In Exercises 5–8, use the following two control charts that result from testing batches of newly manufactured aircraft altimeters, with 100 in each batch. The original sample values are errors (in feet) obtained when the altimeters are tested in a pressure chamber that simulates an altitude of 6000 ft. The Federal Aviation Administration requires an error of no more than 40 ft at that altitude.

Is the process mean within statistical control? Why or why not?

Short Answer

Expert verified

No, the process mean is not within statistical control as the following two features are clearly noticeable in the \(\bar x\) chart:

  • At least eightconsecutive points lie below the centerline.
  • At least one point lies above the upper control limit.

Step by step solution

01

Given information

The\(\bar x\)chart is plotted for the measurement of errors (in feet) obtained when the aircraft altimetersare tested in a pressure chamber.

The sample size is equal to 100.

02

Criteria to determine when the statistical process is out of control

If any of the below-mentioned features appear,the process is said to be out of control.

  • There is a distinguishably visible pattern, trend, or cycle.
  • One or more pointsexceed the upper control limit and fall below the lower control limit.
  • At least eight points must lie above or below the centerline in a row. This condition is called the Run of 8 Rule.
03

Step 3:Analysing process control

Since it is required to assess whether the process mean is within statistical control or not, the

\(\bar x\)chart is observed.

The following two criteria can be distinctly observed, which imply that the process mean is out of control:

  • More than eight consecutive points lie below the centerline (CL).
  • Some points lie above the upper control limit (UCL).

Thus, it can be concluded that the process mean is not within statistical control.

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Most popular questions from this chapter

Notation The control chart for Exercise 1 shows a value of \(\bar p\) = 0.0975. What does that value denote, and how is it obtained? What do UCL and LCL indicate?

Heights On the basis of Data Set 1 “Body Data” in Appendix B, assume that heights of men are normally distributed, with a mean of 68.6 in. and a standard deviation of 2.8 in.

a. The U.S. Coast Guard requires that men must have a height between 60 in. and 80 in. Findthe percentage of men who satisfy that height requirement.

b. Find the probability that 4 randomly selected men have heights with a mean greater than 70 in.

In a survey of n= 2015 adults, 1108 of them said that they learn about medical symptoms more often from the internet than from their doctor (based on a MerckManuals.com survey). Use the data to construct a 95% confidence interval estimate of the population proportion of all adults who say that they learn about medical symptoms more often from the internet than from their doctor. Does the result suggest that the majority of adults learn about medical symptoms more often from the internet than from their doctor?

Quarters. In Exercises 9–12, 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: \(\bar x\)-Chart Treat the 5 measurements from each day as a sample and construct an \(\bar x\)- chart. What does the result suggest?

Energy Consumption. Exercises 1–5 refer to the amounts of energy consumed in the author’s home. (Most of the data are real, but some are fabricated.) Each value represents the energy consumed (kWh) in a two-month period. Let each subgroup consist of the six amounts within the same year. Data are available for download at www.TriolaStats.com.


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: Run Chart Construct a run chart for the 48 values. Does there appear to be a pattern suggesting that the process is not within statistical control?

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