Statistics by Learning Objective 0 edition

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Cengage Learning
Publisher: Cengage Learning

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  • Chapter 1: Concepts of Statistics
    • 1.101: 1A Distinguish the difference between descriptive and inferential statistics. (10)
    • 1.201: 2A Given a study scenario, identify the population and sample. (12)
    • 1.301: 3A Given a description of a data set, distinguish between categorical and numerical data. (12)
    • 1.302: 3B Given a data set or description of a study, distinguish between discrete and continuous variables. (12)
    • 1.401: 4A Given a study scenario, distinguish between parameters and statistics. (12)
    • 1.501: 5A Given descriptions of different variables, identify the level of measurement for each (nominal, ordinal, interval, or ratio). (12)
    • 1.502: 5B Given a study scenario, identify if the data set is univariate, bivariate, or multivariate. (10)
    • 1: Test Bank (54)

  • Chapter 2: Experiments and Types of Studies
    • 2.101: 1A Given a study scenario, determine if it is an observational study or an experiment. (12)
    • 2.102: 1B Given a study scenario, determine the conclusions one can make from an observational study or experiment. (12)
    • 2.103: 1C Given a study scenario, identify the independent and dependent variable(s). (10)
    • 2.104: 1D Given a study scenario, determine whether it includes confounding variables. (6)
    • 2.105: 1E Distinguish between retrospective, cross-sectional, and longitudinal studies. (10)
    • 2.201: 2A Given a study scenario, identify the components of an experiment. (6)
    • 2.202: 2B Explain the advantages of using a control group or placebo in an experimental study. (6)
    • 2.203: 2C Explain the advantages of using single-blind and double-blind studies. (10)
    • 2.301: 3A Given a study scenario, determine whether it uses a completely randomized design. (6)
    • 2.302: 3B Given a study scenario, determine whether it uses a block design. (6)
    • 2.303: 3C Explain the advantages of using a block design for a study. (6)
    • 2.304: 3D Given a study scenario, determine whether it uses repeated measures. (6)
    • 2.305: 3E Explain the advantages of using repeated measures for a study. (6)
    • 2: Test Bank (38)

  • Chapter 3: Sampling Methods
    • 3.101: 1A Construct a simple random sample using random numbers. (12)
    • 3.102: 1B Explain how random sampling could be implemented within a study. (8)
    • 3.201: 2A Explain how stratified random sampling could be implemented within a study. (10)
    • 3.202: 2B Explain the advantages and disadvantages of stratified random sampling. (6)
    • 3.301: 3A Explain how systematic sampling could be implemented for a study. (10)
    • 3.302: 3B Explain the advantages and disadvantages of systematic sampling. (8)
    • 3.401: 4A Given a study scenario, determine whether a convenience sample has been used. (12)
    • 3.402: 4B Explain the advantages and disadvantages of convenience sampling. (8)
    • 3.501: 5A Describe the different types of bias that can occur in a study. (10)
    • 3.502: 5B Explain how bias can affect the data collected for a study and any conclusions to be drawn from that study. (9)
    • 3: Labs (6)
    • 3: Test Bank (16)

  • Chapter 4: Numerical Measures
    • 4: Stats in Practice Video Question (1)
    • 4.101: 1A Given a data set, calculate the mean, median, and mode. (12)
    • 4.102: 1B Explain which measures of central tendency are appropriate for numerical and categorical data. (7)
    • 4.103: 1C Given a data set, calculate the weighted mean. (10)
    • 4.201: 2A Given a data set, calculate the quartiles. (12)
    • 4.202: 2B Given a data set, calculate the percentiles. (12)
    • 4.301: 3A Given a data set, calculate the variance and standard deviation. (12)
    • 4.302: 3B Given a data set, calculate the interquartile range. (12)
    • 4.303: 3C Given a data set, determine if a specific value is an outlier, and identify what kind of an outlier it is. (12)
    • 4.304: 3D Given the sample mean and sample standard deviation for a data set, use Chebyshev's Theorem to calculate the percentage of all data values that lie within an interval. (12)
    • 4.305: 3E Use the coefficient of variation to compare variation spread across different data sets. (10)
    • 4: JMP Simulations (11)
    • 4: Test Bank (211)

  • Chapter 5: Tabular Representations
    • 5.101: 1A Construct a frequency table from numerical data. (12)
    • 5.102: 1B Construct a frequency table from categorical data. (8)
    • 5.103: 1C Calculate marginal frequencies and percentages for data presented as a two-way frequency table. (10)
    • 5: Test Bank (34)

  • Chapter 6: Graphical Representations
    • 6: Stats in Practice Video Question (1)
    • 6.101: 1A Given a data distribution, identify its shape. (8)
    • 6.201: 2A Construct and interpret a bar chart from categorical data. (8)
    • 6.202: 2B Given a data set with two groups, construct and interpret a comparative bar chart from discrete or categorical data to compare the groups. (5)
    • 6.203: 2C Construct and interpret a segmented bar chart. (6)
    • 6.204: 2D Construct and interpret a pie chart from categorical data. (8)
    • 6.301: 3A Construct and interpret a stem-and-leaf plot. (8)
    • 6.302: 3B Given a data set with two groups, construct and interpret a comparative stem-and-leaf plot to compare the groups. (6)
    • 6.303: 3C Construct and interpret a dotplot. (8)
    • 6.304: 3D Given a data set with two groups, construct and interpret a comparative dotplot to compare the groups. (8)
    • 6.305: 3E Construct and interpret a histogram from numerical data. (8)
    • 6.306: 3F Given a data set with two groups, construct and interpret a histogram to compare the groups. (6)
    • 6.307: 3G Construct and interpret a relative cumulative frequency plot from continuous data. (6)
    • 6.308: 3H Construct and interpret a boxplot. (8)
    • 6.309: 3I Construct and interpret a time series plot. (6)
    • 6: JMP Simulations (9)
    • 6: Labs (5)
    • 6: Test Bank (75)

