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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

Euro Coins Repeat Exercise 5, assuming that the size of each batch is 100 instead of 10,000. Compare the control chart to the one found for Exercise 5. Comment on the general quality of the manufacturing process described in Exercise 5 compared to the manufacturing process described in this exercise.

Short Answer

Expert verified

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

The process seems to be within statistical control since none of the three out-of-control criteria is visible from the chart.

The p chart corresponding to the sample size of 10,000 is nearly identical to the p chart shown above with a sample size of 100.

Because the two p charts have comparable outlooks, the manufacturing process quality for the two processes can also be considered to be the same.

Furthermore, whether the sample size is 100 or 10,000, the process produces good and reliable goods, and the manufacturer should continue with the current procedure.

Step by step solution

01

Given information

Data are given on the number of defective coins in 10 samples.

The sample size of each of the 10 samples is equal to 100.

02

Important values of p chart

Let\(\bar p\)be the estimated proportion of defective coins in all the samples.

It is computed as follows:

\(\begin{array}{c}\bar p = \frac{{{\rm{Total}}\;{\rm{number}}\;{\rm{of}}\;{\rm{defectives}}\;{\rm{from}}\;{\rm{all}}\;{\rm{samples}}\;{\rm{combined}}}}{{{\rm{Total}}\;{\rm{number}}\;{\rm{of}}\;{\rm{samples}}}}\\ = \frac{{32 + 21 + 25 + ..... + 33}}{{10\left( {100} \right)}}\\ = \frac{{282}}{{1000}}\\ = 0.282\end{array}\)

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

\(\begin{array}{c}\bar q = 1 - 0.282\\ = 0.718\end{array}\)

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

\(\begin{array}{c}LCL = \bar p - 3\sqrt {\frac{{\bar p\bar q}}{n}} \\ = 0.282 - 3\sqrt {\frac{{\left( {0.282} \right)\left( {0.718} \right)}}{{100}}} \\ = 0.147008\end{array}\)

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

\(\begin{array}{c}UCL = \bar p + 3\sqrt {\frac{{\bar p\bar q}}{n}} \\ = 0.282 + 3\sqrt {\frac{{\left( {0.282} \right)\left( {0.718} \right)}}{{100}}} \\ = 0.416992\end{array}\)

03

Computation of the fraction defective

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

\({p_i} = \frac{{{d_i}}}{{100}}\)

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

32

0.32

2

21

0.21

3

25

0.25

4

19

0.19

5

35

0.35

6

34

0.34

7

27

0.27

8

30

0.30

9

26

0.26

10

33

0.33

04

Construction of the p chart

Follow the given steps to construct the p chart:

  • Mark the values 1, 2, ..., 10 on the horizontal axis and label the axis as 鈥淪ample.鈥
  • Mark the values 0, 0.05, 0.1, 鈥︹, 0.45 on the vertical axis and label the axis as 鈥淧roportion.鈥
  • Plot a straight line corresponding to the value 鈥0.282鈥 on the vertical axis and label the line (on the left side) as 鈥淺(CL = 0.282\).鈥
  • Plot a horizontal line corresponding to the value 鈥0. 147008鈥 on the vertical axis and label the line as 鈥淟CL=0.147008鈥.
  • Similarly, plot a horizontal line corresponding to the value 鈥0.416992鈥 on the vertical axis and label the line as 鈥淯CL=0.416992.鈥
  • Mark the given 10 sample points (fraction defective of the ith batch) on the graph and join the dots using straight lines.

The following p chart is plotted:

05

Analysis of the p chart

None of the three out-of-control criteria is depicted in the plotted p chart.

Thus, it can be concluded that the process is within statistical control.

06

Comparison

Referring to Exercise 5, the p chart corresponding to each sample size is equal to 10,000. In this exercise, the p chart with a sample size equal to 100 is approximately identical to the referred Exercise 5.

Since the two p charts have similar outlooks, it can be said that the quality of the manufacturing process for the two processes is also the same.

Moreover, the process dishes out good and reliable products (whether the sample size is 100 or 10,000), and the manufacturer should continue with the ongoing procedure.

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Most popular questions from this chapter

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

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Jan.-Feb.

Mar.-April

May-June

July-Aug.

Sept.-Oct.

Nov.-dec.

Year 1

3637

2888

2359

3704

3432

2446

Year 2

4463

2482

2762

2288

2423

2483

Year 3

3375

2661

2073

2579

2858

2296

Year 4

2812

2433

2266

3128

3286

2749

Year 5

3427

578

3792

3348

2937

2774

Year 6

4016

3458

3395

4249

4003

3118

Year 7

4016

3458

3395

4249

4003

3118

Year 8

4016

3458

3395

4249

4003

3118

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Euro Coins After constructing a control chart for the proportions of defective one-euro coins, it is concluded that the process is within statistical control. Does it follow that almost all of the coins meet the desired specifications? Explain.

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Jan.-Feb.

Mar.-April

May-June

July-Aug.

Sept.-Oct.

Nov.-dec.

Year 1

3637

2888

2359

3704

3432

2446

Year 2

4463

2482

2762

2288

2423

2483

Year 3

3375

2661

2073

2579

2858

2296

Year 4

2812

2433

2266

3128

3286

2749

Year 5

3427

578

3792

3348

2937

2774

Year 6

4016

3458

3395

4249

4003

3118

Year 7

4016

3458

3395

4249

4003

3118

Year 8

4016

3458

3395

4249

4003

3118

Energy Consumption:\(\bar x\)Chart Let each subgroup consist of the 6 values within a year. Construct an\(\bar x\)chart and determine whether the process mean is within statistical control. If it is not, identify which of the three out-of-control criteria lead to rejection of a statistically stable mean.

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3 2 4 6 5 9 7 10 12 15

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