Statistics by Learning Objective 1st edition

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• Chapter P1: Arithmetic Operations Used in Statistics
  • P1.1A: Round decimal numbers.
  • P1.1B: Round up to the next greater whole number.
  • P1.1C: Round large numbers.
  • P1.1D: Round numbers in applications.
  • P1.2A: Identify different sets of numbers.
  • P1.2B: Classify numbers by their type.
  • P1.2C: Plot numbers on a number line.
  • P1.2D: Compute the distance between two numbers on the number line.
  • P1.2E: Order a group of numbers.
  • P1.2F: Use and identify discrete and continuous numbers in applications.
  • P1.3A: Convert a fraction to a decimal.
  • P1.3B: Convert a decimal to a percentage.
  • P1.3C: Convert a fraction to a percentage.
  • P1.3D: Convert a percentage to a decimal.
  • P1.3E: Apply conversions of fractions, decimals, and percentages.
  • P1.4A: Classify fractions as proper or improper.
  • P1.4B: Reduce a fraction to lowest terms.
  • P1.4C: Add and subtract fractions.
  • P1.4D: Multiply and divide fractions.
  • P1.4E: Simplify a complex fraction.
  • P1.4F: Apply operations with fractions.
  • P1.5A: Determine the absolute error between two values.
  • P1.5B: Compute the absolute error in an application.
  • P1.5C: Determine the relative error between two values.
  • P1.5D: Compute relative error in an application.
  • P1.5E: Determine the percent error between two values.
  • P1.5F: Compute percent error in an application.
  • P1.6A: Convert a large number into scientific notation.
  • P1.6B: Convert a decimal number into scientific notation.
  • P1.6C: Convert a number in scientific notation into standard form.
  • P1.6D: Interpret scientific and E-notation on a calculator.
  • P1.6E: Use scientific notation and E-notation in applications.
  • P1.7A: Use a mathematical table to convert inches to millimeters.
  • P1.7B: Calculate the percent error using mathematical table information.
  • P1.7C: Translate numbers in international system units to scientific notation.
  • P1.7D: Use a hierarchy of large numbers to convert a number from scientific notation.
  • P1.7E: Use a table to convert numbers to different bases.
  
  
• Chapter P2: Algebraic Expressions Used in Statistics and Basics of Solving Equations
  • P2.1A: Differentiate between an expression and an equation.
  • P2.1B: Translate English sentences into mathematical equations.
  • P2.1C: Identify strict inequalities.
  • P2.1D: Classify inclusive inequalities.
  • P2.1E: Determine possible variable values based on inequalities and phrases "at most" and "at least."
  • P2.1F: Determine possible variable values for compound inequalities.
  • P2.2A: Evaluate expressions using the order of operations.
  • P2.2B: Evaluate expressions involving square roots.
  • P2.2C: Use the order of operations in applications.
  • P2.2D: Evaluate algebraic expressions using given values.
  • P2.2E: Extract information from narrative and use formulas in applications.
  • P2.3A: Solve linear equations using addition or subtraction properties.
  • P2.3B: Solve linear equations using the division property.
  • P2.3C: Solve linear equations using multiplication property.
  • P2.3D: Solve linear equations using multiple steps.
  