  • Chapter 7: Concepts of Probability
    • 7: Stats in Practice Video Question (1)
    • 7.101: 1A Calculate the probability of an event using a relative frequency table. (12)
    • 7.102: 1B Calculate the probability of an event when outcomes are equally likely. (12)
    • 7.103: 1C Given a description of an experiment, define its sample space. (11)
    • 7.104: 1D Calculate the probability of an event using a sample space. (12)
    • 7.105: 1E Calculate the probability of a complement of an event. (12)
    • 7.106: 1F Calculate the probability of a union of two or more events. (12)
    • 7.107: 1G Calculate the probability of an intersection of two events. (12)
    • 7.108: 1H Calculate the probability of two or more mutually exclusive events. (12)
    • 7.109: 1I Determine if two events are independent. (12)
    • 7.110: 1J Calculate the probability of two or more independent events using the Multiplication Rule. (12)
    • 7.111: 1K Calculate the probability of an event using a simulation technique. (8)
    • 7: Test Bank (9)

  • Chapter 8: Conditional Probability
    • 8.101: 1A Calculate the conditional probability of an event. (10)
    • 8.102: 1B Use Bayes' Rule and the Law of Total Probability to calculate a conditional probability. (6)
    • 8: Labs (5)

  • Chapter 9: Counting
    • 9.101: 1A Calculate the number of outcomes for an experiment by using the Multiplication Rule. (10)
    • 9.102: 1B Calculate the number of outcomes for an experiment by using combinations. (10)
    • 9.103: 1C Calculate the number of outcomes for an experiment by using permutations. (10)
    • 9.104: 1D Calculate the probability of an event using counting techniques. (10)
    • 9.105: 1E Recognize the difference in sampling with or without replacement and the implications of this distinction in calculating probabilities. (8)

  • Chapter 10: Discrete Probability Distributions and Binomial Distribution
    • 10: Stats in Practice Video Question (1)
    • 10.101: 1A Construct a probability distribution of a discrete random variable. (12)
    • 10.102: 1B Calculate probabilities for a discrete random variable given its probability distribution. (12)
    • 10.103: 1C Find the cumulative distribution function for a discrete random variable. (8)
    • 10.104: 1D Calculate the median of a discrete random variable. (12)
    • 10.105: 1E Calculate the expected value or mean of a discrete random variable. (11)
    • 10.106: 1F Calculate the expected value or mean of a function of a discrete random variable. (11)
    • 10.107: 1G Calculate the variance and standard deviation of a discrete random variable. (12)
    • 10.108: 1H Calculate the variance and standard deviation of a linear function of a discrete random variable. (10)
    • 10.109: 1I Calculate the mean of a linear combination of independent random variables. (10)
    • 10.110: 1J Calculate the variance and standard deviation of a linear combination of independent random variables. (10)
    • 10.201: 2A Determine if a random variable has a binomial distribution. (8)
    • 10.202: 2B Calculate the probability of a binomial random variable using the formula for the binomial distribution. (12)
    • 10.203: 2C Calculate the probability of a binomial random variable using technology or a table. (12)
    • 10.204: 2D Calculate the mean and standard deviation of a binomial random variable. (11)
    • 10: Labs (5)
    • 10: Test Bank (30)

  • Chapter 11: More Discrete Probability Distributions
    • 11.101: 1A Determine if a random variable has a geometric distribution. (6)
    • 11.102: 1B Calculate the probability of a geometric random variable using the formula for the geometric distribution. (10)
    • 11.103: 1C Calculate the mean and standard deviation of a geometric random variable. (10)
    • 11.201: 2A Determine if a random variable has a hypergeometric distribution. (6)
    • 11.202: 2B Calculate the probability of a hypergeometric random variable using the formula for the hypergeometric distribution. (10)
    • 11.203: 2C Given a small sample size, calculate the probability for a hypergeometric random variable using the binomial distribution formula. (8)
    • 11.204: 2D Calculate the mean and standard deviation of a hypergeometric random variable. (8)
    • 11.301: 3A Determine if a random variable has a Poisson distribution. (6)
    • 11.302: 3B Calculate the probability of a Poisson random variable using the formula for the Poisson distribution. (10)
    • 11.303: 3C Calculate the mean and standard deviation of a Poisson random variable. (6)
    • 11.304: 3D Use the Poisson distribution to approximate the probability of a binomial random variable. (10)
    • 11.401: 4A Determine if a set of random variables has a multinomial distribution. (6)
    • 11.402: 4B Calculate the probability of a set of multinomial random variables using the formula for the multinomial distribution. (10)

  • Chapter 12: Continuous Probability Distribution and Normal Distribution
    • 12.101: 1A Determine if a function is a probability distribution of a continuous random variable. (8)
    • 12.102: 1B Calculate the probability of a continuous random variable given its probability density curve. (6)
    • 12.201: 2A Calculate the z-score for a normal random variable, given its mean and standard deviation. (12)
    • 12.202: 2B Calculate the probability of a standard normal random variable using the Empirical Rule. (12)
    • 12.203: 2C Calculate the probability of a normal random variable using the Empirical Rule. (12)
    • 12.204: 2D Calculate the probability of a standard normal random variable using technology or a table. (12)
    • 12.205: 2E Calculate probabilities for a normal random variable using technology or a table. (12)
    • 12.206: 2F Find percentiles for a standard normal random variable using technology or a table. (12)
    • 12.207: 2G Find percentiles for a normal random variable using technology or a table. (12)
    • 12.208: 2H Find the value of a standard normal random variable given a probability using technology or a table. (12)
    • 12.209: 2I Find the value of a normal random variable given a probability using technology or a table. (12)
    • 12.210: 2J Determine whether data have a normal distribution using a histogram and normal probability plot. (6)
    • 12.211: 2K Determine whether transformed data have a normal distribution using a histogram and normal probability plot. (6)
    • 12.212: 2L Use the normal distribution to approximate the probability of a binomial random variable. (6)
    • 12: JMP Simulations (5)
    • 12: Labs (5)
    • 12: Test Bank (224)

  • Chapter 13: More Continuous Probability Distributions
    • 13.101: 1A Calculate the probability of a random variable with a t distribution using technology or a table. (12)
    • 13.102: 1B Find the value of a random variable with a t distribution given a probability using technology or a table. (12)
    • 13.201: 2A Calculate the probability of a random variable with a chi-square distribution using technology or a table. (10)
    • 13.202: 2B Find the value of a random variable with a chi-square distribution given a probability using technology or a table. (10)
    • 13.301: 3A Calculate the probability of a random variable with an F distribution using technology or a table. (8)
    • 13.401: 4A Find the probability of a uniform random variable. (10)
    • 13: Test Bank (51)

