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Control Charts for p. In Exercises 5鈥12, use the given process data to construct a control chart for p. In each case, use the three out-of-control criteria listed near the beginning of this section and determine whether the process is within statistical control. If it is not, identify which of the three out-of-control criteria apply

Car Batteries Defective car batteries are a nuisance because they can strand and inconvenience drivers, and drivers could be put in danger. A car battery is considered to be defective if it fails before its warranty expires. Defects are identified when the batteries are returned under the warranty program. The Powerco Battery corporation manufactures car batteries in batches of 250, and the numbers of defects are listed below for each of 12 consecutive batches. Does the manufacturing process require correction?

3 4 2 5 3 6 8 9 12 14 17 20

Short Answer

Expert verified

The following p chart is constructed for the proportions of defective batteries:

It can be said that the process is not within statistical control because of two visible out-of-control criteria that are:

  • At least one point beyond the UCL
  • An upward trend in the sample proportions over time.

Here, the proportion of defects in batteries are increasing. Thus, the manufacturer needs to take a corrective procedure to ensure that the number of defects decline and good quality batteries are produced.

Step by step solution

01

Given information

Data are given on the number of defective batteries in 12 samples.

The sample size of each of the 12 samples is equal to 250.

02

Important values of p chart

Let\(\bar p\)be the estimated proportion of defectivebatteriesin all the samples.

It is computed as follows:

\(\begin{array}{c}\bar p = \frac{{{\rm{Total}}\;{\rm{number}}\;{\rm{of}}\;{\rm{defectives}}\;{\rm{from}}\;{\rm{all}}\;{\rm{samples}}\;{\rm{combined}}}}{{{\rm{Total}}\;{\rm{number}}\;{\rm{of}}\;{\rm{samples}}}}\\ = \frac{{3 + 4 + 2 + ..... + 20}}{{12\left( {250} \right)}}\\ = \frac{{103}}{{3000}}\\ = 0.034333\end{array}\)

The value of\(\bar q\)is computed as shown:

\(\begin{array}{c}\bar q = 1 - 0.034333\\ = 0.965667\end{array}\)

The value of the lower control limit (LCL) is computed below:

\(\begin{array}{c}LCL = \bar p - 3\sqrt {\frac{{\bar p\bar q}}{n}} \\ = 0.034333 - 3\sqrt {\frac{{\left( {0.034333} \right)\left( {0.965667} \right)}}{{250}}} \\ = - 0.00021\\ \approx 0\end{array}\)

The value of the upper control limit (UCL) is computed below:

\(\begin{array}{c}UCL = \bar p + 3\sqrt {\frac{{\bar p\bar q}}{n}} \\ = 0.034333 + 3\sqrt {\frac{{\left( {0.034333} \right)\left( {0.965667} \right)}}{{250}}} \\ = 0.06888133\end{array}\)

03

Computation of the fraction defective

The sample fraction defective for the ith batch can be computed as:

\({p_i} = \frac{{{d_i}}}{{250}}\)

Where,

\({p_i}\)be the sample fraction defective for the ith batch;

\({d_i}\)be the number of defective orders in the ith batch.

The computation of fraction defective for the ith batch is given as follows:

S.No.

Defectives (d)

Sample fraction defective (p)

1

3

0.012

2

4

0.016

3

2

0.008

4

5

0.020

5

3

0.012

6

6

0.024

7

8

0.032

8

9

0.036

9

12

0.048

10

14

0.056

11

17

0.068

12

20

0.080

04

Construction of the p chart

Follow the given steps to construct the p chart:

  • Mark the values 1, 2, ...,12 on the horizontal axis and label the axis as 鈥淪ample鈥.
  • Mark the values 0, 0.01, 0.02, 鈥︹, 0.09 on the vertical axis and label the axis as 鈥淧roportion鈥.
  • Plot a straight line corresponding to the value 鈥0.034333鈥 on the vertical axis and label the line (on the left side) as 鈥淺(CL = 0.034333\)鈥.
  • Plot a horizontal line corresponding to the value 鈥0鈥 on the vertical axis and label the line as 鈥淟CL=0.鈥
  • Similarly, plot a horizontal line corresponding to the value 鈥0. 0.0688813鈥 on the vertical axis and label the line as 鈥淯CL=0.0688813.鈥
  • Mark the given12 sample points (fraction defective of the ith lot) on the graph and join the dots using straight lines.

