# Statistics Question Collection 1st edition

Cengage Learning
Publisher: Cengage Learning

Access is contingent on use of this textbook in the instructor's classroom.

• Chapter 1: Concepts of Statistics
• 1.1A: Distinguish the difference between descriptive and inferential statistics. (8)
• 1.2A: Given a study scenario, identify the population and sample. (10)
• 1.3A: Given a description of a data set, distinguish between categorical and numerical data. (10)
• 1.3B: Given a data set or description of a study, distinguish between discrete and continuous variables. (10)
• 1.4A: Given a study scenario, distinguish between parameters and statistics. (10)
• 1.5A: Given descriptions of different variables, identify the level of measurement for each (nominal, ordinal, interval, or ratio). (10)
• 1.5B: Given a study scenario, identify if the data set is univariate, bivariate, or multivariate. (10)

• Chapter 2: Experiments and Types of Studies
• 2.1A: Given a study scenario, determine if it is an observational study or an experiment. (10)
• 2.1B: Given a study scenario, determine the conclusions one can make from an observational study or experiment. (10)
• 2.1C: Given a study scenario, identify the independent and dependent variable(s). (8)
• 2.1D: Given a study scenario, determine whether it includes confounding variables. (6)
• 2.1E: Distinguish between retrospective, cross-sectional, and longitudinal studies. (10)
• 2.2A: Given a study scenario, identify the components of an experiment. (6)
• 2.2B: Explain the advantages of using a control group or placebo in an experimental study. (6)
• 2.2C: Explain the advantages of using single-blind and double-blind studies. (10)
• 2.3A: Given a study scenario, determine whether it uses a completely randomized design. (6)
• 2.3B: Given a study scenario, determine whether it uses a block design. (6)
• 2.3C: Explain the advantages of using a block design for a study. (6)
• 2.3D: Given a study scenario, determine whether it uses repeated measures. (6)
• 2.3E: Explain the advantages of using repeated measures for a study. (6)

• Chapter 3: Sampling Methods
• 3.1A: Construct a simple random sample using random numbers. (10)
• 3.1B: Explain how random sampling could be implemented within a study. (6)
• 3.2A: Explain how stratified random sampling could be implemented within a study. (10)
• 3.2B: Explain the advantages and disadvantages of stratified random sampling. (6)
• 3.3A: Explain how systematic sampling could be implemented for a study. (10)
• 3.4A: Given a study scenario, determine whether a convenience sample has been used. (10)
• 3.5A: Describe the different types of bias that can occur in a study. (10)
• 3.5B: Explain how bias can affect the data collected for a study and any conclusions to be drawn from that study. (9)

• Chapter 4: Numerical Measures
• 4.1A: Given a data set, calculate the mean, median, and mode. (10)
• 4.1B: Explain which measures of central tendency are appropriate for numerical and categorical data. (6)
• 4.1C: Given a data set, calculate the weighted mean. (10)
• 4.2A: Given a data set, calculate the quartiles. (10)
• 4.2B: Given a data set, calculate the percentiles. (10)
• 4.3A: Given a data set, calculate the variance and standard deviation. (10)
• 4.3B: Given a data set, calculate the interquartile range. (10)
• 4.3C: Given a data set, determine if a specific value is an outlier, and identify what kind of an outlier it is. (10)
• 4.3D: 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. (10)
• 4.3E: Use the coefficient of variation to compare variation spread across different data sets. (10)

• Chapter 5: Tabular Representations
• 5.1A: Construct a frequency table from numerical data. (10)
• 5.1B: Construct a frequency table from categorical data. (6)
• 5.1C: Calculate marginal frequencies and percentages for data presented as a two-way frequency table. (10)

• Chapter 6: Graphical Representations
• 6.1A: Given a data distribution, identify its shape. (6)
• 6.2A: Construct and interpret a bar chart from categorical data. (6)
• 6.2B: Given a data set with two groups, construct and interpret a comparative bar chart from discrete or categorical data to compare the groups. (5)
• 6.2C: Construct and interpret a segmented bar chart. (6)
• 6.2D: Construct and interpret a pie chart from categorical data. (6)
• 6.3A: Construct and interpret a stem-and-leaf plot. (6)
• 6.3B: Given a data set with two groups, construct and interpret a comparative stem-and-leaf plot to compare the groups. (6)
• 6.3C: Construct and interpret a dotplot. (6)
• 6.3D: Given a data set with two groups, construct and interpret a comparative dotplot to compare the groups. (6)
• 6.3E: Construct and interpret a histogram from numerical data. (6)
• 6.3F: Given a data set with two groups, construct and interpret a histogram to compare the groups. (6)
• 6.3G: Construct and interpret a relative cumulative frequency plot from continuous data. (6)
• 6.3H: Construct and interpret a boxplot. (6)
• 6.3I: Construct and interpret a time series plot. (6)