  
• Chapter P3: Equations, Inequalities, and Problem Solving Techniques
  • P3.1A: Solve linear equations used in statistics using addition or subtraction properties.
  • P3.1B: Solve a linear equation used in statistics in an application.
  • P3.1C: Use multiple steps to solve equations used in statistics for z or s.
  • P3.1D: Use multiple steps to solve equations used in statistics for x.
  • P3.1E: Use multiple steps to solve equations used in statistics for m.
  • P3.1F: Solve a rational equation used in statistics.
  • P3.1G: Solve an equation used in statistics in an application.
  • P3.2A: Graph linear inequalities on a number line.
  • P3.2B: Graph compound inequalities that use "or" on a number line and write inequalities in interval notation.
  • P3.2C: Graph compound inequalities that use "and" on a number line and write inequalities in interval notation.
  • P3.2D: Write an interval using plus/minus notation.
  • P3.2E: Convert plus/minus notation to interval notation.
  • P3.2F: Use inequalities and plus/minus notation in applications.
  • P3.2G: Determine whether a given value satisfies an interval, inequality, and plus/minus notation.
  • P3.2H: Solve compound linear inequalities.
  • P3.3A: Solve an absolute value equation.
  • P3.3B: Solve an absolute value inequality involving "less than."
  • P3.3C: Solve an absolute value inequality involving "greater than."
  • P3.3D: Solve an absolute value inequality and use it in an application.
  • P3.3E: Solve an absolute value inequality for a set of discrete values.
  • P3.4A: Translate English to mathematics.
  • P3.4B: Use the problem-solving method to solve a word problem.
  • P3.4C: Write an equation using direct variation.
  • P3.4D: Write an equation using inverse variation.
  • P3.4E: Use direct and inverse variation in an application.
  • P3.5A: Solve a literal equation that is linear in form.
  • P3.5B: Solve a literal equation involving fractions.
  • P3.5C: Solve literal equations involving squares and square roots.
  • P3.5D: Use literal equations in formulas and applications.
  
  
• Chapter P4: Graphing Linear Equations in Two Variables
  • P4.1A: Plot points on a rectangular coordinate system.
  • P4.1B: Read ordered pairs from a graph.
  • P4.1C: Use scatterplots in applications.
  • P4.1D: Construct and interpret time-series scatterplots.
  • P4.2A: Identify independent (explanatory) and dependent (response) variables.
  • P4.2B: Read and interpret graphs.
  • P4.2C: Use a graph to compute absolute and percent error.
  • P4.2D: Interpret graphs in applications.
  • P4.3A: Identify linear equations in two variables.
  • P4.3B: Determine the intercepts of graphs of linear equations.
  • P4.3C: Graph lines that pass through the origin.
  • P4.3D: Graph horizontal and vertical lines.
  • P4.3E: Apply the graphs of vertical and horizontal lines.
  • P4.4A: Compute the slope of a line through two points.
  • P4.4B: Graph a line using slope and a point on the line.
  • P4.4C: Describe the slope of horizontal and vertical lines.
  • P4.4D: Use marginal change in an application.
  • P4.5A: Write an equation of a line given a point on the line and its slope.
  • P4.5B: Use the point-slope form in an application.
  • P4.5C: Write the statistical form of a line given marginal change and y-intercept.
  • P4.5D: Determine marginal change and y-intercept of a line given an equation.
  • P4.5E: Solve an application using the statistical form of a line.
  • P4.5F: Write the statistical form of a line given two points on a line.
  
  
• Chapter P5: Sets, Counting, and Sums
  • P5.1A: Use the listing method to rewrite sets.
  • P5.1B: Write sets using set-builder notation.
  • P5.1C: Determine whether a set is a subset.
  • P5.1D: Determine the intersection of sets.
  • P5.1E: Determine the union of sets.
  • P5.1F: Determine the complement of a set.
  • P5.2A: Determine the cardinality of a set.
  • P5.2B: Determine the cardinality of the intersection of sets.
  • P5.2C: Determine the cardinality of the union of sets.
  • P5.2D: Determine the cardinality of the complement of a set.
  • P5.2E: Apply cardinality of sets to a contingency table.
  • P5.3A: Construct a tree diagram.
  • P5.3B: Apply the Multiplication Principle.
  • P5.3C: Use factorial notation.
  • P5.3D: Use the permutation formula.
  • P5.3E: Use the combination formula.
  • P5.3F: Combine the Multiplication Principle and combinations.
  • P5.4A: Compute sums involving constants and powers.
  • P5.4B: Compute the square of a sum and the sum of squares.
  • P5.4C: Compare the sum of products and the product of sums.
  • P5.4D: Compute a partial sum.
  • P5.4E: Solve an application involving the sum of tabular values.
  
  
• Chapter P6: Functions and Area Under Functions
  • P6.1A: Determine whether a set of ordered pairs represents a function.
  • P6.1B: Write the domain and range of a function.
  • P6.1C: Evaluate and graph a function whose domain is discrete.
  • P6.1D: Evaluate functions in an application.
  • P6.2A: Graph linear functions.
  • P6.2B: Graph linear functions over continuous and discrete domains.
  • P6.2C: Evaluate a piecewise defined function.
  • P6.2D: Graph piecewise defined functions.
  • P6.2E: Evaluate a piecewise defined function unique to statistics.
  • P6.2F: Use a piecewise defined function in an application.
  • P6.3A: Determine the area of a rectangle.
  • P6.3B: Determine the area under a constant function on a closed interval.
  • P6.3C: Apply the area under a constant function.
  • P6.4A: Determine the area of a trapezoid.
  • P6.4B: Determine the area under a linear function on a closed interval.
  • P6.4C: Apply the area under a linear function on a closed interval.
  • 6: Labs
  • 6: Test Bank
  
  
• Chapter P7: Survey of Functions Used in Statistics
  • P7.1A: Evaluate and graph a general exponential function.
  • P7.1B: Evaluate and graph a natural exponential function.
  • P7.1C: Evaluate and graph a natural logarithmic function.
  • P7.1D: Use an exponential function model in an application.
  • P7.1E: Use a logarithmic function model in an application.
  • P7.2A: Evaluate and graph a rational exponent function.
  • P7.2B: Evaluate and graph a power function.
  • P7.2C: Use a power function in an application.
  • P7.3A: Identify a multivariable linear function.
  • P7.3B: Evaluate a multivariable linear function.
  • P7.3C: Use a multivariable linear function in an application.
  • 7.111: 1K Calculate the probability of an event using a simulation technique.
  • 7: Test Bank
  
  
• Chapter 1: Concepts of Statistics
  • 1: Concept Explorations
  • 1.1A: Distinguish the difference between descriptive and inferential statistics.
  • 1.2A: Given a study scenario, identify the population and sample.
  • 1.3A: Given a description of a data set, distinguish between categorical and numerical data.
  • 1.3B: Given a data set or description of a study, distinguish between discrete and continuous variables.
  • 1.4A: Given a study scenario, distinguish between parameters and statistics.
  • 1.5A: Given descriptions of different variables, identify the level of measurement for each (nominal, ordinal, interval, or ratio).
  • 1.5B: Given a study scenario, identify if the data set is univariate, bivariate, or multivariate.
  • 1: Labs
  • 1: Test Bank
  
  
• Chapter 2: Experiments and Types of Studies
  • 2: Concept Explorations
  • 2.1A: Given a study scenario, determine if it is an observational study or an experiment.
  • 2.1B: Given a study scenario, determine the conclusions one can make from an observational study or experiment.
  • 2.1C: Given a study scenario, identify the independent and dependent variable(s).
  • 2.1D: Given a study scenario, determine whether it includes confounding variables.
  • 2.1E: Distinguish between retrospective, cross-sectional, and longitudinal studies.
  • 2.2A: Given a study scenario, identify the components of an experiment.
  • 2.2B: Explain the advantages of using a control group or placebo in an experimental study.
  • 2.2C: Explain the advantages of using single-blind and double-blind studies.
  • 2.3A: Given a study scenario, determine whether it uses a completely randomized design.
  • 2.3B: Given a study scenario, determine whether it uses a block design.
  • 2.3C: Explain the advantages of using a block design for a study.
  • 2.3D: Given a study scenario, determine whether it uses repeated measures.
  • 2.3E: Explain the advantages of using repeated measures for a study.
  • 2: Labs
  • 2: Test Bank
  
