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The article 鈥淪tudy on the Life Distribution of Microdrills鈥 (J. of Engr. Manufacture, 2002: 301鈥 305) reported the following observations, listed in increasing order, on drill lifetime (number of holes that a drill machines before it breaks) when holes were drilled in a certain brass alloy.

11 14 20 23 31 36 39 44 47 50

59 61 65 67 68 71 74 76 78 79

81 84 85 89 91 93 96 99 101 104

105 105 112 118 123 136 139 141 148 158

161 168 184 206 248 263 289 322 388 513

a. Why can a frequency distribution not be based on the class intervals 0鈥50, 50鈥100, 100鈥150, and so on?

b. Construct a frequency distribution and histogram of the data using class boundaries 0, 50, 100, 鈥 , and then comment on interesting characteristics.

c. Construct a frequency distribution and histogram of the natural logarithms of the lifetime observations, and comment on interesting characteristics.

d. What proportion of the lifetime observations in this sample are less than 100? What proportion of the observations are at least 200?

Short Answer

Expert verified

a.

Observation 50 falls on a class boundary.

b. The histogram is represented as,

c. The histogram is represented as,

d.

The proportion of the lifetime observations in this sample that are less than 100 is 0.56.

The proportion of the lifetime observations in this sample that are at least200 is 0.14.

Step by step solution

01

Given information

The data for the drill lifetime (number of holes that a drill machines before it breaks) when holes were drilled in a certain brass alloy is provided.

02

Explain why frequency distribution for interval 0-50, 50-100 can not be constructed.

a.

The frequency distribution based on the class intervals 0-50, 50-100 cannot be constructed because the observation 50 falls on a class boundary.

03

Given information

The data for the drill lifetime (number of holes that a drill machines before it breaks) when holes were drilled in a certain brass alloy is provided.

04

Construct a frequency distribution and a histogram

b.

The relative frequency is computed as,

\({\rm{relative frequency }} = \frac{{frequency}}{{Total\;number\;of\;observations}}\)

The frequency distribution using the class boundaries 0,50,100 is as follows,

Class

Frequency

Relative frequency

0鈭&濒迟;50

9

0.18

50鈭&濒迟;100

19

0.38

100鈭&濒迟;150

11

0.22

150-<200

4

0.08

200鈭&濒迟;300

4

0.08

300鈭&濒迟;400

2

0.04

400鈭&濒迟;500

0

0

500鈭&濒迟;600

1

0.02

Total

50

1

Steps to construct a histogram are,

1) Determine the frequency or the relative frequency.

2) Mark the class boundaries on the horizontal axis.

3) Draw a rectangle on the horizontal axis corresponding to the frequency or relative frequency.

The histogram is represented as,

It can be observed from the above histogram that the distribution is positively skewed and the outliers are in the range of 500 to 600.

05

Given information

The data for the drill lifetime (number of holes that a drill machines before it breaks) when holes were drilled in a certain brass alloy is provided.

06

Construct a frequency distribution and a histogram

c.

The relative frequency is computed as,

\({\rm{relative frequency }} = \frac{{frequency}}{{Total\;number\;of\;observations}}\)

The frequency distribution of the natural logarithms of the lifetime observations is represented as,

Class

Frequency

Relative frequency

2.25鈭&濒迟;2.75

2

0.04

2.75鈭&濒迟;3.25

2

0.04

3.25鈭&濒迟;3.75

3

0.06

3.75鈭&濒迟;4.25

8

0.16

4.25鈭&濒迟;4.75

18

0.36

4.75鈭&濒迟;5.25

10

0.2

5.25鈭&濒迟;5.75

4

0.08

5.75鈭&濒迟;6.25

3

0.06

Total

50

1

Steps to construct a histogram are,

1) Determine the frequency or the relative frequency.

2) Mark the class boundaries on the horizontal axis.

3) Draw a rectangle on the horizontal axis corresponding to the frequency or relative frequency.

The histogram is represented as,

The characteristics of the above graph are:

1) The distribution is negatively skewed.

2) There are no outliers present.