• Chapter 7: Concepts of Probability
• 7.1A: Calculate the probability of an event using a relative frequency table. (10)
• 7.1B: Calculate the probability of an event when outcomes are equally likely. (10)
• 7.1C: Given a description of an experiment, define its sample space. (9)
• 7.1D: Calculate the probability of an event using a sample space. (10)
• 7.1E: Calculate the probability of a complement of an event. (10)
• 7.1F: Calculate the probability of a union of two or more events. (10)
• 7.1G: Calculate the probability of an intersection of two events. (10)
• 7.1H: Calculate the probability of two or more mutually exclusive events. (10)
• 7.1I: Determine if two events are independent. (10)
• 7.1J: Calculate the probability of two or more independent events using the Multiplication Rule. (10)
• 7.1K: Calculate the probability of an event using a simulation technique. (8)

• Chapter 8: Conditional Probability
• 8.1A: Calculate the conditional probability of an event. (10)
• 8.1B: Use Bayes' Rule and the Law of Total Probability to calculate a conditional probability. (6)

• Chapter 9: Counting
• 9.1A: Calculate the number of outcomes for an experiment by using the Multiplication Rule. (10)
• 9.1B: Calculate the number of outcomes for an experiment by using combinations. (10)
• 9.1C: Calculate the number of outcomes for an experiment by using permutations. (10)
• 9.1D: Calculate the probability of an event using counting techniques. (10)
• 9.1E: 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
• 10.1A: Construct a probability distribution of a discrete random variable. (10)
• 10.1B: Calculate probabilities for a discrete random variable given its probability distribution. (10)
• 10.1C: Find the cumulative distribution function for a discrete random variable. (8)
• 10.1D: Calculate the median of a discrete random variable. (10)
• 10.1E: Calculate the expected value or mean of a discrete random variable. (9)
• 10.1F: Calculate the expected value or mean of a function of a discrete random variable. (9)
• 10.1G: Calculate the variance and standard deviation of a discrete random variable. (10)
• 10.1H: Calculate the variance and standard deviation of a linear function of a discrete random variable. (10)
• 10.1I: Calculate the mean of a linear combination of independent random variables. (10)
• 10.1J: Calculate the variance and standard deviation of a linear combination of independent random variables. (10)
• 10.2A: Determine if a random variable has a binomial distribution. (6)
• 10.2B: Calculate the probability of a binomial random variable using the formula for the binomial distribution. (10)
• 10.2C: Calculate the probability of a binomial random variable using technology or a table. (10)
• 10.2D: Calculate the mean and standard deviation of a binomial random variable. (9)

• Chapter 11: More Discrete Probability Distributions
• 11.1A: Determine if a random variable has a geometric distribution. (6)
• 11.1B: Calculate the probability of a geometric random variable using the formula for the geometric distribution. (10)
• 11.1C: Calculate the mean and standard deviation of a geometric random variable. (10)
• 11.2A: Determine if a random variable has a hypergeometric distribution. (6)
• 11.2B: Calculate the probability of a hypergeometric random variable using the formula for the hypergeometric distribution. (10)
• 11.2C: Given a small sample size, calculate the probability for a hypergeometric random variable using the binomial distribution formula. (8)
• 11.2D: Calculate the mean and standard deviation of a hypergeometric random variable. (8)
• 11.3A: Determine if a random variable has a Poisson distribution. (6)
• 11.3B: Calculate the probability of a Poisson random variable using the formula for the Poisson distribution. (10)
• 11.3C: Calculate the mean and standard deviation of a Poisson random variable. (6)
• 11.3D: Use the Poisson distribution to approximate the probability of a binomial random variable. (10)
• 11.4A: Determine if a set of random variables has a multinomial distribution. (6)
• 11.4B: 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
• 12.1A: Determine if a function is a probability distribution of a continuous random variable. (6)
• 12.1B: Calculate the probability of a continuous random variable given its probability density curve. (6)
• 12.2A: Calculate the z-score for a normal random variable, given its mean and standard deviation. (10)
• 12.2B: Calculate the probability of a standard normal random variable using the Empirical Rule. (10)
• 12.2C: Calculate the probability of a normal random variable using the Empirical Rule. (10)
• 12.2D: Calculate the probability of a standard normal random variable using technology or a table. (10)
• 12.2E: Calculate probabilities for a normal random variable using technology or a table. (10)
• 12.2F: Find percentiles for a standard normal random variable using technology or a table. (10)
• 12.2G: Find percentiles for a normal random variable using technology or a table. (10)
• 12.2H: Find the value of a standard normal random variable given a probability using technology or a table. (10)
• 12.2I: Find the value of a normal random variable given a probability using technology or a table. (10)
• 12.2J: Determine whether data have a normal distribution using a histogram and normal probability plot. (6)
• 12.2K: Determine whether transformed data have a normal distribution using a histogram and normal probability plot. (6)
• 12.2L: Use the normal distribution to approximate the probability of a binomial random variable. (6)