  • Chapter 14: Sampling Distributions
    • 14: Stats in Practice Video Question (1)
    • 14.101: 1A Define the sampling distribution of a sample mean for a normal random variable. (12)
    • 14.102: 1B Define the sampling distribution of a sample mean using the Central Limit Theorem. (12)
    • 14.103: 1C Calculate the probability of a sample mean, given a normal or approximately normal sampling distribution. (10)
    • 14.201: 2A Define the mean and standard deviation of the sampling distribution of a sample proportion. (10)
    • 14.202: 2B Determine if the sampling distribution of the sample proportion is approximately normal. (10)
    • 14.203: 2C Calculate the probability of a sample proportion, given a normal or approximately normal sampling distribution. (10)
    • 14.301: 3A Define the mean and standard deviation of the sampling distribution of a difference of two sample means. (10)
    • 14.302: 3B Define the mean and standard deviation of the sampling distribution of a mean difference for paired data. (10)
    • 14.303: 3C Define the mean and standard deviation of the sampling distribution of a difference of two sample proportions. (10)
    • 14: Test Bank (58)

  • Chapter 15: Estimation for One Sample
    • 15: Stats in Practice Video Question (1)
    • 15.101: 1A Define the point estimate and calculate its margin of error for estimating a population mean when σ is known. (12)
    • 15.102: 1B Calculate a confidence interval for a population mean when σ is known. (12)
    • 15.103: 1C Calculate the sample size for estimating a population mean when σ is known. (12)
    • 15.104: 1D Define the point estimate and calculate its margin of error for estimating a population mean when σ is unknown. (12)
    • 15.105: 1E Calculate a confidence interval for a population mean when σ is unknown. (12)
    • 15.106: 1F Calculate the sample size for estimating a population mean when σ is unknown. (12)
    • 15.201: 2A Define the point estimate and calculate its margin of error for estimating a population proportion. (12)
    • 15.202: 2B Calculate a confidence interval for a population proportion. (12)
    • 15.203: 2C Calculate the sample size for estimating a population proportion. (12)
    • 15.301: 3A Define the point estimate for estimating the population variance and population standard deviation. (10)
    • 15.302: 3B Calculate a confidence interval for the population variance and population standard deviation. (12)
    • 15: JMP Simulations (9)
    • 15: Labs (6)
    • 15: Test Bank (147)

  • Chapter 16: Estimation for Two Samples
    • 16.101: 1A Define the point estimate and calculate its margin of error for estimating for the difference of population means from two independent samples. (10)
    • 16.102: 1B Calculate a confidence interval for a difference of two population means. (10)
    • 16.201: 2A Define the point estimate and calculate its margin of error for estimating a population mean difference for paired data. (10)
    • 16.202: 2B Calculate a confidence interval for a population mean difference for paired data. (10)
    • 16.301: 3A Define the point estimate and calculate its margin of error for estimating a difference of two population proportions. (10)
    • 16.302: 3B Calculate a confidence interval for a difference of two population proportions. (10)
    • 16: JMP Simulations (12)
    • 16: Test Bank (48)

  • Chapter 17: Hypothesis Tests for One Sample
    • 17.101: 1A Given a study scenario involving a population mean, write the null and alternative hypotheses. (12)
    • 17.102: 1B Identify whether a test is upper-tailed, lower-tailed, or two-tailed given the null and alternative hypotheses for a study involving a population mean. (12)
    • 17.103: 1C Conduct a hypothesis test for a population mean using the normal distribution. (12)
    • 17.104: 1D Conduct a hypothesis test for a population mean using a Student's t distribution. (12)
    • 17.105: 1E Given a hypothesis test for a study involving a population mean, explain what constitutes Type I and Type II errors in this context. (12)
    • 17.106: 1F Determine the outcome of a hypothesis test for a population mean using an appropriate critical value. (12)
    • 17.107: 1G Calculate the power of a hypothesis test for a population mean. (12)
    • 17.108: 1H Discuss the connection between confidence intervals and hypothesis tests. (6)
    • 17.201: 2A Given a study scenario for the test of a population proportion, write the null and alternative hypotheses. (12)
    • 17.202: 2B Identify whether a test is upper-tailed, lower-tailed, or two-tailed given the null and alternative hypotheses for a study involving a population proportion. (12)
    • 17.203: 2C Conduct a hypothesis test for a population proportion. (9)
    • 17.204: 2D Given a hypothesis test for a study involving a population proportion, explain what constitutes Type I and Type II errors in this context. (10)
    • 17.301: 3A Given a study scenario for testing a population variance, write the null and alternative hypotheses. (12)
    • 17.302: 3B Conduct a hypothesis test for a population variance. (12)
    • 17: JMP Simulations (13)
    • 17: Labs (6)
    • 17: Test Bank (168)

  • Chapter 18: Hypothesis Tests for Two Samples
    • 18: Stats in Practice Video Question (1)
    • 18.101: 1A Given a study scenario for the test of the difference of two population means, write the null and alternative hypotheses. (12)
    • 18.102: 1B Conduct a hypothesis test for the difference of two population means. (10)
    • 18.201: 2A Given a study scenario for the test of a population mean difference using paired samples, write the null and alternative hypotheses. (12)
    • 18.202: 2B Conduct a hypothesis test for the population mean difference using paired samples. (10)
    • 18.301: 3A Given a study scenario for the test of the difference of two population proportions, write the null and alternative hypotheses. (12)
    • 18.302: 3B Conduct a hypothesis test for the difference of two population proportions. (10)
    • 18.401: 4A Given a study scenario for a test of two population variances, write the null and alternative hypotheses. (12)
    • 18.402: 4B Conduct a hypothesis test for the equality of two population variances. (10)
    • 18: JMP Simulations (12)
    • 18: Labs (6)
    • 18: Test Bank (145)

