MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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A study was conducted to determine whether automobile repair charges are higher for female customers than for male customers. Twenty auto repair shops were randomly selected from the telephone book. Two cars of the same age, brand, and engine problem were used in the study. For each repair shop, the two cars were randomly assigned to a man and woman participant and then taken to the shop for an estimate of repair cost. The repair costs (in dollars) are given in data 4. Which procedure, t or Wilcoxon, is more appropriate in this situation? Why? Are repair costs generally higher for female customers than for male customers?

data4

'RepairShop'  'Female'  'Male'
1                      871         792
2                      684         765
3                      795         511
4                      838         520
5                     1033        618
6                       917        447
7                     1047        548
8                       723        720
9                     1179        899
10                     707        788
11                     817        927
12                     846        657
13                     975        851
14                     868        702
15                    1323       918
16                      791       528
17                    1157       884
18                      932       702
19                    1089       839
20                      770       878

 

The dataset provided contains three columns: 'RepairShop,' 'Female,' and 'Male.' Each column lists corresponding numerical values across 20 entries. The data appears to represent some form of measure or performance metric categorized by gender for each repair shop.

Here is the transcription of the data:

- **Row 1:** 
  - RepairShop: 1
  - Female: 871
  - Male: 792

- **Row 2:** 
  - RepairShop: 2
  - Female: 684
  - Male: 765

- **Row 3:** 
  - RepairShop: 3
  - Female: 795
  - Male: 511

- **Row 4:** 
  - RepairShop: 4
  - Female: 838
  - Male: 520

- **Row 5:** 
  - RepairShop: 5
  - Female: 1033
  - Male: 618

- **Row 6:** 
  - RepairShop: 6
  - Female: 917
  - Male: 447

- **Row 7:** 
  - RepairShop: 7
  - Female: 1047
  - Male: 548

- **Row 8:** 
  - RepairShop: 8
  - Female: 723
  - Male: 720

- **Row 9:** 
  - RepairShop: 9
  - Female: 1179
  - Male: 899

- **Row 10:** 
  - RepairShop: 10
  - Female: 707
  - Male: 788

- **Row 11:** 
  - RepairShop: 11
  - Female: 817
  - Male: 927

- **Row 12:** 
  - RepairShop: 12
  - Female: 846
  - Male: 657

- **Row 13:** 
  - RepairShop: 13
  - Female: 975
  - Male: 851

- **Row 14:** 
  - RepairShop: 14
  - Female: 868
  - Male: 702

- **Row 15:** 
  - RepairShop: 15
  - Female: 1323
  - Male: 918

- **Row
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Transcribed Image Text:The dataset provided contains three columns: 'RepairShop,' 'Female,' and 'Male.' Each column lists corresponding numerical values across 20 entries. The data appears to represent some form of measure or performance metric categorized by gender for each repair shop. Here is the transcription of the data: - **Row 1:** - RepairShop: 1 - Female: 871 - Male: 792 - **Row 2:** - RepairShop: 2 - Female: 684 - Male: 765 - **Row 3:** - RepairShop: 3 - Female: 795 - Male: 511 - **Row 4:** - RepairShop: 4 - Female: 838 - Male: 520 - **Row 5:** - RepairShop: 5 - Female: 1033 - Male: 618 - **Row 6:** - RepairShop: 6 - Female: 917 - Male: 447 - **Row 7:** - RepairShop: 7 - Female: 1047 - Male: 548 - **Row 8:** - RepairShop: 8 - Female: 723 - Male: 720 - **Row 9:** - RepairShop: 9 - Female: 1179 - Male: 899 - **Row 10:** - RepairShop: 10 - Female: 707 - Male: 788 - **Row 11:** - RepairShop: 11 - Female: 817 - Male: 927 - **Row 12:** - RepairShop: 12 - Female: 846 - Male: 657 - **Row 13:** - RepairShop: 13 - Female: 975 - Male: 851 - **Row 14:** - RepairShop: 14 - Female: 868 - Male: 702 - **Row 15:** - RepairShop: 15 - Female: 1323 - Male: 918 - **Row
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