• Chapter 13: More Continuous Probability Distributions
• 13.1A: Calculate the probability of a random variable with a t distribution using technology or a table. (10)
• 13.1B: Find the value of a random variable with a t distribution given a probability using technology or a table. (10)
• 13.2A: Calculate the probability of a random variable with a chi-square distribution using technology or a table. (8)
• 13.2B: Find the value of a random variable with a chi-square distribution given a probability using technology or a table. (8)
• 13.3A: Calculate the probability of a random variable with an F distribution using technology or a table. (8)
• 13.4A: Find the probability of a uniform random variable. (10)

• Chapter 14: Sampling Distributions
• 14.1A: Define the sampling distribution of a sample mean for a normal random variable. (10)
• 14.1B: Define the sampling distribution of a sample mean using the Central Limit Theorem. (10)
• 14.1C: Calculate the probability of a sample mean, given a normal or approximately normal sampling distribution. (10)
• 14.2A: Define the mean and standard deviation of the sampling distribution of a sample proportion. (10)
• 14.2B: Determine if the sampling distribution of the sample proportion is approximately normal. (10)
• 14.2C: Calculate the probability of a sample proportion, given a normal or approximately normal sampling distribution. (10)
• 14.3A: Define the mean and standard deviation of the sampling distribution of a difference of two sample means. (10)
• 14.3B: Define the mean and standard deviation of the sampling distribution of a mean difference for paired data. (10)
• 14.3C: Define the mean and standard deviation of the sampling distribution of a difference of two sample proportions. (10)

• Chapter 15: Estimation for One Sample
• 15.1A: Define the point estimate and calculate its margin of error for estimating a population mean when σ is known. (10)
• 15.1B: Calculate a confidence interval for a population mean when σ is known. (10)
• 15.1C: Calculate the sample size for estimating a population mean when σ is known. (10)
• 15.1D: Define the point estimate and calculate its margin of error for estimating a population mean when σ is unknown. (10)
• 15.1E: Calculate a confidence interval for a population mean when σ is unknown. (10)
• 15.1F: Calculate the sample size for estimating a population mean when σ is unknown. (10)
• 15.2A: Define the point estimate and calculate its margin of error for estimating a population proportion. (10)
• 15.2B: Calculate a confidence interval for a population proportion. (10)
• 15.2C: Calculate the sample size for estimating a population proportion. (10)
• 15.3A: Define the point estimate for estimating the population variance and population standard deviation. (10)
• 15.3B: Calculate a confidence interval for the population variance and population standard deviation. (10)

• Chapter 16: Estimation for Two Samples
• 16.1A: Define the point estimate and calculate its margin of error for estimating for the difference of population means from two independent samples. (10)
• 16.1B: Calculate a confidence interval for a difference of two population means. (10)
• 16.2A: Define the point estimate and calculate its margin of error for estimating a population mean difference for paired data. (10)
• 16.2B: Calculate a confidence interval for a population mean difference for paired data. (10)
• 16.3A: Define the point estimate and calculate its margin of error for estimating a difference of two population proportions. (10)
• 16.3B: Calculate a confidence interval for a difference of two population proportions. (10)

