Introduction to Statistical Quality Control
7th Edition
ISBN: 9781118146811
Author: Montgomery, Douglas C.
Publisher: John Wiley & Sons Inc
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Textbook Question
Chapter 3, Problem 21E
Reconsider the yield data in Exercise 3.9. Construct a time-series plot for these data. Interpret the plot.
The data shown in Table 3E.2 are chemical process yield readings on successive days (read down, then across). Construct a histogram for these data.
TABLE 3E.2 Process Meld
Comment on the shape of the histogram. Does it resemble any of the distributions that we have discussed in this chapter?
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Chapter 3 Solutions
Introduction to Statistical Quality Control
Ch. 3 - The content of liquid detergent bottles is being...Ch. 3 - The bore diameters of eight randomly selected...Ch. 3 - The service time in minutes from admit to...Ch. 3 - The Really Cool Clothing Company sells its...Ch. 3 - The nine measurements that follow are furnace...Ch. 3 - Consider the furnace temperature data in Exercise...Ch. 3 - Yield strengths of circular tubes with end caps...Ch. 3 - The time to failure in hours of an electronic...Ch. 3 - The data shown in Table 3E.2 are chemical process...Ch. 3 - An article in Quality Engineering (Vol. 4, 1992,...
Ch. 3 - Construct and interpret a normal probability plot...Ch. 3 - Construct and interpret a normal probability plot...Ch. 3 - Construct a normal probability plot of the failure...Ch. 3 - Construct a normal probability plot of the...Ch. 3 - Consider the viscosity data in Exercise 3.10....Ch. 3 - Table 3E.4 contains 20 observations on cycles to...Ch. 3 - An important quality characteristic of water is...Ch. 3 - Consider the outpatient service times in Exercise...Ch. 3 - Consider the call handling limes in Exercise 3.4....Ch. 3 - Consider the viscosity data in Exercise 3.10....Ch. 3 - Reconsider the yield data in Exercise 3.9....Ch. 3 - Consider the concentration of suspended solids...Ch. 3 - Consider the chemical process yield data in...Ch. 3 - Consider the chemical process yield data in...Ch. 3 - Construct a box plot for the data in Exercise 3.1.Ch. 3 - Construct a box plot for the data in Exercise 3.2.Ch. 3 - Suppose that two fair dice are tossed and the...Ch. 3 - Find the mean and variance of the random variable...Ch. 3 - A mechatronic assembly is subjected to a final...Ch. 3 - The probability distribution of x is f(x) = kex, 0...Ch. 3 - The random variable x takes on the values 1, 2, or...Ch. 3 - The probability distribution of the discrete...Ch. 3 - A manufacturer of electronic calculators offers a...Ch. 3 - The net contents in ounces of canned soup is a...Ch. 3 - A production process operates with 1 %...Ch. 3 - Continuation of Exercise 3.35. Consider the...Ch. 3 - A random sample of 50 units is drawn from a...Ch. 3 - A sample of 100 units is selected from a...Ch. 3 - Suppose that 10% of the adult population has blood...Ch. 3 - Patients arriving at an outpatient clinic are...Ch. 3 - A stock brokerage has four computers that are used...Ch. 3 - A computer system uses passwords consisting of the...Ch. 3 - An electronic component for a medical X-ray unit...Ch. 3 - A lot of size N = 30 contains three nonconforming...Ch. 3 - A textbook has 500 pages on which typographical...Ch. 3 - Surface-finish defects in a small electric...Ch. 3 - Glass bottles are formed by pouring molten glass...Ch. 3 - The billing department of a major credit card...Ch. 3 - A production process operates in one of two...Ch. 3 - An inspector is looking for nonconforming welds in...Ch. 3 - The tensile strength of a metal part is normally...Ch. 3 - The output voltage of a power supply is normally...Ch. 3 - Continuation of Exercise 3.52. Reconsider the...Ch. 3 - If x is normally distributed with mean and...Ch. 3 - The life of an automotive battery is normally...Ch. 3 - A lightbulb has a normally distributed light...Ch. 3 - Derive the mean and variance of the binomial...Ch. 3 - Derive the mean and variance of the Poisson...Ch. 3 - Derive the mean and variance of the exponential...Ch. 3 - Derive the mean and variance of the geometric...
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- Planetary Velocity The following table gives the mean velocity of planets in their orbits versus their mean distance from the sun. Note that 1AU astronomical unit is the mean distance from Earth to the sun, abut 93 million miles. Planet d=distance AU v=velocity km/sec Mercury 0.39 47.4 Venus 0.72 35.0 Earth 1.00 29.8 Mars 1.52 24.1 Jupiter 5.20 13.1 Saturn 9.58 9.7 Uranus 19.20 6.8 Neptune 30.05 5.4 Astronomers tell us that it is reasonable to model these data with a power function. a Use power regression to express velocity as a power function of distance from the sun. b Plot the data along with the regression equation. c An asteroid orbits at a mean distance of 3AU from the sun. According to the power model you found in part a, what is the mean orbital velocity of the asteroid?arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forward
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