  • Chapter 19: Chi-Square Tests
    • 19: Stats in Practice Video Question (1)
    • 19.101: 1A Calculate the sample chi-square statistic using observed and expected frequencies. (10)
    • 19.102: 1B Conduct a goodness-of-fit test and decide if sample data are consistent with a given distribution. (10)
    • 19.201: 2A Conduct a chi-square test of homogeneity and decide if two or more populations are not homogeneous. (10)
    • 19.301: 3A Given data in a contingency table, calculate the sample chi-square statistic. (10)
    • 19.302: 3B Conduct a chi-square test of independence and decide if two random variables are not independent. (10)
    • 19: JMP Simulations (6)
    • 19: Labs (6)
    • 19: Test Bank (73)

  • Chapter 20: Correlation and Regression
    • 20: Stats in Practice Video Question (1)
    • 20.101: 1A Construct a scatterplot given bivariate data. (8)
    • 20.102: 1B Use a scatterplot to decide if a linear relationship exists between two variables. (8)
    • 20.201: 2A Calculate the correlation coefficient from sample data. (12)
    • 20.202: 2B Interpret a correlation coefficient calculated from sample data. (12)
    • 20.203: 2C Given a correlation coefficient calculated from sample data, determine if it is significant. (12)
    • 20.301: 3A Given sample data, calculate the least squares regression line. (12)
    • 20.302: 3B Use the least squares regression line to predict the value of a response variable. (12)
    • 20.303: 3C Use the coefficient of determination to interpret variation in the response variable. (8)
    • 20.304: 3D Calculate the prediction interval for predicting a single value for a response variable given a least squares regression line. (10)
    • 20.305: 3E Conduct a hypothesis test for the slope of the least squares regression line. (10)
    • 20.306: 3F Calculate the confidence interval for the slope of a least squares regression line. (10)
    • 20.307: 3G Use a residual plot to analyze the appropriateness of a linear regression model for a given data set. (10)
    • 20: JMP Simulations (14)
    • 20: Labs (6)
    • 20: Test Bank (153)

  • Chapter 21: Multiple Regression
    • 21.101: 1A Use a statistical calculator or computer program to develop a multiple regression model. (10)
    • 21.102: 1B Given a multiple regression model, determine if its coefficients are significant. (10)
    • 21.103: 1C Given a regression model, calculate the confidence interval for the mean value of a response. (6)
    • 21: Test Bank (26)

  • Chapter 22: One-Way Analysis of Variance
    • 22.101: 1A Given a study scenario for a one-way ANOVA, write the null and alternative hypotheses. (10)
    • 22.102: 1B Given sample data, calculate the between-groups and within-groups mean squares. (10)
    • 22.103: 1C Calculate the sample F statistic for a one-way ANOVA. (10)
    • 22.104: 1D Conduct a one-way ANOVA given sample data. (10)
    • 22.105: 1E Conduct the Tukey–Kramer (T–K) multiple comparisons procedure to identify significant differences among different population means. (10)
    • 22: JMP Simulations (4)
    • 22: Test Bank (176)

  • Chapter 23: Two-Way Analysis of Variance
    • 23.101: 1A Given a study scenario for a two-way ANOVA, write the null and alternative hypotheses. (10)
    • 23.102: 1B Construct and interpret an interaction plot. (10)
    • 23.103: 1C Calculate the mean squares and the sample F statistic for each factor and interaction in a two-way ANOVA. (10)
    • 23.104: 1D Conduct a two-way ANOVA given sample data. (10)
    • 23: JMP Simulations (2)
    • 23: Test Bank (99)

  • Chapter 24: Nonparametric Methods
    • 24: Stats in Practice Video Question (1)
    • 24.101: 1A Conduct a matched pairs sign test. (10)
    • 24.102: 1B Conduct a single-sample sign test for a measure of central tendency. (10)
    • 24.201: 2A Conduct a Wilcoxon Rank Sum Test for two independent samples. (10)
    • 24.301: 3A Calculate the Spearman rank correlation coefficient for a sample. (10)
    • 24.302: 3B Determine if the Spearman rank correlation coefficient is significant. (10)
    • 24.401: 4A Conduct the Kruskal–Wallis Test for more than two independent samples. (10)
    • 24.501: 5A Use McNemar's Test for matched pairs from two categories to test if two frequencies occur in the same proportion. (10)
    • 24: Labs (6)
    • 24: Test Bank (133)

  • Chapter PJT: Project
    • PJT.1: Project (4)


Statistics by Learning Objective content is built around 200+ learning objectives organized by sections across 24 modules, enabling you to teach what you want to teach, how you want to teach, when you want to teach it. With the power of WebAssign, you have the flexibility to cherry-pick and organize these self-contained learning objectives to seamlessly align with your syllabus and teaching style. Real-world data sets, technology guides, 1400+ relevant examples and 2000+ assessments across a variety of major-specific interests provide the context students need to connect the dots to the statistical concepts at hand.

Product Features

  • Read It links under each question quickly jump to the corresponding section of a complete, interactive eBook that lets students highlight and take notes as they read.
  • Watch It links provide step-by-step instruction with short, engaging videos that are ideal for visual learners.
  • Course Packs for four different course levels (Low, Middle, Upper, All) have ready-to-use assignments built by subject matter experts specifically for this product, incorporating Simulation Questions by JMP. They are designed to save you time, and can be easily customized to meet your teaching goals. Course Packs are also available for Stats in Practice Video Questions, Labs, and Project Milestones.
  • Stats in Practice Video Questions (SIP) show students how Statistics applies in the real world. Short and current news videos introduce each module. Each video is accompanied by multiple-choice and discussion questions, so that students can understand real-world context of what they're learning and stay engaged throughout the whole module.
  • Simulation Questions by JMP (JMP): Have your students understand concepts by utilizing real data. Students must discover the answer to guided questions by interacting with a simulation of real data in our JMP interactive applet within WebAssign.
  • Labs (LAB): Students can perform real statistical analysis in class or online with premade and module-specific Stats Labs. Require students to use the instructor-selected data analysis tool to analyze a real data set, pulling together knowledge learned from that module and previous material to facilitate whole-picture learning.
  • Project Milestones (PJT): Allow one place for students to ideate, collaborate, and submit a longer-term project. The four sequential milestones are:
    1. Research Design
    2. Gather Data
    3. Analyze Data
    4. Present Results
  • Test Banks: A pool of over 1000 assessments for use in quizzes, tests, and exams.
  • Student Resources include Data Analysis Tool Instructions / Tech Guides for the below software. Can be used stand-alone or in conjunction with assessment items (Homework, Labs, or Project Milestones).
    • TI-83/84 and TI-Nspire Calculator
    • Excel
    • JMP
    • Minitab
    • SPSS
    • R
  • Instructor Resources include Instructional Lecture Videos, hosted by Dana Mosely. These topic-specific videos provide explanations of key concepts, examples, and applications in a lecture-based format.