• Chapter 17: Hypothesis Tests for One Sample
• 17.1A: Given a study scenario involving a population mean, write the null and alternative hypotheses. (10)
• 17.1B: 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. (10)
• 17.1C: Conduct a hypothesis test for a population mean using the normal distribution. (10)
• 17.1D: Conduct a hypothesis test for a population mean using a Student's t distribution. (10)
• 17.1E: Given a hypothesis test for a study involving a population mean, explain what constitutes Type I and Type II errors in this context. (10)
• 17.1F: Determine the outcome of a hypothesis test for a population mean using an appropriate critical value. (10)
• 17.1G: Calculate the power of a hypothesis test for a population mean. (10)
• 17.1H: Discuss the connection between confidence intervals and hypothesis tests. (6)
• 17.2A: Given a study scenario for the test of a population proportion, write the null and alternative hypotheses. (10)
• 17.2B: 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. (10)
• 17.2C: Conduct a hypothesis test for a population proportion. (9)
• 17.2D: Given a hypothesis test for a study involving a population proportion, explain what constitutes Type I and Type II errors in this context. (10)
• 17.3A: Given a study scenario for testing a population variance, write the null and alternative hypotheses. (10)
• 17.3B: Conduct a hypothesis test for a population variance. (10)

• Chapter 18: Hypothesis Tests for Two Samples
• 18.1A: Given a study scenario for the test of the difference of two population means, write the null and alternative hypotheses. (10)
• 18.1B: Conduct a hypothesis test for the difference of two population means. (10)
• 18.2A: Given a study scenario for the test of a population mean difference using paired samples, write the null and alternative hypotheses. (10)
• 18.2B: Conduct a hypothesis test for the population mean difference using paired samples. (10)
• 18.3A: Given a study scenario for the test of the difference of two population proportions, write the null and alternative hypotheses. (10)
• 18.3B: Conduct a hypothesis test for the difference of two population proportions. (10)
• 18.4A: Given a study scenario for a test of two population variances, write the null and alternative hypotheses. (10)
• 18.4B: Conduct a hypothesis test for the equality of two population variances. (10)

• Chapter 19: Chi-Square Tests
• 19.1A: Calculate the sample chi-square statistic using observed and expected frequencies. (10)
• 19.1B: Conduct a goodness-of-fit test and decide if sample data are consistent with a given distribution. (10)
• 19.2A: Conduct a chi-square test of homogeneity and decide if two or more populations are not homogeneous. (10)
• 19.3A: Given data in a contingency table, calculate the sample chi-square statistic. (10)
• 19.3B: Conduct a chi-square test of independence and decide if two random variables are not independent. (10)

• Chapter 20: Correlation and Regression
• 20.1A: Construct a scatterplot given bivariate data. (6)
• 20.1B: Use a scatterplot to decide if a linear relationship exists between two variables. (6)
• 20.2A: Calculate the correlation coefficient from sample data. (10)
• 20.2B: Interpret a correlation coefficient calculated from sample data. (10)
• 20.2C: Given a correlation coefficient calculated from sample data, determine if it is significant. (10)
• 20.3A: Given sample data, calculate the least squares regression line. (10)
• 20.3B: Use the least squares regression line to predict the value of a response variable. (10)
• 20.3C: Use the coefficient of determination to interpret variation in the response variable. (6)
• 20.3D: Calculate the prediction interval for predicting a single value for a response variable given a least squares regression line. (10)
• 20.3E: Conduct a hypothesis test for the slope of the least squares regression line. (10)
• 20.3F: Calculate the confidence interval for the slope of a least squares regression line. (10)
• 20.3G: Use a residual plot to analyze the appropriateness of a linear regression model for a given data set. (10)

• Chapter 21: Multiple Regression
• 21.1A: Use a statistical calculator or computer program to develop a multiple regression model. (10)
• 21.1B: Given a multiple regression model, determine if its coefficients are significant. (10)
• 21.1C: Given a regression model, calculate the confidence interval for the mean value of a response. (6)

• Chapter 22: One-Way Analysis of Variance
• 22.1A: Given a study scenario for a one-way ANOVA, write the null and alternative hypotheses. (10)
• 22.1B: Given sample data, calculate the between-groups and within-groups mean squares. (10)
• 22.1C: Calculate the sample F statistic for a one-way ANOVA. (10)
• 22.1D: Conduct a one-way ANOVA given sample data. (10)
• 22.1E: Conduct the Tukey–Kramer (T–K) multiple comparisons procedure to identify significant differences among different population means. (10)

