1. Determine the LSRL equation. 2. Describe the slope and its meaning in the context of the data. 3. Use the LSRL to predict how much of a tip Brianna should expect for a $150.00. Does that tip match up with her other tips? Why or why not? 4. Determine the residual for the $102.21 order. What does this residual tell you about the tip this customer left for Brianna? 5. Is a linear model the most appropriate for this data? Why or why not?

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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I need answer for 2,3,4. Can someone please help me out
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.
Data Table
Order Amount S
Tip Percentage %
1
29.75
14
2
172.43
12
3
102.21
13
4
78.6
15
48.89
17
256.54
10
7
24.14
17
8
118.63
15
214.72
11
10
187.2
10
18.0-
16.5
Residual Table
15.0
Order Amount S
Resid
13.5
1
29.75
-2.1028
12.0
2
172.43
0.019
102.21
-1.0096
10.5
78.6
0.3084
100
250
150
Order Amount $
50
200
300
48.89
1.4501
256.54
0.4488
7
24.14
0.7351
Regression Output for Tip Percentage % vs. Order Amount $
118.63
1.4648
Сoef
SE Coef
T
P
214.72
0.2407
Constant
16.9623
0.7615
22.2733
0.0000
10
187.2
-1.5543
Order Amount $
-0.0289
0.0052
-5.5135
0.0006
S = 1.275
R-Sq = 0.7917
Adj. R-Sq = 0.7656
r= -0.8898
Residual Plot
2.
1
lual
Tip Percentage %
Transcribed Image Text: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. Data Table Order Amount S Tip Percentage % 1 29.75 14 2 172.43 12 3 102.21 13 4 78.6 15 48.89 17 256.54 10 7 24.14 17 8 118.63 15 214.72 11 10 187.2 10 18.0- 16.5 Residual Table 15.0 Order Amount S Resid 13.5 1 29.75 -2.1028 12.0 2 172.43 0.019 102.21 -1.0096 10.5 78.6 0.3084 100 250 150 Order Amount $ 50 200 300 48.89 1.4501 256.54 0.4488 7 24.14 0.7351 Regression Output for Tip Percentage % vs. Order Amount $ 118.63 1.4648 Сoef SE Coef T P 214.72 0.2407 Constant 16.9623 0.7615 22.2733 0.0000 10 187.2 -1.5543 Order Amount $ -0.0289 0.0052 -5.5135 0.0006 S = 1.275 R-Sq = 0.7917 Adj. R-Sq = 0.7656 r= -0.8898 Residual Plot 2. 1 lual Tip Percentage %
9
214.72
0.2407
Constant
16.9623
0.7615
22.2733
0.0000
10
187.2
-1.5543
Order Amount $
-0.0289
0.0052
-5.5135
0.0006
S= 1.275
R-Sq = 0.7917
Adj. R-Sq = 0.7656
r= -0.8898
Residual Plot
1
-2
50
100
150
200
250
300
Order Amount $
1. Determine the LSRL equation.
2. Describe the slope and its meaning in the context of the data.
3. Use the LSRL to predict how much of a tip Brianna should expect for a $150.00. Does that tip
match up with her other tips? Why or why not?
4. Determine the residual for the $102.21 order. What does this residual tell you about the tip
this customer left for Brianna?
5. Is a linear model the most appropriate for this data? Why or why not?
1. y=-0.0289x+16.9623
2.
3.
2. y=-0.0289(150.00)+16.9623=12.6273
1
3
4
7
8
9
10
Next
Residual
Transcribed Image Text:9 214.72 0.2407 Constant 16.9623 0.7615 22.2733 0.0000 10 187.2 -1.5543 Order Amount $ -0.0289 0.0052 -5.5135 0.0006 S= 1.275 R-Sq = 0.7917 Adj. R-Sq = 0.7656 r= -0.8898 Residual Plot 1 -2 50 100 150 200 250 300 Order Amount $ 1. Determine the LSRL equation. 2. Describe the slope and its meaning in the context of the data. 3. Use the LSRL to predict how much of a tip Brianna should expect for a $150.00. Does that tip match up with her other tips? Why or why not? 4. Determine the residual for the $102.21 order. What does this residual tell you about the tip this customer left for Brianna? 5. Is a linear model the most appropriate for this data? Why or why not? 1. y=-0.0289x+16.9623 2. 3. 2. y=-0.0289(150.00)+16.9623=12.6273 1 3 4 7 8 9 10 Next Residual
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