Homework Question Features

  • We use real-world examples to help cover a variety of major-specific interests and show how it may connect with a student's field of study and/or life in general. Discipline categories include:
    • Life Sciences, Environmental Sciences, and Agriculture
    • Political Science and Public Issues
    • Business and Marketing
    • Education and Social Issues
    • Physical Sciences, Engineering, and Manufacturing
    • College Life, Sports, and Entertainment
  • Student Difficulty Levels are marked within the "Comments" feature of each item.
    • Easy: Recall and reproduction. Recalling information such as a fact, definition, term, or simple procedure, as well as applying a simple formula.
    • Medium: Skills and concepts. At this level, a student must make some decisions about his or her approach. Typically involves a multi-step procedure.
    • Hard: Strategic thinking and justifying choice. Students must use planning and evidence via abstract thinking. Includes drawing conclusions from observations, citing evidence, and developing a logical argument for concepts, explaining phenomena in terms of concepts, and using concepts to solve problems.
  • Data Set Problems (DS) use real-world data sets to set up the question.
  • Challenge Problems (CH) are multi-concept questions that pull information from prerequisite learning objectives already covered.
  • Challenge Data Set Problems (CHDS) include data sets and cover prerequisite learning objectives.
  • Concept Questions (CQ) provide a new way of engaging with non-computational questions. Students enter a free response before they choose a multiple-choice answer, closing the gap between homework and test preparedness.

Questions Available within WebAssign

One-hundred percent of questions written specifically for this product are available in WebAssign. Whenever possible, variables, numbers, or words have been randomized so that each student receives a unique version of the question. This list is updated nightly.

Question Group Key
PJT: Project Milestones
JMP: Simulation Questions by JMP
LAB: Labs
SIP: Stats in Practice Video Questions
TB: Test Banks
DS: Data Set Problem
CH: Challenge Problem
CHDS: Challenge Data Set Problem
CQ: Concept Question

Question Availability Color Key
BLACK questions are available now
GRAY questions are under development


Group Quantity Questions
Chapter PJT: Project
PJT.1 4 001 002 003 004
Chapter 1: Concepts of Statistics
1.TB 54 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054
1.1A 10 001 002 003 004 005 006 007 008 101.CQ 102.CQ
1.2A 12 001 002 003 004 005 006 007 008 009.CH 010.CH 101.CQ 102.CQ
1.3A 12 001 002 003 004 005 006 007 008 009 010.CH 101.CQ 102.CQ
1.3B 12 001 002 003.DS 004 005 006 007 008 009.CH 010.CH 101.CQ 102.CQ
1.4A 12 001 002 003 004 005 006 007 008.DS 009.CH 010.CH 101.CQ 102.CQ
1.5A 12 001 002 003 004 005 006 007 008.DS 009.CH 010.CH 101.CQ 102.CQ
1.5B 10 001 002 003.DS 004 005 006.DS 007 008 009.DS 010
Chapter 2: Experiments and Types of Studies
2.TB 38 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038
2.1A 12 001 002 003 004 005.CH 006.CH 007.CH 008.CH 009.CH 010.CH 101.CQ 102.CQ
2.1B 12 001 002 003 004 005 006.CH 007.CH 008.CH 009.CH 010.CH 101.CQ 102.CQ
2.1C 10 001 002 003 004 005 006 007.CH 008.CH 101.CQ 102.CQ
2.1D 6 001 002 003 004.CHDS 005.CH 006.CH
2.1E 10 001 002 003 004 005 006 007 008 009.CH 010.CH
2.2A 6 001 002 003 004 005.CH 006.CH
2.2B 6 001 002 003 004.CH 005.CHDS 006.CH
2.2C 10 001 002 003 004.DS 005 006 007.CH 008.CH 009.CH 010.CH
2.3A 6 001 002 003 004.CH 005.CH 006.CH
2.3B 6 001 002 003 004.CH 005.CH 006.CH
2.3C 6 001 002 003 004 005.CH 006.CH
2.3D 6 001 002 003 004 005.CHDS 006.CH
2.3E 6 001 002 003 004 005.CH 006.CH
Chapter 3: Sampling Methods
3.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
3.TB 16 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016
3.1A 12 001 002 003 004 005.DS 006 007 008.CH 009 010.CH 101.CQ 102.CQ
3.1B 8 001 002 003 004 005.CH 006.CH 101.CQ 102.CQ
3.2A 10 001 002 003 004 005.CHDS 006 007 008.CH 009 010
3.2B 6 001 002 003 004 005.CH 006.CH
3.3A 10 001 002 003 004 005.CHDS 006 007 008.CH 009 010.CH
3.3B 8 001 002 003 004 005.CH 006.CH 101.CQ 102.CQ
3.4A 12 001 002 003 004.CH 005.DS 006 007.CH 008.CH 009.CH 010.CH 101.CQ 102.CQ
3.4B 8 001 002 003 004 005.CH 006.CH 101.CQ 102.CQ
3.5A 10 001 002 003 004 005 006 007 008.DS 009.CH 010.CH
3.5B 9 002 003 004 005 006 007 008.DS 009.CH 010.CH
Chapter 4: Numerical Measures
4.JMP 11 001 002 003 004 005 006 007 008 009 010 011
4.SIP 1 001
4.TB 211 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211
4.1A 12 001.DS 002.DS 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS 101.CQ 102.CQ
4.1B 7 001 002 003.DS 004.DS 005.CHDS 006.CHDS 101.CQ
4.1C 10 001 002 003 004.DS 005.DS 006.DS 007.CHDS 008.DS 009.CHDS 010.CHDS
4.2A 12 001 002 003 004.DS 005.DS 006.CHDS 007.DS 008.DS 009.DS 010.CHDS 101.CQ 102.CQ
4.2B 12 001 002.DS 003.DS 004.DS 005.DS 006.DS 007.CHDS 008.DS 009.DS 010.CHDS 101.CQ 102.CQ
4.3A 12 001 002 003.DS 004 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS 101.CQ 102.CQ
4.3B 12 001 002 003 004.DS 005.DS 006.DS 007.CHDS 008.DS 009.DS 010.CHDS 101.CQ 102.CQ
4.3C 12 001 002 003 004.DS 005.DS 006.DS 007.CHDS 008.DS 009.DS 010.CHDS 101.CQ 102.CQ
4.3D 12 001 002 003.DS 004 005 006.DS 007 008.CHDS 009.CHDS 010 101.CQ 102.CQ
4.3E 10 001 002 003 004.DS 005.DS 006.DS 007.DS 008 009.CHDS 010.CHDS
Chapter 5: Tabular Representations
5.TB 34 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034
5.1A 12 001 002.DS 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS 101.CQ 102.CQ
5.1B 8 001 002.DS 003.DS 004 005.CH 006.CHDS 101.CQ 102.CQ
5.1C 10 001 002 003.DS 004 005 006 007 008.CH 009.CH 010.DS
Chapter 6: Graphical Representations
6.JMP 9 001 002 003 004 005 006 007 008 009
6.Lab 5 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS
6.SIP 1 001
6.TB 75 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075
6.1A 8 001 002 003 004.CHDS 005.CHDS 006.CHDS 101.CQ 102.CQ
6.2A 8 001 002 003.DS 004.CHDS 005.CHDS 006.CHDS 101.CQ 102.CQ
6.2B 5 002 003 004.CHDS 005.CHDS 006.CHDS
6.2C 6 001 002 003 004.DS 005.CHDS 006.CHDS
6.2D 8 001 002 003 004 005.CH 006.CH 101.CQ 102.CQ
6.3A 8 001 002 003 004.DS 005.CHDS 006.CHDS 101.CQ 102.CQ
6.3B 6 001.DS 002 003 004.DS 005 006
6.3C 8 001 002 003.CHDS 004.CHDS 005.CHDS 006.CHDS 101.CQ 102.CQ
6.3D 8 001 002 003.DS 004.DS 005.CHDS 006.CHDS 101.CQ 102.CQ
6.3E 8 001 002 003 004 005.CHDS 006.CHDS 101.CQ 102.CQ
6.3F 6 001 002 003.DS 004.DS 005.CHDS 006.CHDS
6.3G 6 001 002 003 004 005.CHDS 006.CHDS
6.3H 8 001 002 003.DS 004.DS 005.CHDS 006.CHDS 101.CQ 102.CQ
6.3I 6 001 002 003 004.DS 005.CHDS 006.CHDS
Chapter 7: Concepts of Probability
7.SIP 1 001
7.TB 9 001 002 003 004 005 006 007 008 009
7.1A 12 001 002 003 004 005 006 007.CH 008 009 010.CH 101.CQ 102.CQ
7.1B 12 001 002 003 004 005 006 007.CHDS 008 009 010.CH 101.CQ 102.CQ
7.1C 11 001 002 004 005 006 007 008 009.CH 010.CH 101.CQ 102.CQ
7.1D 12 001.DS 002.DS 003 004 005.DS 006.DS 007.DS 008.DS 009.CH 010.CHDS 101.CQ 102.CQ
7.1E 12 001 002.DS 003 004 005.DS 006.DS 007 008 009.CHDS 010.CH 101.CQ 102.CQ
7.1F 12 001 002 003 004 005.DS 006 007 008.CHDS 009.CHDS 010.CH 101.CQ 102.CQ
7.1G 12 001 002 003 004.DS 005.DS 006.DS 007.CH 008.DS 009.CHDS 010.CH 101.CQ 102.CQ
7.1H 12 001 002 003 004 005.DS 006.CHDS 007 008.DS 009.CH 010.CH 101.CQ 102.CQ
7.1I 12 001 002 003 004 005.DS 006 007 008.CH 009 010.CH 101.CQ 102.CQ
7.1J 12 001 002 003 004 005 006 007 008 009.CHDS 010.CHDS 101.CQ 102.CQ
7.1K 8 001 002 003 004 005 006.CH 007.CH 008.CH
Chapter 8: Conditional Probability
8.Lab 5 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS
8.1A 10 001.DS 002.DS 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
8.1B 6 001 002 003 004 005.CH 006.CH
Chapter 9: Counting
9.1A 10 001 002 003 004 005 006 007 008 009.CH 010.CH
