I need help. ASAP Clever | Portal0.0052 -5.5135 0.0006Adj. R-Sq= 0.7656 r=-0.8898●-250100 150 200 250 300Order Amount $1. Determine the LSRL equation using the format y-mx+b.2. Describe the slope (m) and its meaning in the context of the data [particularly as it relates to the y-variable].3. Use the LSRL you wrote to predict how much of a tip Brianna should expect for a $150.00 order. Does that tip match up with her othertips? Why or why not?4. Determine the residual for the $102.21 order using the Residual Table. What does this residual tell you about the tip this customer leftfor Brianna?5. Using the Residual Plot, is a linear model the most appropriate for this data? Why or why not?Number EACH of your answers to the above 5 questions so you don't forget to answer any of them!!!Residual Plot2Residual1O101.2●●Order Amount SS = 1.275-0.0289R-Sq=0.7917 Brianna is a shopper for Instacart. She was wondering if there was a correlation between the amount of an order and the percentage of a tipthat the customer would give her. She collected data from her 10 most recent shopping trips and produced the following informationregarding the relationship between the order amount and the tip percentage each customer gave her.18.0Data Table16.5-Residual TableOrder Amount $Tip Percentage %15.0Order Amount $Resid-2.1028129.751413.5129.752172.43122172.430.01912.03102.21133102.21-1.009610.5478.61540.308478.648.89548.8917250 300 51.450150 100150 200Order Amount $6256.54106256.540.4488724.1417724.140.7351Regression Output for Tip Percentage % vs. Order Amount S8118.63158118.631.4648TP9214.72119214.720.2407ConstantCoef16.9623-0.0289SE Coef0.7615 22.2733 0.00000.0052 -5.5135 0.000610187.21010187.2-1.5543Order Amount $S = 1.275 R-Sq=0.7917Adj. R-Sq = 0.7656r = -0.8898Residual Plot2Residual●Tip Percentage %●6

Dear student according to the guidelines we solve only first three subparts . Solution The given…

1. Determine the LSRL equation using the format y-mx+b. 2. Describe the slope (m) and its meaning in the context of the data [particularly as it relates to the y-variable]. 3. Use the LSRL you wrote to predict how much of a tip Brianna should expect for a $150.00 order. Does that tip match up with her other tips? Why or why not? 4. Determine the residual for the $102.21 order using the Residual Table. What does this residual tell you about the tip this customer left for Brianna?

1. Determine the LSRL equation using the format y-mx+b. 2. Describe the slope (m) and its meaning in the context of the data [particularly as it relates to the y-variable]. 3. Use the LSRL you wrote to predict how much of a tip Brianna should expect for a $150.00 order. Does that tip match up with her other tips? Why or why not? 4. Determine the residual for the $102.21 order using the Residual Table. What does this residual tell you about the tip this customer left for Brianna?

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

I need help. ASAP

Clever | Portal
0.0052 -5.5135 0.0006
Adj. R-Sq= 0.7656 r=-0.8898
●
-2
50
100 150 200 250 300
Order Amount $
1. Determine the LSRL equation using the format y-mx+b.
2. Describe the slope (m) and its meaning in the context of the data [particularly as it relates to the y-variable].
3. Use the LSRL you wrote to predict how much of a tip Brianna should expect for a $150.00 order. Does that tip match up with her other
tips? Why or why not?
4. Determine the residual for the $102.21 order using the Residual Table. What does this residual tell you about the tip this customer left
for Brianna?
5. Using the Residual Plot, is a linear model the most appropriate for this data? Why or why not?
Number EACH of your answers to the above 5 questions so you don't forget to answer any of them!!!
Residual Plot
2
Residual
1
O
101.2
●
●
Order Amount S
S = 1.275
-0.0289
R-Sq=0.7917

Brianna is a shopper for Instacart. She was wondering if there was a correlation between the amount of an order and the percentage of a tip
that the customer would give her. She collected data from her 10 most recent shopping trips and produced the following information
regarding the relationship between the order amount and the tip percentage each customer gave her.
18.0
Data Table
16.5-
Residual Table
Order Amount $
Tip Percentage %
15.0
Order Amount $
Resid
-2.1028
1
29.75
14
13.5
1
29.75
2
172.43
12
2
172.43
0.019
12.0
3
102.21
13
3
102.21
-1.0096
10.5
4
78.6
15
4
0.3084
78.6
48.89
5
48.89
17
250 300 5
1.4501
50 100
150 200
Order Amount $
6
256.54
10
6
256.54
0.4488
7
24.14
17
7
24.14
0.7351
Regression Output for Tip Percentage % vs. Order Amount S
8
118.63
15
8
118.63
1.4648
T
P
9
214.72
11
9
214.72
0.2407
Constant
Coef
16.9623
-0.0289
SE Coef
0.7615 22.2733 0.0000
0.0052 -5.5135 0.0006
10
187.2
10
10
187.2
-1.5543
Order Amount $
S = 1.275 R-Sq=0.7917
Adj. R-Sq = 0.7656
r = -0.8898
Residual Plot
2
Residual
●
Tip Percentage %
●
6

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