 6.1.1E: What are the characteristics of a normal distribution?
 6.1.2E: Why is the standard normal distribution important in statistical an...
 6.1.3E: What is the total area under the standard normal distribution curve?
 6.1.4E: What percentage of the area falls below the mean?
 6.1.5E: About what percentage of the area under the normal distribution cur...
 6.1.8E: Find the area under the standard normal distribution curve.Between ...
 6.1.10E: Find the area under the standard normal distribution curve.Between ...
 6.1.11E: Find the area under the standard normal distribution curve.To the r...
 6.1.12E: Find the area under the standard normal distribution curve.To the r...
 6.1.13E: Find the area under the standard normal distribution curve.To the l...
 6.1.14E: Find the area under the standard normal distribution curve.To the l...
 6.1.16E: Find the area under the standard normal distribution curve.Between ...
 6.1.17E: Find the area under the standard normal distribution curve.Between ...
 6.1.18E: Find the area under the standard normal distribution curve.Between ...
 6.1.19E: Find the area under the standard normal distribution curve.Between ...
 6.1.20E: Find the area under the standard normal distribution curve.Between ...
 6.1.22E: Find the area under the standard normal distribution curve.To the l...
 6.1.24E: Find the area under the standard normal distribution curve.To the r...
 6.1.25E: Find the area under the standard normal distribution curve.To the r...
 6.1.26E: Find the area under the standard normal distribution curve.To the l...
 6.1.28E: Find the probabilities for each, using the standard normal distribu...
 6.1.29E: Find the probabilities for each, using the standard normal distribu...
 6.1.30E: Find the probabilities for each, using the standard normal distribu...
 6.1.32E: Find the probabilities for each, using the standard normal distribu...
 6.1.34E: Find the probabilities for each, using the standard normal distribu...
 6.1.36E: Find the probabilities for each, using the standard normal distribu...
 6.1.38E: Find the probabilities for each, using the standard normal distribu...
 6.1.39E: Find the probabilities for each, using the standard normal distribu...
 6.1.40E: Find the probabilities for each, using the standard normal distribu...
 6.1.41E: Find the z value that corresponds to the given area.
 6.1.42E: Find the z value that corresponds to the given area.
 6.1.43E: Find the z value that corresponds to the given area.
 6.1.44E: Find the z value that corresponds to the given area.
 6.1.45E: Find the z value that corresponds to the given area.
 6.1.46E: Find the z value that corresponds to the given area.
 6.1.47E: Find the z value to the left ot the mean so thata. 98.87% of the ar...
 6.1.48E: Find the z value to the right of the mean so thata. 54.78% of the a...
 6.1.49E: Find two z values, one positive and one negative, that are equidist...
 6.1.50E: Find two z values so that 48% of the middle area is bounded by them.
 6.1.51EC: Find P( ? 1 < z < 1), P(?2 < z < 2), and P(?3 < z < 3). How do thes...
 6.1.52EC: In the standard normal distribution, find the values of z for the 7...
 6.1.53EC: Find z0 such that P(?1.2 < z < z0) = 0.8671.
 6.1.54EC: Find z0such that P(z0 < z < 2.5) = 0.7672.
 6.1.55EC: Find z0 such that the area between and z0 and z = ?0.5 is 0.2345 (t...
 6.1.56EC: Find z0 such that P(?z0< < z0) = 0.76.
 6.1.57EC: Find the equation for the standard normal distribution by substitut...
 6.1.58EC: Graph by hand the standard normal distribution by using the formula...
Solutions for Chapter 6.1: Elementary Statistics: A Step By Step Approach 9th Edition
Full solutions for Elementary Statistics: A Step By Step Approach  9th Edition
ISBN: 9780073534985
Solutions for Chapter 6.1
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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

Analytic study
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

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

Block
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

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

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.

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.

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.

Conidence interval
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

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

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

Correlation matrix
A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the offdiagonal elements rij are the correlations between Xi and Xj .

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

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.

Empirical model
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.

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.

Gamma function
A function used in the probability density function of a gamma random variable that can be considered to extend factorials