• Chapter 23: Two-Way Analysis of Variance
• 23.1A: Given a study scenario for a two-way ANOVA, write the null and alternative hypotheses. (10)
• 23.1B: Construct and interpret an interaction plot. (10)
• 23.1C: Calculate the mean squares and the sample F statistic for each factor and interaction in a two-way ANOVA. (10)
• 23.1D: Conduct a two-way ANOVA given sample data. (10)

• Chapter 24: Nonparametric Methods
• 24.1A: Conduct a matched pairs sign test. (10)
• 24.1B: Conduct a single-sample sign test for a measure of central tendency. (10)
• 24.2A: Conduct a Wilcoxon Rank Sum Test for two independent samples. (10)
• 24.3A: Calculate the Spearman rank correlation coefficient for a sample. (10)
• 24.3B: Determine if the Spearman rank correlation coefficient is significant. (10)
• 24.4A: Conduct the Kruskal–Wallis Test for more than two independent samples. (10)
• 24.5A: Use McNemar's Test for matched pairs from two categories to test if two frequencies occur in the same proportion. (10)

Statistics Question Collection content is built around 200+ learning objectives organized by sections across 24 modules, enabling you to teach flexibly. With the power of WebAssign, you can pick and organize these self-contained learning objectives to seamlessly align with your syllabus and teaching style. Real-world data sets, 500+ relevant examples and 1500+ assessments across a variety of major-specific interests provide the context students need to connect the dots to the statistical concepts at hand.

#### Finding the Questions for your Course

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• SALT (Statistical Analysis and Learning Tool) is a data analysis tool for introductory level statistics courses that helps students gain improved conceptual understanding of statistics through visualization and analysis of datasets. SALT can be used on its own or as a tool to answer SALT-enabled questions in WebAssign.
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The Statistical Analysis and Learning Tool (SALT) is designed by statisticians, for statisticians, to help you get introductory students deeply engaged in da...

### Student Learning Tools

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### Questions to Help Students Gain Interest and Assess Conceptual Understanding

• Challenge Problems (CH) are multi-concept questions that pull information from prerequisite learning objectives already covered.
• Data Set Problems (DS) use real-world data sets to set up the question.
• Challenge Data Set Problems (CHDS) include data sets and cover prerequisite learning objectives.

### Tools to Explore Real Data with Technology

• The Statistical Analysis and Learning Tool (SALT) is designed by statisticians, for statisticians, to help you get introductory students deeply engaged in data manipulation, analysis, and interpretation without getting bogged down in complex computations.

### Real-World Examples & Varying Student Difficulty Levels

• 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:
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• Easy: Recall and reproduction. Recalling information such as a fact, definition, term, or simple procedure, as well as applying a simple formula.
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• 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.

## Questions Available within WebAssign

Most questions from this textbook are available in WebAssign. The online questions are identical to the textbook questions except for minor wording changes necessary for Web use. 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
CH - Challenge Problem
DS - Data Set Problem
CHDS - Challenge Data Set Problem
S - SALT

