 6.61: Will the sample mean always correspond to one of the observations i...
 6.62: Will exactly half of the observations in a sample fall below the mean?
 6.63: Will the sample mean always be the most frequently occurring data v...
 6.64: For any set of data values, is it possible for the sample standard ...
 6.65: Can the sample standard deviation be equal to zero? Give an example.
 6.66: Suppose that you add 10 to all of the observations in a sample. How...
 6.67: Eight measurements were made on the inside diameter of forged pisto...
 6.68: In Applied Life Data Analysis (Wiley, 1982), Wayne Nelson presents ...
 6.69: The January 1990 issue of Arizona Trend contains a supplement descr...
 6.610: An article in the Journal of Structural Engineering (Vol. 115, 1989...
 6.611: An article in Human Factors (June 1989) presented data on visual ac...
 6.612: The following data are direct solar intensity measurements (watts/m...
 6.613: The April 22, 1991, issue of Aviation Week and Space Technology rep...
 6.614: Preventing fatigue crack propagation in aircraft structures is an i...
 6.615: An article in the Journal of Physiology [Response of Rat Muscle to ...
 6.616: Exercise 611 describes data from an article in Human Factors on vi...
 6.617: The pH of a solution is measured eight times by one operator using ...
 6.618: An article in the Journal of Aircraft (1988) described the computat...
 6.619: The following data are the joint temperatures of the Orings (F) fo...
 6.620: When will the median of a sample be equal to the sample mean
 6.621: When will the median of a sample be equal to the mode
 6.622: An article in Technometrics (Vol. 19, 1977, p. 425) presented the f...
 6.623: The following data are the numbers of cycles to failure of aluminum...
 6.624: The percentage of cotton in material used to manufacture mens shirt...
 6.625: The following data represent the yield on 90 consecutive batches of...
 6.626: Calculate the sample median, mode, and mean of the data in Exercise...
 6.627: Calculate the sample median, mode, and mean of the data in Exercise...
 6.628: Calculate the sample median, mode, and mean for the data in Exercis...
 6.629: The net energy consumption (in billions of kilowatthours) for count...
 6.630: The female students in an undergraduate engineering core course at ...
 6.631: The shear strengths of 100 spot welds in a titanium alloy follow. C...
 6.632: An important quality characteristic of water is the concentration o...
 6.633: The United States Golf Association tests golf balls to ensure that ...
 6.634: A semiconductor manufacturer produces devices used as central proce...
 6.635: A group of wine enthusiasts tastetested a pinot noir wine from Ore...
 6.636: In their book Introduction to Linear Regression Analysis (4th editi...
 6.637: In Exercise 630, we presented height data that was selfreported b...
 6.638: Construct a frequency distribution and histogram for the motor fuel...
 6.639: Construct a frequency distribution and histogram using the failure ...
 6.640: Construct a frequency distribution and histogram for the cotton con...
 6.641: Construct a frequency distribution and histogram for the yield data...
 6.642: Construct frequency distributions and histograms with 8 bins and 16...
 6.643: Construct histograms with 8 and 16 bins for the data in Exercise 6...
 6.644: Construct histograms with 8 and 16 bins for the data in Exercise 6...
 6.645: Construct a histogram for the energy consumption data in Exercise 629
 6.646: Construct a histogram for the female student height data in Exercis...
 6.647: Construct a histogram for the spot weld shear strength data in Exer...
 6.648: Construct a histogram for the water quality data in Exercise 632. ...
 6.649: Construct a histogram for the overall golf distance data in Exercis...
 6.650: Construct a histogram for the semiconductor speed data in Exercise ...
 6.651: Construct a histogram for the pinot noir wine rating data in Exerci...
 6.652: The Pareto Chart. An important variation of a histogram for categor...
 6.653: The cold start ignition time of an automobile engine is being inves...
 6.654: An article in Transactions of the Institution of Chemical Engineers...
 6.655: The nine measurements that follow are furnace temperatures recorded...
 6.656: Exercise 618 presents drag coefficients for the NASA 0012 airfoil....
 6.657: Exercise 619 presented the joint temperatures of the Orings (F) f...
 6.658: Reconsider the motor fuel octane rating data in Exercise 620. Cons...
 6.659: Reconsider the energy consumption data in Exercise 629. Construct ...
 6.660: Reconsider the water quality data in Exercise 632. Construct a box...
 6.661: Reconsider the weld strength data in Exercise 631. Construct a box...
 6.662: Reconsider the semiconductor speed data in Exercise 634. Construct...
 6.663: Use the data on heights of female and male engineering students fro...
 6.664: In Exercise 653, data were presented on the cold start ignition ti...
 6.665: An article in Nature Genetics [Treatmentspecific Changes in Gene E...
 6.666: The following data are the viscosity measurements for a chemical pr...
 6.667: The pulloff force for a connector is measured in a laboratory test...
 6.668: In their book Time Series Analysis, Forecasting, and Control (Prent...
 6.669: The 100 annual Wolfer sunspot numbers from 1770 to 1869 follow. (Fo...