9.1B 10 001 002 003 004 005 006 007 008.CH 009.CH 010
9.1C 10 001 002 003 004 005 006 007 008 009.CH 010.CH
9.1D 10 001 002 003 004 005 006.CH 007 008 009.CH 010.CH
9.1E 8 001 002 003 004 005 006 007 008.CHDS
Chapter 10: Discrete Probability Distributions and Binomial Distribution
10.Lab 5 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS
10.SIP 1 001
10.TB 30 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030
10.1A 12 001 002 003 004 005 006 007 008.CH 009.CH 010.CHDS 101.CQ 102.CQ
10.1B 12 001 002 003 004 005 006 007 008.CH 009.CH 010.CHDS 101.CQ 102.CQ
10.1C 8 001 002 003 004 005 006 007.CH 008.CH
10.1D 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
10.1E 11 001 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
10.1F 11 001 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
10.1G 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
10.1H 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
10.1I 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
10.1J 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
10.2A 8 001 002 003 004 005 006.CH 101.CQ 102.CQ
10.2B 12 001 002 003 004 005 006 007.CH 008 009 010.CH 101.CQ 102.CQ
10.2C 12 001 002 003 004 005.DS 006.DS 007.DS 008 009.CHDS 010.CH 101.CQ 102.CQ
10.2D 11 002 003 004 005.DS 006.DS 007.DS 008 009.CH 010.CH 101.CQ 102.CQ
Chapter 11: More Discrete Probability Distributions
11.1A 6 001 002 003 004 005 006.CH
11.1B 10 001 002 003 004 005 006 007.CH 008.CH 009.CH 010.CH
11.1C 10 001 002 003 004 005 006 007.CH 008.CH 009.CH 010.CH
11.2A 6 001 002 003 004 005.CH 006.CH
11.2B 10 001 002 003 004 005 006 007 008 009.CH 010.CH
11.2C 8 001 002 003 004 005 006 007.CH 008.CH
11.2D 8 001 002 003 004 005 006 007 008
11.3A 6 001 002 003 004 005.CH 006.CH
11.3B 10 001 002 003 004 005 006 007.DS 008 009.CHDS 010.CH
11.3C 6 001 002 003 004 005.CH 006.CHDS
11.3D 10 001 002 003 004 005 006.DS 007 008 009.CH 010.CH
11.4A 6 001 002 003 004 005.CH 006.CH
11.4B 10 001 002 003 004 005 006 007 008 009.CH 010.CH
Chapter 12: Continuous Probability Distribution and Normal Distribution
12.JMP 5 001 002 003 004 005
12.Lab 5 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS
12.TB 224 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
12.1A 8 001 002 003 004 005.CH 006.CHDS 101.CQ 102.CQ
12.1B 6 001 002 003 004 005.CH 006.CH
12.2A 12 001 002 003 004 005.DS 006 007 008 009.CH 010.CH 101.CQ 102.CQ
12.2B 12 001 002 003 004 005 006 007.CH 008.CH 009.CH 010.CH 101.CQ 102.CQ
12.2C 12 001 002 003 004 005 006 007 008.CH 009.CH 010.CH 101.CQ 102.CQ
12.2D 12 001 002 003 004 005 006 007 008.CH 009.CH 010.CH 101.CQ 102.CQ
12.2E 12 001 002 003 004 005 006 007 008.CH 009.CH 010.CH 101.CQ 102.CQ
12.2F 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
12.2G 12 001 002 003 004 005 006 007 008.CH 009.CH 010.CHDS 101.CQ 102.CQ
12.2H 12 001 002 003 004 005 006 007 008 009.CH 010.CH 101.CQ 102.CQ
12.2I 12 001 002 003 004 005 006 007 008 009.CH 010.CH 101.CQ 102.CQ
12.2J 6 001 002 003 004 005 006.DS
12.2K 6 001 002 003 004 005.DS 006.DS
12.2L 6 001 002 003 004 005.CH 006.CH
Chapter 13: More Continuous Probability Distributions
13.TB 51 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051
13.1A 12 001 002 003 004 005 006 007 008 009 010 101.CQ 102.CQ
13.1B 12 001 002 003.CH 004 005 006 007.DS 008 009 010 101.CQ 102.CQ
13.2A 10 001 002 003 004 005 006 007 008 101.CQ 102.CQ
13.2B 10 001 002 003 004 005 006 007 008 101.CQ 102.CQ
13.3A 8 001 002 003 004 005 006 007 008
13.4A 10 001 002 003 004 005 006 007 008 009.CH 010.CH
Chapter 14: Sampling Distributions
14.SIP 1 001
14.TB 58 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058
14.1A 12 001 002 003 004 005.DS 006.DS 007.DS 008 009.CHDS 010.CH 101.CQ 102.CQ
14.1B 12 001 002 003 004 005.DS 006.DS 007.DS 008 009.CHDS 010.CH 101.CQ 102.CQ
14.1C 10 001 002 003 004 005 006 007 008 009.CH 010.CH
14.2A 10 001 002 003 004 005 006 007 008 009.CH 010.CH
14.2B 10 001 002 003 004 005 006 007.CH 008 009 010.CH
14.2C 10 001 002 003 004 005 006 007 008 009.CH 010.CH
14.3A 10 001 002 003 004 005 006 007 008 009.CH 010.CH
14.3B 10 001 002 003 004 005 006 007 008 009.CH 010.CH
14.3C 10 001 002 003 004 005 006 007 008 009.CH 010.CH
Chapter 15: Estimation for One Sample
15.JMP 9 001 002 003 004 005 006 007 008 009
15.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
15.SIP 1 001
15.TB 147 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147
15.1A 12 001 002 003 004 005 006 007 008.CHDS 009.DS 010.CHDS 101.CQ 102.CQ
15.1B 12 001.DS 002 003 004.DS 005 006.CH 007.CHDS 008.CH 009.CH 010.CHDS 101.CQ 102.CQ
15.1C 12 001 002 003 004 005 006 007 008.DS 009.CH 010.CHDS 101.CQ 102.CQ
15.1D 12 001 002 003 004 005 006 007 008.DS 009.CH 010.CH 101.CQ 102.CQ
15.1E 12 001 002 003 004 005 006.CHDS 007 008.DS 009 010.CHDS 101.CQ 102.CQ
15.1F 12 001 002 003 004 005 006 007 008.DS 009.CH 010.CHDS 101.CQ 102.CQ
15.2A 12 001 002 003 004 005 006 007 008 009.CH 010.CHDS 101.CQ 102.CQ
15.2B 12 001 002 003 004 005 006 007.CHDS 008 009 010.CH 101.CQ 102.CQ
15.2C 12 001 002 003 004 005 006 007 008.DS 009.CH 010.CH 101.CQ 102.CQ
15.3A 10 001 002 003 004 005 006 007 008.CHDS 009.CH 010.CHDS
15.3B 12 001 002 003 004 005 006 007 008 009.CH 010.CHDS 101.CQ 102.CQ
Chapter 16: Estimation for Two Samples
16.JMP 12 001 002 003 004 005 006 007 008 009 010 011 012
16.TB 48 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048
16.1A 10 001 002 003 004 005 006 007 008.CHDS 009.DS 010.CHDS
16.1B 10 001 002 003 004.DS 005 006.CH 007.CHDS 008.CH 009.CH 010.CHDS