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

Group Quantity Questions
Chapter 1: Concepts of Statistics
1.1A 8 001 002 003 004 005 006 007 008
1.2A 10 001 002 003 004 005 006 007 008 009.CH 010.CH
1.3A 10 001 002 003 004 005 006 007 008 009 010.CH
1.3B 10 001 002 003.DS 004 005 006 007 008 009.CH 010.CH
1.4A 10 001 002 003 004 005 006 007 008.DS 009.CH 010.CH
1.5A 10 001 002 003 004 005 006 007 008.DS 009.CH 010.CH
1.5B 10 001 002 003.DS 004 005 006.DS 007 008 009.DS 010
Chapter 2: Experiments and Types of Studies
2.1A 10 001 002 003 004 005.CH 006.CH 007.CH 008.CH 009.CH 010.CH
2.1B 10 001 002 003 004 005 006.CH 007.CH 008.CH 009.CH 010.CH
2.1C 8 001 002 003 004 005 006 007.CH 008.CH
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
3.1A 10 001 002 003 004 005.DS 006 007 008.CH 009 010.CH
3.1B 6 001 002 003 004 005.CH 006.CH
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 6 001 002 003 004 005.CH 006.CH
3.4A 10 001 002 003 004.CH 005.DS 006 007.CH 008.CH 009.CH 010.CH
3.4B 6 001 002 003 004 005.CH 006.CH
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
4.1A 11 001.DS 002.DS 003.DS 003.DS.Copy 004.DS.S 005.DS.S 006.DS 007.DS.S 008.DS 009.CHDS.S 010.CHDS.S
4.1B 6 001 002 003.DS 004.DS 005.CHDS 006.CHDS
4.1C 10 001 002 003 004.DS 005.DS 006.DS 007.CHDS 008.DS 009.CHDS 010.CHDS
4.2A 10 001 002 003 004.DS 005.DS.S 006.CHDS.S 007.DS.S 008.DS.S 009.DS.S 010.CHDS.S
4.2B 10 001 002.DS 003.DS 004.DS 005.DS 006.DS 007.CHDS 008.DS 009.DS 010.CHDS
4.3A 10 001 002 003.DS 004 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
4.3B 10 001 002 003 004.DS 005.DS 006.DS 007.CHDS 008.DS 009.DS 010.CHDS
4.3C 10 001 002 003 004.DS 005.DS 006.DS 007.CHDS 008.DS 009.DS 010.CHDS
4.3D 10 001 002 003.DS 004 005 006.DS 007 008.CHDS.S 009.CHDS.S 010
4.3E 10 001 002 003 004.DS 005.DS 006.DS 007.DS 008 009.CHDS 010.CHDS.S
Chapter 5: Tabular Representations
5.1A 10 001 002.DS 003.DS 004.DS.S 005.DS.S 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
5.1B 6 001 002.DS 003.DS 004 005.CH 006.CHDS
5.1C 10 001 002 003.DS 004 005 006 007 008.CH 009.CH 010.DS
Chapter 6: Graphical Representations
6.1A 6 001 002 003 004.CHDS 005.CHDS 006.CHDS
6.2A 6 001 002 003.DS 004.CHDS 005.CHDS 006.CHDS
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 6 001 002 003 004 005.CH 006.CH
6.3A 6 001 002 003 004.DS 005.CHDS 006.CHDS
6.3B 6 001.DS 002 003 004.DS 005 006
6.3C 6 001 002 003.CHDS.S 004.CHDS.S 005.CHDS.S 006.CHDS.S
6.3D 6 001 002 003.DS 004.DS 005.CHDS 006.CHDS
6.3E 6 001 002 003 004 005.CHDS.S 006.CHDS.S
6.3F 6 001 002 003.DS 004.DS 005.CHDS 006.CHDS
6.3G 6 001 002 003 004 005.CHDS.S 006.CHDS
6.3H 6 001 002 003.DS 004.DS 005.CHDS.S 006.CHDS.S
6.3I 6 001 002 003 004.DS 005.CHDS 006.CHDS
Chapter 7: Concepts of Probability
7.1A 10 001 002 003 004 005 006 007.CH 008 009 010.CH
7.1B 10 001 002 003 004 005 006 007.CHDS 008 009 010.CH
7.1C 9 001 002 004 005 006 007 008 009.CH 010.CH
7.1D 10 001.DS 002.DS 003 004 005.DS 006.DS 007.DS 008.DS 009.CH 010.CHDS
7.1E 10 001 002.DS 003 004 005.DS 006.DS 007 008 009.CHDS 010.CH
7.1F 10 001 002 003 004 005.DS 006 007 008.CHDS 009.CHDS 010.CH
7.1G 10 001 002 003 004.DS 005.DS 006.DS 007.CH 008.DS 009.CHDS 010.CH
7.1H 10 001 002 003 004 005.DS 006.CHDS 007 008.DS 009.CH 010.CH
7.1I 10 001 002 003 004 005.DS 006 007 008.CH 009 010.CH