3) The typical value is 4.25.

07

Given information

The data for the drill lifetime (number of holes that a drill machines before it breaks) when holes were drilled in a certain brass alloy is provided.

08

Compute the proportion

Referring to the relative frequencies computed in part b,

d.

Let x represents the drill lifetime.

The proportion of the lifetime observations in this sample that are less than 100 is computed as,

\(\begin{aligned}P\left( {x < 100} \right) &= P\left( {0 - < 50} \right) + P\left( {50 - < 100} \right)\\ &= 0.18 + 0.38\\ &= 0.56\end{aligned}\)

Thus, the proportion of the lifetime observations in this sample that are less than 100 is 0.56.

The proportion of the lifetime observations in this sample that are at least200 is computed as,

\(\begin{aligned}P\left( {x \ge 200} \right) &= 1 - \left( {P\left( {0 - < 50} \right) + P\left( {50 - < 100} \right) + P\left( {50 - < 150} \right) + P\left( {150 - < 200} \right)} \right)\\ &= 1 - \left( {0.18 + 0.38 + 0.22 + 0.08} \right)\\ &= 1 - 0.86\\ &= 0.14\end{aligned}\)

Thus, the proportion of the lifetime observations in this sample that are at least200 is 0.14.

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Most popular questions from this chapter

The value of Young鈥檚 modulus (GPa) was determined forcast plates consisting of certain intermetallic substrates,resulting in the following sample observations (鈥淪trengthand Modulus of a Molybdenum-Coated Ti-25Al-10Nb-3U-1Mo Intermetallic,鈥 J. of Materials Engr.and Performance, 1997: 46鈥50):

116.4 115.9 114.6 115.2 115.8

  1. Calculate\({\bf{\bar x}}\) and the deviations from the mean.
  2. Use the deviations calculated in part (a) to obtain the sample variance and the sample standard deviation.
  3. Calculate\({{\bf{s}}^{\bf{2}}}\)by using the computational formula for the numerator \({S_{xx}}\).
  4. Subtract 100 from each observation to obtain a sample of transformed values. Now calculate the sample variance of these transformed values, and compare it to\({{\bf{s}}^{\bf{2}}}\)for the original data.

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4570

4990

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5015

4659

4806

4637

5670

4381

4820

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4886

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5189

4986

The cdf for \({\rm{X( = measurement error)}}\) of Exercise \({\rm{3}}\) is

\({\rm{F(x) = }}\left\{ {\begin{array}{*{20}{c}}{\rm{0}}&{{\rm{x < - 2}}}\\{\frac{{\rm{1}}}{{\rm{2}}}{\rm{ + }}\frac{{\rm{3}}}{{{\rm{32}}}}\left( {{\rm{4x - }}\frac{{{{\rm{x}}^{\rm{3}}}}}{{\rm{3}}}} \right)}&{{\rm{ - 2}} \le {\rm{x < 2}}}\\{\rm{1}}&{{\rm{2}} \le {\rm{x}}}\end{array}} \right.\)

a. Compute \({\rm{P(X < 0)}}\).

b. Compute \({\rm{P( - 1 < X < 1)}}\).

c. Compute \({\rm{P(}}{\rm{.5 < X)}}\).

d. Verify that \({\rm{f(x)}}\) is as given in Exercise \({\rm{3}}\) by obtaining \({\rm{F'(x)}}\).

e. Verify that \(\widetilde {\rm{\mu }}{\rm{ = 0}}\).

The three measures of center introduced in this chapter are the mean, median, and trimmed mean. Two additional measures of center that are occasionally used are the midrange,which is the average of the smallest and largest observations, and the midfourth,which is the average of the two fourths. Which of these five measures of center are resistant to the effects of outliers and which are not? Explain your reasoning.

The California State University (CSU) system consists of 23 campuses, from San Diego State in the south to Humboldt State near the Oregon border. A CSU administrator wishes to make an inference about the average distance between the hometowns of students and their campuses. Describe and discuss several different sampling methods that might be employed. Would this be an enumerative or an analytic study? Explain yourreasoning.

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