 3.4.1E: Find the uncertainty in U, assuming that X = 10.0 ± 0.5, Y = 5.0 ± ...
 3.4.2E: The volume of a cone is given by V = ?r2h/3, where is the radius of...
 3.4.3E: From a fixed point on the ground, the distance to a certain tree is...
 3.4.4E: Refer to Exercise 10 in Section 3.2. Assume that ? = 30.0 ± 0.1 Pa,...
 3.4.5E: When air enters a compressor at pressure P1 and leaves at pressure ...
 3.4.6E: One way to measure the water content of a soil is to weigh the soil...
 3.4.7E: The lens equation says that if an object is placed at a distance p ...
 3.4.8E: The pressure P, temperature T, and volume V of one mole of an ideal...
 3.4.9E: The BeerLambert law relates the absorbance A of a solution to the ...
 3.4.10E: In the article “TemperatureDependent Optical Constants of Water Ic...
 3.4.11E: Refer to Exercise 12 in Section 3.2. Assume that ?0 = 50 ± 1 MPa, w...
 3.4.12E: According to Snell’s law, the angle of refraction ?2 of a light ray...
 3.4.13E: Archaeologists studying meat storage methods employed by the Nunami...
 3.4.14E: The resistance R (in ohms) of a cylindrical conductor is given by R...
 3.4.15E: A cylindrical wire of radius R elongates when subjected to a tensil...
 3.4.16E: According to Newton’s law of cooling, the time t needed for an obje...
 3.4.17E: Refer to Exercise 16. In an experiment to determine the value of k,...
 3.4.18E: The vertical displacement v of a cracked slurry infiltrated fiber c...
 3.4.19E: The shape of a bacterium can be approximated by a cylinder of radiu...
 3.4.20E: Estimate U, and find the relative uncertainty in the estimate, assu...
 3.4.21E: Refer to Exercise 10 in Section 3.2. Assume that ? = 35.2 ± 0.1 Pa,...
 3.4.22E: Refer to Exercise 5. Assume that P1 = 15.3 ± 0.2 MPa and P2 = 25.8 ...
 3.4.23E: Refer to Exercise 7. Assume that p = 4.3 ± 0.1 cm and q =2.1 ± 0.2 ...
 3.4.24E: Refer to Exercise 8. a. Assume that P = 224.51 ± 0.04 kPa and V = 1...
 3.4.25E: Refer to Exercise 12. Estimate n, and find the relative uncertainty...
 3.4.26E: Refer to Exercise 14. Assume that l = 10.0 cm ± 0.5% and d = 10.4 c...
 3.4.27E: Refer to Exercise 15. Assume that F = 750 ± 1 N, R = 0.65 ± 0.09 mm...
 3.4.28E: Refer to Exercise 16. Assume that T0 = 73.1 ± 0.1 °F, Ta = 37.5 ± 0...
 3.4.29E: Refer to Exercise 19. Assume that for a certain bacterium, r = 0.8 ...
 3.4.30E: Refer to Exercise 5. Assume that the relative uncertainty in P1, is...
 3.4.31E: Refer to Exercise 14. Assume that the relative uncertainty in l is ...
Solutions for Chapter 3.4: Statistics for Engineers and Scientists 4th Edition
Full solutions for Statistics for Engineers and Scientists  4th Edition
ISBN: 9780073401331
Solutions for Chapter 3.4
Get Full SolutionsSince 31 problems in chapter 3.4 have been answered, more than 148217 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 3.4 includes 31 full stepbystep solutions. Statistics for Engineers and Scientists was written by and is associated to the ISBN: 9780073401331. This textbook survival guide was created for the textbook: Statistics for Engineers and Scientists , edition: 4.

All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

Assignable cause
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

Center line
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

Chance cause
The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.

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 probability
The probability of an event given that the random experiment produces an outcome in another event.

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.

Continuous random variable.
A random variable with an interval (either inite or ininite) of real numbers for its range.

Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.

Convolution
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

Experiment
A series of tests in which changes are made to the system under study

Ftest
Any test of signiicance involving the F distribution. The most common Ftests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating function

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 .

Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

Harmonic mean
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .