 6.3.1: Consider a standard normal random variable withm 0 and standard dev...
 6.3.2: Find these probabilities associated with the standardnormal random ...
 6.3.3: Calculate the area under the standard normalcurve to the left of th...
 6.3.4: Calculate the area under the standard normalcurve between these val...
 6.3.5: Find the following probabilities for the standardnormal random vari...
 6.3.6: Find these probabilities for the standard normalrandom variable z:a...
 6.3.7: a. Find a z0 such that P(z z0) .025.b. Find a z0 such that P(z z0) ...
 6.3.8: Find a z0 such that P(z0 z z0) .8262.
 6.3.9: a. Find a z0 that has area .9505 to its left.b. Find a z0 that has ...
 6.3.10: a. Find a z0 such that P(z0 z z0) .90.b. Find a z0 such that P(z0 z...
 6.3.11: Find the following percentiles for the standardnormal random variab...
 6.3.12: A normal random variable x has mean m 10and standard deviation s 2....
 6.3.13: A normal random variable x has mean m 1.2and standard deviation s ....
 6.3.14: A normal random variable x has an unknownmean m and standard deviat...
 6.3.15: A normal random variable x has mean 35 andstandard deviation 10. Fi...
 6.3.16: A normal random variable x has mean 50 andstandard deviation 15. Wo...
 6.3.17: A normal random variable x has an unknownmean and standard deviatio...
 6.3.18: Hamburger Meat The meat department at alocal supermarket specifical...
 6.3.19: Human Heights Human heights are one ofmany biological random variab...
 6.3.20: Christmas Trees The diameters of Douglasfirs grown at a Christmas t...
 6.3.21: Cerebral Blood Flow Cerebral blood flow(CBF) in the brains of healt...
 6.3.22: Braking Distances For a car traveling30 miles per hour (mph), the d...
 6.3.23: Elevator Capacities Suppose that you mustestablish regulations conc...
 6.3.24: A Phosphate Mine The discharge of suspendedsolids from a phosphate ...
 6.3.25: Sunflowers An experimenter publishing in theAnnals of Botany invest...
 6.3.26: Breathing Rates The number of times x anadult human breathes per mi...
 6.3.27: Economic Forecasts One method of arrivingat economic forecasts is t...
 6.3.28: Tax Audits How does the IRS decide on thepercentage of income tax r...
 6.3.29: Bacteria in Drinking Water Suppose thenumbers of a particular type ...
 6.3.30: Loading Grain A grain loader can be set todischarge grain in amount...
 6.3.31: How Many Words? A publisher has discoveredthat the number of words ...
 6.3.32: Tennis Anyone? A stringer of tennis racketshas found that the actua...
 6.3.33: Mall Rats An article in American Demographicsclaims that more than ...
 6.3.34: Pulse Rates Whats a normal pulse rate? Thatdepends on a variety of ...
Solutions for Chapter 6.3: Tabulated Areas of the Normal Probability Distribution
Full solutions for Introduction to Probability and Statistics 1  14th Edition
ISBN: 9781133103752
Solutions for Chapter 6.3: Tabulated Areas of the Normal Probability Distribution
Get Full SolutionsThis textbook survival guide was created for the textbook: Introduction to Probability and Statistics 1, edition: 14. This expansive textbook survival guide covers the following chapters and their solutions. Since 34 problems in chapter 6.3: Tabulated Areas of the Normal Probability Distribution have been answered, more than 9188 students have viewed full stepbystep solutions from this chapter. Chapter 6.3: Tabulated Areas of the Normal Probability Distribution includes 34 full stepbystep solutions. Introduction to Probability and Statistics 1 was written by and is associated to the ISBN: 9781133103752.

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

Analysis of variance (ANOVA)
A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

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.

Backward elimination
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

Bivariate distribution
The joint probability distribution of two random variables.

Causeandeffect diagram
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

Comparative experiment
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

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

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

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

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 .

Correlation coeficient
A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).

Critical value(s)
The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

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.

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

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 random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.