 2.4.1E: Explain how to find the range of a data set. What is an advantage o...
 2.4.2E: Explain how to find the deviation of an entry in a data set. What i...
 2.4.3E: Why is the standard deviation used more frequently than the variance?
 2.4.4E: Explain the relationship between variance and standard deviation. C...
 2.4.5E: Constructing Data Sets In Exercise, construct a data set that has t...
 2.4.6E: Constructing Data Sets In Exercise, construct a data set that has t...
 2.4.7E: Describe the difference between the calculation of population stand...
 2.4.8E: Given a data set, how do you know whether to calculate ? or s?
 2.4.9E: Discuss the similarities and the differences between the Empirical ...
 2.4.10E: What must you know about a data set before you can use the Empirica...
 2.4.16E: Graphical Reasoning In Exercise, find the range of the data set rep...
 2.4.17E: Graphical Reasoning In Exercise, find the range of the data set rep...
 2.4.19E: Archaeology The depths (in inches) at which 10 artifacts are found ...
 2.4.20E: In Exercise, compare your answer to part (a) with your answer to pa...
 2.4.21E: Graphical Reasoning Both data sets shown in the stemandleaf plots...
 2.4.22E: Graphical Reasoning Both data sets shown in the histograms have a m...
 2.4.23E: Salary Offers You are applying for jobs at two companies. Company A...
 2.4.25E: Comparing Two Data Sets In Exercise, find the coefficient of variat...
 2.4.26E: Comparing Two Data Sets In Exercise, find the coefficient of variat...
 2.4.27E: Comparing Two Data Sets In Exercise, find the coefficient of variat...
 2.4.28E: Comparing Two Data Sets In Exercise, find the coefficient of variat...
 2.4.29E: Graphical Reasoning In Exercise, you are asked to compare three dat...
 2.4.30E: Graphical Reasoning In Exercise, you are asked to compare three dat...
 2.4.31E: Graphical Reasoning In Exercise, you are asked to compare three dat...
 2.4.32E: Graphical Reasoning In Exercise, you are asked to compare three dat...
 2.4.39E: Chebychev’s Theorem Old Faithful is a famous geyser at Yellowstone ...
 2.4.42E: Calculating Using Grouped Data In Exercise, make a frequency distri...
 2.4.52E: Shortcut Formula You used SSx ? = 2 (x  x)2 when calculating varia...
 2.4.53E: Scaling Data Sample annual salaries (in thousands of dollars) for e...
 2.4.54E: Shifting Data Sample annual salaries (in thousands of dollars) for ...
 2.4.55E: Mean Absolute Deviation Another useful measure of variation for a d...
 2.4.56E: Chebychev’s Theorem At least 99% of the data in any data set lie wi...
 2.4.57E: Pearson’s Index of Skewness The English statistician Karl Pearson (...
Solutions for Chapter 2.4: Elementary Statistics: Picturing the World 5th Edition
Full solutions for Elementary Statistics: Picturing the World  5th Edition
ISBN: 9780321693624
Solutions for Chapter 2.4
Get Full SolutionsSince 33 problems in chapter 2.4 have been answered, more than 12236 students have viewed full stepbystep solutions from this chapter. Chapter 2.4 includes 33 full stepbystep solutions. This textbook survival guide was created for the textbook: Elementary Statistics: Picturing the World, edition: 5. Elementary Statistics: Picturing the World was written by and is associated to the ISBN: 9780321693624. This expansive textbook survival guide covers the following chapters and their solutions.

`error (or `risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).

Acceptance region
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

Addition rule
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).

Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

Central tendency
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

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

Conditional probability distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

Contour plot
A twodimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

Correction factor
A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

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

Defect
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

Defectsperunit control chart
See U chart

Error of estimation
The difference between an estimated value and the true value.

Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a modelitting process and not on replication.

Event
A subset of a sample space.

Exhaustive
A property of a collection of events that indicates that their union equals the sample space.

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

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

Gaussian distribution
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications