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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 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:\(\bar x\)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.

Short Answer

Expert verified

The following is the constructed \(\bar x\) chart for the given samples:

There seems to be an upward shift in the sample means, which indicates that the process is not stable.

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

Step by step solution

01

Given information

Data values are given for eightyears on the energy consumed (in kWh) in a two-month period.

The sample size for each of the eightyears is equal to 6.

02

Important lines in \(\bar x\) Chart

For constructing the\(\bar x\)chart, the values of the central line\(\bar \bar x\), the lower control limit (LCL), and the upper control limit (UCL) need to be determined.

Referring to Exercise 1 CRE, the values are as follows:

\(\begin{aligned}{l}\bar \bar x = 3157\;{\rm{kWh}}\\LCL = 2322\;{\rm{kWh}}\\UCL = 3992\;{\rm{kWh}}\end{aligned}\)

03

Construction

Follow the given steps to construct the R chart:

  • Mark the values 1, 2, ..., 8 on the horizontal axis and label the axis as “Sample.”
  • Mark the values 2500, 3000, ……,4000 on the vertical axis and label the axis as “Sample Mean.”
  • Plot a straight line corresponding to the value “3157” on the vertical axis and label the line (on the left side) as “\(\bar \bar x\)=3157.”
  • Plot a horizontal line corresponding to the value “2322” on the vertical axis and label the line as “LCL=2322.”
  • Similarly, plot a horizontal line corresponding to the value “3992” on the vertical axis and label the line as “UCL=3992.”
  • Mark the sample means on the graph and join the dots using straight lines.

Sample No.

Sample Means

1

3078

2

2817

3

2640

4

2779

5

2809

6

3707

7

3772

8

3652

The following \(\bar x\)chart is plotted:

04

Analysis of the\(\bar x\)chart

Here, the initial few points lie very low in the chart, and the points at the end lie very high.

This indicates that there has been an upward shift in the values, which violates the stability/control of the process.

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

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Most popular questions from this chapter

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.

Listed below are annual sunspot numbers paired with annual high values of the Dow Jones Industrial Average (DJIA). Sunspot numbers are measures of dark spots on the sun, and the DJIA is an index that measures the value of select stocks. The data are from recent and consecutive years. Use a 0.05 significance level to test for a linear correlation between values of the DJIA and sunspot numbers. Is the result surprising?

Sunspot

DJIA

45

10941

31

12464

46

14198

31

13279

50

10580

48

11625

56

12929

38

13589

65

16577

51

18054

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.

If the R chart and\(\bar x\)chart both showed that the process of manufacturing aircraft altimeters is within statistical control, can we conclude that the altimeters satisfy the Federal Aviation Administration requirement of having errors of no more than 40 ft when tested at an altitude of 6000 ft?

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

Control Charts for p. In Exercises 5–12, 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

Cola Cans In each of several consecutive days of production of cola cans, 50 cans are tested and the numbers of defects each day are listed below. Do the proportions of defects appear to be acceptable? What action should be taken?

8 7 9 8 10 6 5 7 9 12 9 6 8 7 9 8 11 10 9 7

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