 6.670: In their book Introduction to Time Series Analysis and Forecasting,...
 6.671: The following table shows the number of earthquakes per year of mag...
 6.672: The following table shows U.S. petroleum imports, imports as a perc...
 6.673: Construct a normal probability plot of the piston ring diameter dat...
 6.674: Construct a normal probability plot of the insulating fluid breakdo...
 6.675: Construct a normal probability plot of the visual accommodation dat...
 6.676: Construct a normal probability plot of the solar intensity data in ...
 6.677: Construct a normal probability plot of the Oring joint temperature...
 6.678: Construct a normal probability plot of the octane rating data in Ex...
 6.679: Construct a normal probability plot of the cycles to failure data i...
 6.680: Construct a normal probability plot of the suspended solids concent...
 6.681: Construct two normal probability plots for the height data in Exerc...
 6.682: It is possible to obtain a quick and dirty estimate of the mean of ...
 6.683: The National Oceanic and Atmospheric Administration provided the mo...
 6.684: The concentration of a solution is measured six times by one operat...
 6.685: The table below shows unemployment data for the U.S. that are seaso...
 6.686: A sample of six resistors yielded the following resistances (ohms):...
 6.687: Consider the following two samples: Sample 1: 10, 9, 8, 7, 8, 6, 10...
 6.688: An article in Quality Engineering (Vol. 4, 1992, pp. 487495) presen...
 6.689: The total net electricity consumption of the U.S. by year from 1980...
 6.690: Reconsider the data from Exercise 688. Prepare comparative box plo...
 6.691: The data shown in Table 67 are monthly champagne sales in France (...
 6.692: The following data are the temperatures of effluent at discharge fr...
 6.693: A manufacturer of coil springs is interested in implementing a qual...
 6.694: A communication channel is being monitored by recording the number ...
 6.695: Reconsider the golf course yardage data in Exercise 69. Construct ...
 6.696: Reconsider the data in Exercise 688. Construct normal probability ...
 6.697: Construct a normal probability plot of the effluent discharge tempe...
 6.698: Construct normal probability plots of the cold start ignition time ...
 6.699: Reconsider the golf ball overall distance data in Exercise 633. Co...
 6.6100: Transformations. In some data sets, a transformation by some mathem...
 6.6101: In 1879, A. A. Michelson made 100 determinations of the velocity of...
 6.6102: In 1789, Henry Cavendish estimated the density of the earth by usin...
 6.6103: In their book Introduction to Time Series Analysis and Forecasting ...
 6.6104: Patients arriving at a hospital emergency department present a vari...
 6.6105: Consider the airfoil data in Exercise 618. Subtract 30 from each v...
 6.6106: Consider the quantity . For what value of a is this quantity minimi...
 6.6107: Using the results of Exercise 6106, which of the two quantities an...
 6.6108: Coding the Data. Let i 1, 2, . . . , n, where a and b are nonzero c...
 6.6109: A sample of temperature measurements in a furnace yielded a sample ...
 6.6110: Consider the sample with sample mean and sample standard deviation ...
 6.6111: An experiment to investigate the survival time in hours of an elect...
 6.6112: Suppose that we have a sample x1, x2, p , xn and we have calculated...
 6.6113: Trimmed Mean. Suppose that the data are arranged in increasing orde...
 6.6114: Trimmed Mean. Suppose that the sample size n is such that the quant...
Solutions for Chapter 6: Descriptive Statistics
Full solutions for Applied Statistics and Probability for Engineers  5th Edition
ISBN: 9780470053041
Solutions for Chapter 6: Descriptive Statistics
Get Full SolutionsSince 114 problems in chapter 6: Descriptive Statistics have been answered, more than 24298 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 6: Descriptive Statistics includes 114 full stepbystep solutions. This textbook survival guide was created for the textbook: Applied Statistics and Probability for Engineers, edition: 5. Applied Statistics and Probability for Engineers was written by and is associated to the ISBN: 9780470053041.

2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

Alias
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

Bias
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

Causeandeffect diagram
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

Chisquare (or chisquared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

Comparative experiment
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

Conditional mean
The mean of the conditional probability distribution of a random variable.

Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.

Confounding
When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.

Conidence level
Another term for the conidence coeficient.

Control limits
See Control chart.

Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.

Discrete random variable
A random variable with a inite (or countably ininite) range.

Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

Exponential random variable
A series of tests in which changes are made to the system under study

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .

Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .