 1.1: ?In 1–5, provide a definition using your own words.1. Statistics
 1.2: ?In 1–5, provide a definition using your own words.2. Population
 1.3: ?In 1–5, provide a definition using your own words.3. Sample
 1.4: ?In 1–5, provide a definition using your own words.4. Observational...
 1.5: ?In 1–5, provide a definition using your own words.5. Designed expe...
 1.6: ?List and describe the three major types of observational studies.
 1.7: ?What is meant by the process of statistics?
 1.8: ?List and explain the three sources of bias in sampling. Provide so...
 1.9: ?Distinguish between sampling and nonsampling error.
 1.10: ?Explain the steps in designing an experiment.
 1.11: ?In 11–13, classify the variable as qualitative or quantitative. If...
 1.12: ?In 11–13, classify the variable as qualitative or quantitative. If...
 1.13: ?In 11–13, classify the variable as qualitative or quantitative. If...
 1.14: ?In 14 and 15, determine whether the underlined value is a paramete...
 1.15: ?In 14 and 15, determine whether the underlined value is a paramete...
 1.16: ?In 16–19, determine the level of measurement of each variable.16. ...
 1.17: ?In 16–19, determine the level of measurement of each variable.17. ...
 1.18: ?In 16–19, determine the level of measurement of each variable.18. ...
 1.19: ?In 16–19, determine the level of measurement of each variable.19. ...
 1.20: ?In 20 and 21, determine whether the study depicts an observational...
 1.21: ?In 20 and 21, determine whether the study depicts an observational...
 1.22: ?Read the following description of an observational study and deter...
 1.23: ?In 23–26, determine the type of sampling used.23. On election day,...
 1.24: ?In 23–26, determine the type of sampling used.24. An Internet serv...
 1.25: ?In 23–26, determine the type of sampling used.25. Thirtyfive soph...
 1.26: ?In 23–26, determine the type of sampling used.26. Officers for the...
 1.27: ?Each of the following surveys has bias. Determine the type of bias...
 1.28: ?Obtaining a Simple Random Sample The mayor of a town wants to cond...
 1.29: ?Obtaining a Systematic Sample A qualitycontrol engineer wants to ...
 1.30: ?Obtaining a Simple Random Sample Based on the Military Standard 10...
 1.31: ?ToothWhitening Gum Smoking and drinking coffee have a tendency to...
 1.32: ?Reaction Time Researchers wanted to assess the effect of low alcoh...
 1.33: ?Exam Grades A statistics instructor wants to see if allowing stude...
 1.34: ?Multiple Choice A common tip for taking multiplechoice tests is t...
 1.35: ?Humor in Advertising A marketing research firm wants to know wheth...
 1.36: ?Describe what is meant by a matchedpairs design. Contrast this ex...
 1.37: ?Internet Search Go to an online science magazine such as Science D...
 1.38: ?Cell Phones Many newspaper articles discuss the dangers of teens t...
 1.39: ?What is the role of randomization in a designed experiment? If you...
 1.1: ?List the four components that comprise the definition of statistics.
 1.2: ?What is meant by the process of statistics?
 1.3: ?In 3–5, determine if the variable is qualitative or quantitative. ...
 1.4: ?In 3–5, determine if the variable is qualitative or quantitative. ...
 1.5: ?In 3–5, determine if the variable is qualitative or quantitative. ...
 1.6: ?In 6 and 7, determine whether the study depicts an observational s...
 1.7: ?In 6 and 7, determine whether the study depicts an observational s...
 1.8: ?Contrast the three major types of observational studies in terms o...
 1.9: ?Compare and contrast observational studies and designed experiment...
 1.10: ?Explain why it is important to use a control group and blinding in...
 1.11: ?List the steps required to conduct an experiment.
 1.12: ?A cellular phone company is looking for ways to improve customer s...
 1.13: ?A congresswoman wants to survey her constituency regarding public ...
 1.14: ?A farmer has a 500acre orchard in Florida. Each acre is subdivide...
 1.15: ?A casino manager wants to inspect a sample of 14 slot machines in ...
 1.16: ?Describe what is meant by an experiment that has a completely rand...
 1.17: ?Each of the following surveys has bias. Identify the type of bias....
 1.18: ?Shapely Glasses Does the shape of a glass play a role in determini...
 1.19: ?Nucryst Pharmaceuticals, Inc., announced the results of its first ...
 1.20: ?Researchers Katherine Tucker and associates wanted to determine wh...
 1.21: ?Explain the difference between a lurking variable and a confoundin...
 1.1: ?Chrysalises for Cash The butterfly symbolizes the notion of person...
Solutions for Chapter 1: Data Collection
Full solutions for Statistics: Informed Decisions Using Data  5th Edition
ISBN: 9780134133539
Solutions for Chapter 1: Data Collection
Get Full SolutionsSummary of Chapter 1: Data Collection
Introduction to the practice of Statistics. Understand the difference between observational studies versus designed experiments. Explain simple random sampling. Discuss other effective sampling methods. Discuss the bias in sampling. Explain the design of experiments
Chapter 1: Data Collection includes 61 full stepbystep solutions. Statistics: Informed Decisions Using Data was written by and is associated to the ISBN: 9780134133539. This textbook survival guide was created for the textbook: Statistics: Informed Decisions Using Data, edition: 5. This expansive textbook survival guide covers the following chapters and their solutions. Since 61 problems in chapter 1: Data Collection have been answered, more than 29313 students have viewed full stepbystep solutions from this chapter.

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

Alternative hypothesis
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

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

Bimodal distribution.
A distribution with two modes

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

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).

Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

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

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.

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

Decision interval
A parameter in a tabular CUSUM algorithm that is determined from a tradeoff between false alarms and the detection of assignable causes.

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.

Deming
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

Dispersion
The amount of variability exhibited by data

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.

Error variance
The variance of an error term or component in a model.

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating function