 2.2.8 BSC: ?In Exercises 5–10, identify the class width, class midpoints, and ...
 2.2.29BSC: Categorical Data. Use the given categorical data to construct the r...
 2.2.25BSC: ?Constructing Frequency Distributions. In Exercises 19–28, use the ...
 2.2.30BSC: ?Categorical Data. In Exercises 29–32, use the given categorical da...
 2.2.2BSC: Relative Frequency Distribution After construction of a relative fr...
 2.2.32BSC: Categorical Data. In Exercise, use the given categorical data to co...
 2.2.33BB: ?Interpreting Effects of Outliers Refer to Data Set 22 in Appendix ...
 2.2.34BB: Number of Classes According to what is known as Sturges’ guideline,...
 2.2.26BSC: ?Constructing Frequency Distributions. In Exercises 19–28, use the ...
 2.2.27BSC: ?Constructing Frequency Distributions. In Exercises 19–28, use the ...
 2.2.28BSC: ?Constructing Frequency Distributions. In Exercises 19–28, use the ...
 2.2.1BSC: ?Frequency Distribution Table 22 on page 45 is a frequency distri...
 2.2.3BSC: ?Do You Believe? In a Harris Interactive survey, 2303 adults were a...
 2.2.4BSC: ?Analyzing a Frequency Distribution The accompanying frequency dist...
 2.2.5BSC: ?In Exercises 5–10, identify the class width, class midpoints, and ...
 2.2.6BSC: ?Identify the class width, class midpoints, and class boundaries fo...
 2.2.7BSC: ?Identify the class width, class midpoints, and class boundaries fo...
 2.2.10BSC: ?In Exercises 5–10, identify the class width, class midpoints, and ...
 2.2.11BSC: ?Normal Distributions. In Exercises 11–14, answer the given questio...
 2.2.12BSC: ?Normal Distributions. In Exercises 11–14, answer the given questio...
 2.2.13BSC: ?Normal Distributions. In Exercises 11–14, answer the given questio...
 2.2.14BSC: ?Normal Distributions. In Exercises 11–14, answer the given questio...
 2.2.15BSC: ?Relative Frequencies for Comparisons. In Exercises 15 and 16, cons...
 2.2.16BSC: ?Relative Frequencies for Comparisons. In Exercises 15 and 16, cons...
 2.2.17BSC: ?Cumulative Frequency Distributions. In Exercises 17 and 18, constr...
 2.2.18BSC: ?Cumulative Frequency Distributions. In Exercises 17 and 18, constr...
 2.2.19BSC: Constructing Frequency Distributions. Use the indicated data and co...
 2.2.20BSC: ?Weights of respondents were recorded as part of the California Hea...
 2.2.21BSC: ?Pulse Rates of Males Refer to Data Set 1 in Appendix B and use the...
 2.2.22BSC: Frequency Distributions.? I ? n E? ercise, ?use the indicated data ...
 2.2.24BSC: ?Constructing Frequency Distributions. In Exercises 19–28, use the ...
 2.2.23BSC: ?Constructing Frequency Distributions. In Exercises 19–28, use the ...
Solutions for Chapter 2.2: Frequency Distributions
Full solutions for Elementary Statistics  12th Edition
ISBN: 9780321836960
Solutions for Chapter 2.2: Frequency Distributions
Get Full SolutionsSummary of Chapter 2.2: Frequency Distributions
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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.

Bivariate normal distribution
The joint distribution of two normal random variables

Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

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

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.

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

Cumulative distribution function
For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.

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

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.

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.

Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

Density function
Another name for a probability density function

Dispersion
The amount of variability exhibited by data

Error propagation
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model

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

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.