 Chapter 4.1.4AYU: What does it mean to say two variables are positively associated? N...
 Chapter 4.1.5AYU: If r = _______, then a perfect negative linear relation exists betw...
 Chapter 4.1.6AYU: True or False: If the linear correlation coefficient is close to 0,...
 Chapter 4.1.7AYU: A _______ variable is a variable that is related to both the explan...
 Chapter 4.1.8AYU: True or False: Correlation implies causation.
 Chapter 4.1.9AYU: In determine whether the scatter diagram indicates that a linear re...
 Chapter 4.1.10AYU: In determine whether the scatter diagram indicates that a linear re...
 Chapter 4.1.11AYU: In determine whether the scatter diagram indicates that a linear re...
 Chapter 4.1.12AYU: In determine whether the scatter diagram indicates that a linear re...
 Chapter 4.1.13AYU: Match the linear correlation coefficient to the scatter diagrams. T...
 Chapter 4.1.14AYU: Match the linear correlation coefficient to the scatter diagram. Th...
 Chapter 4.1.15AYU: Does Education Pay?The scatter diagram drawn in MINITAB shows the r...
 Chapter 4.1.16AYU: Relation between Income and Birthrate?The following scatter diagram...
 Chapter 4.1.17AYU: In (a) draw a scatter diagram of the data,(b) by hand, compute the ...
 Chapter 4.1.18AYU: In (a) draw a scatter diagram of the data,(b) by hand, compute the ...
 Chapter 4.1.19AYU: In (a) draw a scatter diagram of the data,(b) by hand, compute the ...
 Chapter 4.1.20AYU: In (a) draw a scatter diagram of the data,(b) by hand, compute the ...
 Chapter 4.1.21AYU: Name the Relation, Part IFor each of the following statements, expl...
 Chapter 4.1.22AYU: Name the Relation, Part IIFor each of the following statements, exp...
 Chapter 4.1.25AYU: An Unhealthy CommuteThe Gallup Organization regularly surveys adult...
 Chapter 4.1.26AYU: Credit Scores Your Fair Isaacs Corporation (FICO) credit score is u...
 Chapter 4.1.27AYU: Height versus Head Circumference A pediatrician wants to determine ...
 Chapter 4.1.28AYU: American Black Bears The American black bear (Ursus americanus) is ...
 Chapter 4.1.29AYU: Weight of a Car versus Miles per Gallon An engineer wanted to deter...
 Chapter 4.1.31AYU: CEO Performance The following data represent the total compensation...
 Chapter 4.1.32AYU: Age versus HDL Cholesterol A doctor wanted to determine whether a r...
 Chapter 4.1.34AYU: Male versus Female Drivers The following data represent the number ...
 Chapter 4.1.36AYU: American Black Bears The Web site that contained the American black...
 Chapter 4.1.37AYU: Draw Your Data!Consider the four data sets shown below.Data Set 1Da...
 Chapter 4.1.38AYU: The Best Predictor of the Winning Percentage The ultimate goal in a...
 Chapter 4.1.39AYU: Diversification One basic theory of investing is diversification. T...
 Chapter 4.1.40AYU: Lyme Disease versus Drownings Lyme disease is an inflammatory disea...
 Chapter 4.1.41AYU: Television Stations and Life Expectancy Based on data obtained from...
 Chapter 4.1.42AYU: Obesity In a study published in the Journal of the American Medical...
 Chapter 4.1.43AYU: Crime Rate and Cell PhonesThe linear correlation between violent cr...
 Chapter 4.1.44AYU: Faulty Use of CorrelationOn the basis of the scatter diagram, expla...
 Chapter 4.1.45AYU: Influential Consider the following set of data:x2.23.73.94.12.64.12...
 Chapter 4.1.47AYU: RateMyProfessors.com Professors Theodore Coladarci and Irv Kornfiel...
 Chapter 4.1.48AYU: What does it mean to say that the linear correlation coefficient be...
 Chapter 4.1.49AYU: What does it mean if r = 0?
 Chapter 4.1.50AYU: Explain what is wrong with the following statement: “We have conclu...
 Chapter 4.1.51AYU: Write a paragraph that explains the concept of correlation. Include...
 Chapter 4.1.52AYU: Explain the difference between correlation and causation. When is i...
 Chapter 4.1.53AYU: Draw a scatter diagram that might represent the relation between th...
 Chapter 4.1.54AYU: Suppose you work a parttime job and earn $15 per hour.Draw a scatt...
 Chapter 4.1.55AYU: Suppose that two variables, X and Y, are negatively associated. Doe...
 Chapter 4.1.46AYU: Transformations Consider the following data set:x56778888y4.255.25....
 Chapter 4.1.23AYU: The TIMMS ExamThe Trends in International Mathematics and Science (...
 Chapter 4.1.1AYU: What is the difference between univariate data and bivariate data?
 Chapter 4.1.2AYU: The _______ variable is the variable whose value can be explained b...
 Chapter 4.1.3AYU: A _______ _______ is a graph that shows the relation between two qu...
Solutions for Chapter Chapter 4.1: Fundamentals of Statistics 4th Edition
Full solutions for Fundamentals of Statistics  4th Edition
ISBN: 9780321838704
Solutions for Chapter Chapter 4.1
Get Full SolutionsThis textbook survival guide was created for the textbook: Fundamentals of Statistics, edition: 4. This expansive textbook survival guide covers the following chapters and their solutions. Since 51 problems in chapter Chapter 4.1 have been answered, more than 311212 students have viewed full stepbystep solutions from this chapter. Chapter Chapter 4.1 includes 51 full stepbystep solutions. Fundamentals of Statistics was written by and is associated to the ISBN: 9780321838704.

aerror (or arisk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

Alias
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

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.

Bayes’ theorem
An equation for a conditional probability such as PA B (  ) in terms of the reverse conditional probability PB A (  ).

Bias
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

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.

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

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.

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.

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 .

Counting techniques
Formulas used to determine the number of elements in sample spaces and events.

Covariance matrix
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

Crossed factors
Another name for factors that are arranged in a factorial experiment.

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

Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.

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

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

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 .