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

Short Answer

Expert verified

The following p chart is constructed for the given number of defects:

None of the features in the chart indicates that the process is unstable.

But the values of the proportions of defectives seem to be very high.

As a result, the producer must respond quickly to improve the quality of goods produced.

Step by step solution

01

Given information

Data are given on the number of defects in 20 randomly selected samples.

The size of each sample is 50.

02

Important values of p chart

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

It is computed as follows:

\(\begin{array}{c}\bar p = \frac{{{\rm{Total}}\;{\rm{number}}\;{\rm{of}}\;{\rm{defectives}}\;{\rm{from}}\;{\rm{all}}\;{\rm{samples}}\;{\rm{combined}}}}{{{\rm{Total}}\;{\rm{number}}\;{\rm{of}}\;{\rm{observations}}}}\\ = \frac{{8 + 7 + 9 + ..... + 7}}{{20\left( {50} \right)}}\\ = \frac{{165}}{{1000}}\\ = 0.165\end{array}\)

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

\(\begin{array}{c}\bar q = 1 - 0.165\\ = 0.835\end{array}\)

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

\(\begin{array}{c}LCL = \bar p - 3\sqrt {\frac{{\bar p\bar q}}{n}} \\ = 0.165 - 3\sqrt {\frac{{\left( {0.165} \right)\left( {0.835} \right)}}{{50}}} \\ = 0.0075\end{array}\)

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

\(\begin{array}{c}UCL = \bar p + 3\sqrt {\frac{{\bar p\bar q}}{n}} \\ = 0.165 + 3\sqrt {\frac{{\left( {0.165} \right)\left( {0.835} \right)}}{{50}}} \\ = 0.3225\end{array}\)

03

Computation of the fraction defective

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

\({p_i} = \frac{{{d_i}}}{{50}}\)

Here,

\({p_i}\)is the sample fraction defective for the ith batch, and

\({d_i}\)is 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

8

0.16

2

7

0.14

3

9

0.18

4

8

0.16

5

10

0.20

6

6

0.12

7

5

0.10

8

7

0.14

9

9

0.18

10

12

0.24

11

9

0.18

12

6

0.12

13

8

0.16

14

7

0.14

15

9

0.18

16

8

0.16

17

11

0.22

18

10

0.20

19

9

0.18

20

7

0.14

04

Construction of the p chart

Follow the given steps to construct the p chart:

  • Mark the values 2, 4, ...,20on the horizontal axis and label it “Sample.”
  • Mark the values 0.00, 0.05, 0.10, ……, 0.35 on the vertical axis and label it “Proportion.”
  • Plot a straight line corresponding to the value 0.165 on the vertical axis and label it (on the left side) “\(\bar P\;or\;\bar p = 0.165\).”
  • Plot a horizontal line corresponding to the value 0.0075 on the vertical axis and label it “LCL=0.0075.”
  • Similarly, plot a horizontal line corresponding to the value 0.3225 on the vertical axis and label it “UCL=0.3225.”
  • Mark all 20 sample points (fraction defective of the ith lot) on the graph and join the dots using straight lines.

The following p chart is obtained:

05

Analysis of the p chart

The chart has no feature that indicates the violation of the stability of the process. Thus, the process is within statistical control.

But the values of the proportions of defectives seem to be very high.

Thus, the manufacturer should take quick action to correct the quality of the goods produced.

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Most popular questions from this chapter

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 atwww.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: R Chart Let each subgroup consist of the 6 values within a year. Construct an R chart and determine whether the process variation is within statistical control. If it is not, identify which of the three out-of-control criteria lead to rejection of statistically stable variation

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?

Control Limits In constructing a control chart for the proportions of defective dimes, it is found that the lower control limit is -0.00325. How should that value be adjusted?

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?

Child Restraint Systems Use the numbers of defective child restraint systems given in Exercise 8. Find the mean, median, and standard deviation. What important characteristic of the sample data is missed if we explore the data using those statistics?

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