7.1J 10 001 002 003 004 005 006 007 008 009.CHDS 010.CHDS
7.1K 8 001 002 003 004 005 006.CH 007.CH 008.CH
Chapter 8: Conditional Probability
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
10.1A 10 001 002 003 004 005 006 007 008.CH 009.CH 010.CHDS
10.1B 10 001 002 003 004 005 006 007 008.CH 009.CH 010.CHDS
10.1C 8 001 002 003 004 005 006 007.CH 008.CH
10.1D 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
10.1E 9 001 003 004 005 006 007 008 009.CHDS 010.CH
10.1F 9 001 003 004 005 006 007 008 009.CHDS 010.CH
10.1G 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
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 6 001 002 003 004 005 006.CH
10.2B 10 001 002 003 004 005 006 007.CH 008 009 010.CH
10.2C 10 001 002 003 004 005.DS.S 006.DS.S 007.DS.S 008.S 009.CHDS.S 010.CH.S
10.2D 9 002 003 004 005.DS 006.DS 007.DS 008 009.CH 010.CH
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.S 006.S 007.CH.S 008.CH.S
11.2D 8 001 002 003 004 005 006 007 008
11.3A 6 001 002 003 004 005.CH.S 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
12.1A 6 001 002 003 004 005.CH 006.CHDS
12.1B 6 001 002 003 004 005.CH 006.CH
12.2A 10 001 002 003.S 004 005.DS.S 006.S 007.S 008.S 009.CH.S 010.CH.S
12.2B 10 001 002 003 004 005 006 007.CH 008.CH 009.CH 010.CH
12.2C 10 001 002 003 004 005 006 007 008.CH 009.CH 010.CH
12.2D 10 001.S 002.S 003.S 004.S 005.S 006.S 007.S 008.CH.S 009.CH.S 010.CH.S
12.2E 10 001.S 002.S 003.S 004.S 005.S 006.S 007.S 008.CH.S 009.CH.S 010.CH.S
12.2F 10 001 002 003 004 005.S 006.S 007.S 008.S 009.CHDS.S 010.CH.S
12.2G 10 001 002 003.S 004.S 005.S 006.S 007.S 008.CH.S 009.CH.S 010.CHDS.S
12.2H 10 001.S 002.S 003.S 004.S 005.S 006.S 007.S 008.S 009.CH.S 010.CH.S
12.2I 10 001.S 002.S 003.S 004.S 005.S 006.S 007.S 008.S 009.CH.S 010.CH.S
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.S 004.S 005.CH.S 006.CH.S
Chapter 13: More Continuous Probability Distributions
13.1A 10 001 002 003.S 004.S 005.S 006.S 007.S 008.S 009.S 010.S
13.1B 10 001 002 003.CH.S 004.S 005.S 006.S 007.DS.S 008.S 009.S 010.S
13.2A 8 001 002 003 004.S 005.S 006.S 007.S 008.S
13.2B 8 001 002 003 004.S 005.S 006.S 007.S 008.S
13.3A 8 001 002 003 004.S 005.S 006.S 007.S 008.S
13.4A 10 001 002 003 004 005 006 007 008 009.CH 010.CH
Chapter 14: Sampling Distributions
14.1A 10 001 002 003 004 005.DS 006.DS 007.DS 008 009.CHDS.S 010.CH
14.1B 10 001 002 003 004 005.DS 006.DS 007.DS 008 009.CHDS.S 010.CH
14.1C 10 001 002 003 004.S 005.S 006.S 007.S 008.S 009.CH.S 010.CH.S
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.S 009.CH.S 010.CH.S
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
15.1A 10 001.S 002 003 004 005 006 007 008.CHDS 009.DS 010.CHDS
15.1B 10 001.DS 002.S 003 004.DS 005 006.CH 007.CHDS 008.CH 009.CH 010.CHDS
15.1C 10 001 002 003 004.S 005.S 006.S 007.S 008.DS.S 009.CH 010.CHDS
15.1D 10 001.S 002 003 004.S 005.S 006.S 007.S 008.DS.S 009.CH.S 010.CH.S
15.1E 10 001 002 003 004.S 005 006.CHDS 007.S 008.DS.S 009.S 010.CHDS.S
15.1F 10 001 002 003 004.S 005.S 006.S 007.S 008.DS.S 009.CH.S 010.CHDS.S
15.2A 10 001 002 003.S 004 005.S 006 007.S 008.S 009.CH.S 010.CHDS.S
15.2B 10 001.S 002 003 004 005 006.S 007.CHDS.S 008.S 009.S 010.CH.S
15.2C 10 001 002 003 004.S 005.S 006.S 007.S 008.DS.S 009.CH.S 010.CH.S
