use the linear regression to find linear model
Where m is slope and b is the intercept of the regression line.
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