The following p chart is plotted:

05

Analysis of the p chart

The following criteria can be distinctly observed from the chart:

There appears to be an upward trend.

There is at least one point that lies beyond the upper control limit.

Since the above observations imply that the given process is not statistically stable, it can be concluded that the process is not within statistical control.

The proportion of defects in batteries is increasing. In order to produce good quality batteries, there is a need to take a corrective procedure to ensure that the number of defects declines.

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Most popular questions from this chapter

What is the difference between an R chart and an\(\bar x\) chart?

Control Charts for p. In Exercises 5鈥12, use the given process data to construct a control chart for p. In each case, use the three out-of-control criteria listed near the beginning of this section and determine whether the process is within statistical control. If it is not, identify which of the three out-of-control criteria apply

Euro Coins Consider a process of minting coins with a value of one euro. Listed below are the numbers of defective coins in successive batches of 10,000 coins randomly selected on consecutive days of production.

32 21 25 19 35 34 27 30 26 33

Defective Child Restraint Systems The Tracolyte Manufacturing Company produces plastic frames used for child booster seats in cars. During each week of production, 120 frames are selected and tested for conformance to all regulations by the Department of Transportation. Frames are considered defective if they do not meet all requirements. Listed below are the numbers of defective frames among the 120 that are tested each week. Use a control chart for p to verify that the process is within statistical control. If it is not in control, explain why it is not.

3 2 4 6 5 9 7 10 12 15

Quarters. In Exercises 9鈥12, refer to the accompanying table of weights (grams) of quarters minted by the U.S. government. This table is available for download at www.TriolaStats.com.

Day

Hour 1

Hour 2

Hour 3

Hour 4

Hour 5

\(\bar x\)

s

Range

1

5.543

5.698

5.605

5.653

5.668

5.6334

0.0607

0.155

2

5.585

5.692

5.771

5.718

5.72

5.6972

0.0689

0.186

3

5.752

5.636

5.66

5.68

5.565

5.6586

0.0679

0.187

4

5.697

5.613

5.575

5.615

5.646

5.6292

0.0455

0.122

5

5.63

5.77

5.713

5.649

5.65

5.6824

0.0581

0.14

6

5.807

5.647

5.756

5.677

5.761

5.7296

0.0657

0.16

7

5.686

5.691

5.715

5.748

5.688

5.7056

0.0264

0.062

8

5.681

5.699

5.767

5.736

5.752

5.727

0.0361

0.086

9

5.552

5.659

5.77

5.594

5.607

5.6364

0.0839

0.218

10

5.818

5.655

5.66

5.662

5.7

5.699

0.0689

0.163

11

5.693

5.692

5.625

5.75

5.757

5.7034

0.0535

0.132

12

5.637

5.628

5.646

5.667

5.603

5.6362

0.0235

0.064

13

5.634

5.778

5.638

5.689

5.702

5.6882

0.0586

0.144

14

5.664

5.655

5.727

5.637

5.667

5.67

0.0339

0.09

15

5.664

5.695

5.677

5.689

5.757

5.6964

0.0359

0.093

16

5.707

5.89

5.598

5.724

5.635

5.7108

0.1127

0.292

17

5.697

5.593

5.78

5.745

5.47

5.657

0.126

0.31

18

6.002

5.898

5.669

5.957

5.583

5.8218

0.185

0.419

19

6.017

5.613

5.596

5.534

5.795

5.711

0.1968

0.483

20

5.671

6.223

5.621

5.783

5.787

5.817

0.238

0.602

Quarters: R Chart Treat the five measurements from each day as a sample and construct an R chart. What does the result suggest?

Service Times The Newport Diner records the times (min) it takes before customers are asked for their order. Each day, 50 customers are randomly selected, and the order is considered to be defective if it takes longer than three minutes. The numbers of defective orders are listed below for consecutive days. Construct an appropriate control chart and determine whether the process is within statistical control. If not, identify which criteria lead to rejection of statistical stability.

3 2 3 5 4 6 7 9 8 10 11 9 12 15 17

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