15.3A 10 001 002 003 004 005 006 007 008.CHDS 009.CH 010.CHDS
15.3B 10 001 002.S 003 004.S 005 006 007 008 009.CH 010.CHDS
Chapter 16: Estimation for Two Samples
16.1A 10 001.S 002 003 004.S 005 006.S 007.S 008.CHDS 009.DS.S 010.CHDS.S
16.1B 10 001 002.S 003 004.DS.S 005 006.CH.S 007.CHDS.S 008.CH.S 009.CH.S 010.CHDS
16.2A 10 001 002 003 004.S 005.S 006.S 007.DS.S 008.S 009.CHDS.S 010.CH.S
16.2B 10 001.S 002 003.S 004.CHDS.S 005 006.CH 007.CHDS.S 008.CH.S 009.CH.S 010.CHDS.S
16.3A 10 001 002 003 004.S 005 006.S 007.S 008.S 009.CH.S 010.CH.S
16.3B 10 001.S 002 003 004.S 005 006.CH.S 007.CH.S 008.CH.S 009.CH.S 010.CH.S
Chapter 17: Hypothesis Tests for One Sample
17.1A 10 001 002 003 004 005 006 007 008 009.CHDS.S 010.CH.S
17.1B 10 001 002 003 004 005 006 007.CH 008.CH 009 010
17.1C 10 001 002 003 004 005 006 007 008 009.CH 010.CH
17.1D 10 001 002 003 004 005.S 006.S 007.S 008.S 009.CHDS.S 010.CH.S
17.1E 10 001 002 003 004 005 006 007.CH 008.CH 009 010
17.1F 10 001.S 002 003.S 004.S 005.S 006 007.DS.S 008 009.CH 010.CH
17.1G 10 001 002 003 004 005 006.S 007.S 008.S 009.CH.S 010.CH.S
17.1H 6 001 002 003.S 004 005.CH.S 006.CHDS.S
17.2A 10 001 002 003 004 005 006 007 008 009.CHDS.S 010.CH.S
17.2B 10 001 002 003 004 005 006 007.CH 008.CH 009 010
17.2C 9 002 003 004 005.S 006.DS.S 007.CH.S 008.CH.S 009.CH.S 010.CH.S
17.2D 10 001 002 003 004 005 006 007.CH 008 009 010.CH
17.3A 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
17.3B 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
Chapter 18: Hypothesis Tests for Two Samples
18.1A 10 001 002 003 004 005 006 007 008 009.CH 010.CH
18.1B 10 001.S 002 003 004 005 006 007.S 008.DS.S 009.CH 010.CH
18.2A 10 001 002 003 004 005 006 007 008 009.CHDS 010.CHDS
18.2B 10 001 002 003 004 005.DS 006 007.DS 008 009.CH 010.CHDS
18.3A 10 001 002 003 004 005 006 007.DS 008 009.CH 010.CH.S
18.3B 10 001 002 003 004 005 006 007.S 008.S 009.CHDS.S 010.CHDS.S
18.4A 10 001 002 003 004 005 006 007 008 009.CHDS.S 010.CH.S
18.4B 10 001 002 003 004 005 006 007 008 009.CHDS 010.CH
Chapter 19: Chi-Square Tests
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
20.1A 6 001.DS 002.DS 003.DS 004.DS 005.CHDS 006.CHDS
20.1B 6 001 002 003 004.CH 005.CHDS 006.CHDS
20.2A 10 001 002 003.DS 004.DS 005.DS 006.DS 007.DS 008.DS 009.CHDS 010.CHDS
20.2B 10 001 002 003.DS 004 005.DS 006.DS 007.DS 008.DS 009.CHDS.S 010.CHDS.S
20.2C 10 001 002 003.DS 004.CHDS 005.DS 006.DS 007.DS 008.CHDS 009.DS 010.DS
20.3A 10 001 002 003 004 005 006 007.CH 008.CHDS.S 009.CHDS.S 010.CHDS.S
20.3B 10 001 002 003 004 005.CHDS.S 006 007.DS 008.CHDS.S 009.CH 010
20.3C 6 001 002 003 004 005.CHDS.S 006.CHDS.S
20.3D 10 001 002.S 003 004 005.CH 006 007 008 009.CH 010.CH
20.3E 10 001 002 003 004 005 006 007 008.S 009.CH 010.CH
20.3F 10 001 002.S 003 004 005 006 007.CH.S 008.DS 009.CH 010.CH
20.3G 10 001 002 003 004.DS 005 006.DS 007 008 009.CHDS.S 010.CHDS.S
Chapter 21: Multiple Regression
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
22.1A 10 001 002 003 004 005 006 007 008 009.CHDS.S 010.CHDS.S
22.1B 10 001 002 003 004 005 006 007 008.DS 009.CHDS.S 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
23.1A 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.S 003 004 005 006 007 008 009.DS 010.DS
Chapter 24: Nonparametric Methods
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 1854