 2.4.1BSC: Bar Chart and Pareto Chart A bar chart and a Pareto chart both use ...
 2.4.2BSC: Scatterplot What is a scatterplot? What type of data is required fo...
 2.4.1CRE: In Exercises, refer to the below table, which summarizes results fr...
 2.4.1RE: ?Frequency Distribution of Brain Volumes Construct a frequency dist...
 2.4.2CQQ: ?Using the same first two classes from Exercise 1, identify the cla...
 2.4.2CRE: In Exercises, refer to the below table, which summarizes results fr...
 2.4.2RE: ?Histogram of Brain Volumes Construct the histogram that correspond...
 2.4.3BSC: ?SAT Scores Listed below are SAT scores from a sample of students (...
 2.4.3CQQ: ?The first class described in Exercise 1 has a frequency of 51. If ...
 2.4.3CRE: 3CRE: In Exercises, refer to the below table, which summarizes resu...
 2.4.3RE: 3RE: Dotplot of California Lottery  In the California Daily 4 lott...
 2.4.4BSC: ?SAT Scores Given that the data in Exercise 3 were obtained from st...
 2.4.4CQQ: A stemplot is created from the intervals (min) between eruptions of...
 2.4.4CRE: ?In Exercises 1–5, refer to the table below, which summarizes resul...
 2.4.4RE: ?Stemplot of IQ Scores Listed below are the first eight IQ scores f...
 2.4.5BSC: ?Scatterplots. In Exercises 5–8, use the given paired data from App...
 2.4.5CQQ: In the California Daily 4 lottery, four digits between 0 and 9 incl...
 2.4.5CRE: In Exercises, refer to the below table, which summarizes results fr...
 2.4.5RE: ?CO Emissions Listed below are the amounts (million metric tons) of...
 2.4.6BSC: ?Scatterplots. In Exercises 5–8, use the given paired data from App...
 2.4.6CQQ: In an investigation of the travel costs of college students, which ...
 2.4.6CRE: Grooming Time Listed below are times (minutes) spent on hygiene and...
 2.4.6RE: ?CO and NO Emissions Exercise 5 lists the amounts of carbon monoxid...
 2.4.7BSC: ?Scatterplots. In Exercises 5–8, use the given paired data from App...
 2.4.7CQQ: In an investigation of the relationship between SAT scores and grad...
 2.4.7CRE: ?Histogram of Grooming Times Use the frequency distribution from Ex...
 2.4.7RE: Sports Equipment According to USA Today, the largest categories of ...
 2.4.8BSC: ?Scatterplots. In Exercises 5–8, use the given paired data from App...
 2.4.8CQQ: As a quality control manager at Sony, you find that defective CDs h...
 2.4.8CRE: ?Stemplot of Grooming Times Use the data from Exercise 6 to constru...
 2.4.9BSC: ?TimeSeries Graphs. In Exercises 9 and 10, construct the timeseri...
 2.4.9CQQ: What characteristic of a data set can be better understood by const...
 2.4.10BSC: ?TimeSeries Graphs. In Exercises 9 and 10, construct the timeseri...
 2.4.10CQQ: ?A histogram is to be constructed from the brain sizes listed in Da...
 2.4.11BSC: ?Dotplots. In Exercises 11 and 12, construct the dotplot.Coke Volum...
 2.4.12BSC: ?Dotplots. In Exercises 11 and 12, construct the dotplot.Car Pollut...
 2.4.13BSC: ?Stemplots. In Exercises 13 and 14, construct the stemplot.Car Cras...
 2.4.14BSC: ?Stemplots. In Exercises 13 and 14, construct the stemplot.Car Brak...
 2.4.15BSC: Pareto Charts. In Exercises construct the Pareto chart.Awful Sounds...
 2.4.16BSC: Pareto Charts. In Exercises construct the Pareto chart.School Day H...
 2.4.17BSC: Pie Charts. In Exercises construct the pie chart.Awful Sounds In a ...
 2.4.18BSC: Pie Charts. In Exercises construct the pie chart.School Day Here ar...
 2.4.19BSC: ?BacktoBack Relative Frequency Histograms When using histograms...
 2.4.20BSC: ?Interpreting a Histogram Refer to the histogram given for Exercise...
 2.4.21BSC: ?Deceptive Graphs. In Exercises 21–24, identify the characteristic ...
 2.4.22BSC: ?Deceptive Graphs. In Exercises 21–24, identify the characteristic ...
 2.4.23BSC: ?Deceptive Graphs. In Exercises 21–24, identify the characteristic ...
 2.4.24BSC: ?Deceptive Graphs. In Exercises 21–24, identify the characteristic ...
 2.4.25BB: ?BacktoBack Stemplots Exercise 19 in Section 23 used backto...
 2.4.26BB: ?Expanded and Condensed Stemplotsa. A stemplot can be expanded by s...
Solutions for Chapter 2.4: Elementary Statistics 12th Edition
Full solutions for Elementary Statistics  12th Edition
ISBN: 9780321836960
Solutions for Chapter 2.4
Get Full SolutionsElementary Statistics was written by and is associated to the ISBN: 9780321836960. Since 50 problems in chapter 2.4 have been answered, more than 441754 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Elementary Statistics, edition: 12. Chapter 2.4 includes 50 full stepbystep solutions.

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

Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chisquare with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chisquare random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chisquare random variables.

Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

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.

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

Causal variable
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

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

Consistent estimator
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

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

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

Critical region
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Degrees of freedom.
The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

Design matrix
A matrix that provides the tests that are to be conducted in an experiment.

Eficiency
A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

Fraction defective control chart
See P chart

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

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 .

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 .