16.2A 10 001 002 003 004 005 006 007.DS 008 009.CHDS 010.CH
16.2B 10 001 002 003 004.CHDS 005 006.CH 007.CHDS 008.CH 009.CH 010.CHDS
16.3A 10 001 002 003 004 005 006 007 008 009.CH 010.CH
16.3B 10 001 002 003 004 005 006.CH 007.CH 008.CH 009.CH 010.CH
Chapter 17: Hypothesis Tests for One Sample
17.JMP 13 001 002 003 004 005 006 007 008 009 010 011 012 013
17.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
17.TB 168 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168
17.1A 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
17.1B 12 001 002 003 004 005 006 007.CH 008.CH 009 010 101.CQ 102.CQ
17.1C 12 001 002 003 004 005 006 007 008 009.CH 010.CH 101.CQ 102.CQ
17.1D 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
17.1E 12 001 002 003 004 005 006 007.CH 008.CH 009 010 101.CQ 102.CQ
17.1F 12 001 002 003 004 005 006 007.DS 008 009.CH 010.CH 101.CQ 102.CQ
17.1G 12 001 002 003 004 005 006 007 008 009.CH 010.CH 101.CQ 102.CQ
17.1H 6 001 002 003 004 005.CH 006.CHDS
17.2A 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
17.2B 12 001 002 003 004 005 006 007.CH 008.CH 009 010 101.CQ 102.CQ
17.2C 9 002 003 004 005 006.DS 007.CH 008.CH 009.CH 010.CH
17.2D 10 001 002 003 004 005 006 007.CH 008 009 010.CH
17.3A 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
17.3B 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
Chapter 18: Hypothesis Tests for Two Samples
18.JMP 12 001 002 003 004 005 006 007 008 009 010 011 012
18.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
18.SIP 1 001
18.TB 145 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145
18.1A 12 001 002 003 004 005 006 007 008 009.CH 010.CH 101.CQ 102.CQ
18.1B 10 001 002 003 004 005 006 007 008.DS 009.CH 010.CH
18.2A 12 001 002 003 004 005 006 007 008 009.CHDS 010.CHDS 101.CQ 102.CQ
18.2B 10 001 002 003 004 005.DS 006 007.DS 008 009.CH 010.CHDS
18.3A 12 001 002 003 004 005 006 007.DS 008 009.CH 010.CH 101.CQ 102.CQ
18.3B 10 001 002 003 004 005 006 007 008 009.CHDS 010.CHDS
18.4A 12 001 002 003 004 005 006 007 008 009.CHDS 010.CH 101.CQ 102.CQ
18.4B 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
Chapter 19: Chi-Square Tests
19.JMP 6 001 002 003 004 005 006
19.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
19.SIP 1 001
19.TB 73 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073
19.1A 10 001 002 003 004.DS 005.DS 006 007 008.DS 009.CHDS 010.CH
19.1B 10 001 002 003.DS 004 005.DS 006 007 008.DS 009.CHDS 010.CHDS
19.2A 10 001.DS 002.DS 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
19.3A 10 001.DS 002.DS 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
19.3B 10 001.DS 002.DS 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
Chapter 20: Correlation and Regression
20.JMP 14 001 002 003 004 005 006 007 008 009 010 011 012 013 014
20.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
20.SIP 1 001
20.TB 153 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153
20.1A 8 001.DS 002.DS 003.DS 004.DS 005.CHDS 006.CHDS 101.CQ 102.CQ
20.1B 8 001 002 003 004.CH 005.CHDS 006.CHDS 101.CQ 102.CQ
20.2A 12 001 002 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS 101.CQ 102.CQ
20.2B 12 001 002 003.DS 004 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS 101.CQ 102.CQ
20.2C 12 001 002 003.DS 004.CHDS 005.DS 006.DS 007.DS 008.CHDS 009.DS 010.DS 101.CQ 102.CQ
20.3A 12 001 002 003 004 005 006 007.CH 008.CHDS 009.CHDS 010.CHDS 101.CQ 102.CQ
20.3B 12 001 002 003 004 005.CHDS 006 007.DS 008.CHDS 009.CH 010 101.CQ 102.CQ
20.3C 8 001 002 003 004 005.CHDS 006.CHDS 101.CQ 102.CQ
20.3D 10 001 002 003 004 005.CH 006 007 008 009.CH 010.CH
20.3E 10 001 002 003 004 005 006 007 008 009.CH 010.CH
20.3F 10 001 002 003 004 005 006 007.CH 008.DS 009.CH 010.CH
20.3G 10 001 002 003 004.DS 005 006.DS 007 008 009.CHDS 010.CHDS
Chapter 21: Multiple Regression
21.TB 26 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026
21.1A 10 001 002 003 004 005 006 007.DS 008 009.CH 010.CH
21.1B 10 001 002 003 004 005 006 007 008 009.CH 010
21.1C 6 001 002 003 004 005.CH 006.CH
Chapter 22: One-Way Analysis of Variance
22.JMP 4 001 002 003 004
22.TB 176 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
22.1A 10 001 002 003 004 005 006 007 008 009.CHDS 010.CHDS
22.1B 10 001 002 003 004 005 006 007 008.DS 009.CHDS 010.CHDS
22.1C 10 001 002 003 004 005 006 007 008.DS 009.DS 010.DS
22.1D 10 001 002 003 004.DS 005 006 007.DS 008.DS 009.DS 010
22.1E 10 001 002 003 004.DS 005 006 007 008.DS 009.DS 010
Chapter 23: Two-Way Analysis of Variance
23.JMP 2 001 002
23.TB 99 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099
23.1A 10 001 002 003 004 005 006 007 008 009.CHDS 010.CHDS
23.1B 10 001 002 003 004 005 006 007 008 009.DS 010.CHDS
23.1C 10 001 002 003 004 005 006 007 008 009.CHDS 010.CHDS
23.1D 10 001 002 003 004 005 006 007 008 009.DS 010.DS
Chapter 24: Nonparametric Methods
24.Lab 6 001.Excel 001.JMP 001.Minitab 001.R 001.SPSS 001.TI
24.SIP 1 001
24.TB 133 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133
24.1A 10 001 002.DS 003.DS 004.DS 005.DS 006.DS 007 008.DS 009.CHDS 010.CH
24.1B 10 001 002.DS 003 004.DS 005 006.DS 007 008.DS 009.CHDS 010.CHDS
24.2A 10 001 002 003.DS 004 005.DS 006.DS 007 008.DS 009.CHDS 010.CHDS
24.3A 10 001 002 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
24.3B 10 001 002 003 004 005 006 007.DS 008.DS 009.DS 010.CHDS
24.4A 10 001 002.DS 003.DS 004 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
24.5A 10 001 002 003 004 005 006 007.DS 008.DS 009.CHDS 010.CHDS
Total 4195