To fit a simple linear regression model to the data and to provide its equation (d = a*t + b), along with R2 Day Date Weekday Daily Demand Weekend 1 4/25/2016 Mon 297 0 2 4/26/2016 Tue 293 0 3 4/27/2016 Wed 327 0 4 4/28/2016 Thu 315 0 5 4/29/2016 Fri 348 0 6 4/30/2016 Sat 447 1 7 5/1/2016 Sun 431 1 8 5/2/2016 Mon 283 0 9 5/3/2016 Tue 326 0 10 5/4/2016 Wed 317 0 11 5/5/2016 Thu 345 0 12 5/6/2016 Fri 355 0 13 5/7/2016 Sat 428 1 14 5/8/2016 Sun 454 1 15 5/9/2016 Mon 305 0 16 5/10/2016 Tue 310 0 17 5/11/2016 Wed 350 0 18 5/12/2016 Thu 308 0 19 5/13/2016 Fri 366 0 20 5/14/2016 Sat 460 1 21 5/15/2016 Sun 427 1 22 5/16/2016 Mon 291 0 23 5/17/2016 Tue 325 0 24 5/18/2016 Wed 354 0 25 5/19/2016 Thu 322 0 26 5/20/2016 Fri 405 0 27 5/21/2016 Sat 442 1 28 5/22/2016 Sun 454 1 29 5/23/2016 Mon 318 0 30 5/24/2016 Tue 298 0 31 5/25/2016 Wed 355 0 32 5/26/2016 Thu 355 0 33 5/27/2016 Fri 374 0 34 5/28/2016 Sat 447 1 35 5/29/2016 Sun 463 1 36 5/30/2016 Mon 291 0 37 5/31/2016 Tue 319 0 38 6/1/2016 Wed 333 0 39 6/2/2016 Thu 339 0 40 6/3/2016 Fri 416 0 41 6/4/2016 Sat 475 1 42 6/5/2016 Sun 459 1 43 6/6/2016 Mon 319 0 44 6/7/2016 Tue 326 0 45 6/8/2016 Wed 356 0 46 6/9/2016 Thu 340 0 47 6/10/2016 Fri 395 0 48 6/11/2016 Sat 465 1 49 6/12/2016 Sun 453 1 50 6/13/2016 Mon 307 0 51 6/14/2016 Tue 324 0 52 6/15/2016 Wed 350 0 53 6/16/2016 Thu 348 0 54 6/17/2016 Fri 384 0 55 6/18/2016 Sat 474 1 56 6/19/2016 Sun 485 1

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

To fit a simple linear regression model to the data and to provide its equation (d = a*t + b), along with R²

Day	Date	Weekday	Daily Demand	Weekend
1	4/25/2016	Mon	297	0
2	4/26/2016	Tue	293	0
3	4/27/2016	Wed	327	0
4	4/28/2016	Thu	315	0
5	4/29/2016	Fri	348	0
6	4/30/2016	Sat	447	1
7	5/1/2016	Sun	431	1
8	5/2/2016	Mon	283	0
9	5/3/2016	Tue	326	0
10	5/4/2016	Wed	317	0
11	5/5/2016	Thu	345	0
12	5/6/2016	Fri	355	0
13	5/7/2016	Sat	428	1
14	5/8/2016	Sun	454	1
15	5/9/2016	Mon	305	0
16	5/10/2016	Tue	310	0
17	5/11/2016	Wed	350	0
18	5/12/2016	Thu	308	0
19	5/13/2016	Fri	366	0
20	5/14/2016	Sat	460	1
21	5/15/2016	Sun	427	1
22	5/16/2016	Mon	291	0
23	5/17/2016	Tue	325	0
24	5/18/2016	Wed	354	0
25	5/19/2016	Thu	322	0
26	5/20/2016	Fri	405	0
27	5/21/2016	Sat	442	1
28	5/22/2016	Sun	454	1
29	5/23/2016	Mon	318	0
30	5/24/2016	Tue	298	0
31	5/25/2016	Wed	355	0
32	5/26/2016	Thu	355	0
33	5/27/2016	Fri	374	0
34	5/28/2016	Sat	447	1
35	5/29/2016	Sun	463	1
36	5/30/2016	Mon	291	0
37	5/31/2016	Tue	319	0
38	6/1/2016	Wed	333	0
39	6/2/2016	Thu	339	0
40	6/3/2016	Fri	416	0
41	6/4/2016	Sat	475	1
42	6/5/2016	Sun	459	1
43	6/6/2016	Mon	319	0
44	6/7/2016	Tue	326	0
45	6/8/2016	Wed	356	0
46	6/9/2016	Thu	340	0
47	6/10/2016	Fri	395	0
48	6/11/2016	Sat	465	1
49	6/12/2016	Sun	453	1
50	6/13/2016	Mon	307	0
51	6/14/2016	Tue	324	0
52	6/15/2016	Wed	350	0
53	6/16/2016	Thu	348	0
54	6/17/2016	Fri	384	0
55	6/18/2016	Sat	474	1
56	6/19/2016	Sun	485	1

Expert Solution

Step 1

To fit a simple linear regression line we first define the independent and the dependent variables.

Suppose d: dependent variable and t: independent variable

Then the regression equation of d on t is

d=a*t+b

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