  
• Chapter 3: Sampling Methods
  • 3: Concept Explorations
  • 3.1A: Construct a simple random sample using random numbers.
  • 3.1B: Explain how random sampling could be implemented within a study.
  • 3.2A: Explain how stratified random sampling could be implemented within a study.
  • 3.2B: Explain the advantages and disadvantages of stratified random sampling.
  • 3.3A: Explain how systematic sampling could be implemented for a study.
  • 3.3B: Explain the advantages and disadvantages of systematic sampling.
  • 3.4A: Given a study scenario, determine whether a convenience sample has been used.
  • 3.4B: Explain the advantages and disadvantages of convenience sampling.
  • 3.5A: Describe the different types of bias that can occur in a study.
  • 3.5B: Explain how bias can affect the data collected for a study and any conclusions to be drawn from that study.
  • 3: Labs
  • 3: Test Bank
  • 10.203: 2C Calculate the probability of a binomial random variable using technology or a table.
  • 10.204: 2D Calculate the mean and standard deviation of a binomial random variable.
  • 10: Labs
  • 10: Test Bank
  
  
• Chapter 4: Numerical Measures
  • 4: Concept Explorations
  • 4.1A: Given a data set, calculate the mean, median, and mode.
  • 4.1B: Explain which measures of central tendency are appropriate for numerical and categorical data.
  • 4.1C: Given a data set, calculate the weighted mean.
  • 4.2A: Given a data set, calculate the quartiles.
  • 4.2B: Given a data set, calculate the percentiles.
  • 4.3A: Given a data set, calculate the variance and standard deviation.
  • 4.3B: Given a data set, calculate the interquartile range.
  • 4.3C: Given a data set, determine if a specific value is an outlier, and identify what kind of an outlier it is.
  • 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.
  • 4.3E: Use the coefficient of variation to compare variation spread across different data sets.
  • 4: JMP Simulations
  • 4: Labs
  • 4: Test Bank
  • 4: Labs
  • 4: Test Bank
  
  
• Chapter 5: Tabular Representations
  • 5: Concept Explorations
  • 5.1A: Construct a frequency table from numerical data.
  • 5.1B: Construct a frequency table from categorical data.
  • 5.1C: Calculate marginal frequencies and percentages for data presented as a two-way frequency table.
  • 5: Labs
  • 5: Test Bank
  • 12.205: 2E Calculate probabilities for a normal random variable using technology or a table.
  • 12.206: 2F Find percentiles for a standard normal random variable using technology or a table.
  • 12.207: 2G Find percentiles for a normal random variable using technology or a table.
  • 12.208: 2H Find the value of a standard normal random variable given a probability using technology or a table.
  • 12.209: 2I Find the value of a normal random variable given a probability using technology or a table.
  • 12.210: 2J Determine whether data have a normal distribution using a histogram and normal probability plot.
  • 12.211: 2K Determine whether transformed data have a normal distribution using a histogram and normal probability plot.
  • 12.212: 2L Use the normal distribution to approximate the probability of a binomial random variable.
  • 12: JMP Simulations
  • 12: Labs
  • 12: Test Bank
  
  
• Chapter 6: Graphical Representations
  • 6: Concept Explorations
  • 6.1A: Given a data distribution, identify its shape.
  • 6.2A: Construct and interpret a bar chart from categorical data.
  • 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.
  • 6.2C: Construct and interpret a segmented bar chart.
  • 6.2D: Construct and interpret a pie chart from categorical data.
  • 6.3A: Construct and interpret a stem-and-leaf plot.
  • 6.3B: Given a data set with two groups, construct and interpret a comparative stem-and-leaf plot to compare the groups.
  • 6.3C: Construct and interpret a dotplot.
  • 6.3D: Given a data set with two groups, construct and interpret a comparative dotplot to compare the groups.
  • 6.3E: Construct and interpret a histogram from numerical data.
  • 6.3F: Given a data set with two groups, construct and interpret a histogram to compare the groups.
  • 6.3G: Construct and interpret a relative cumulative frequency plot from continuous data.
  • 6.3H: Construct and interpret a boxplot.
  • 6.3I: Construct and interpret a time series plot.
  • 6: JMP Simulations
  • 6: Labs
  • 6: Test Bank
  • 6: Labs
  • 6: Test Bank
  
  
• Chapter 7: Concepts of Probability
  • 7: Concept Explorations
  • 7.1A: Calculate the probability of an event using a relative frequency table.
  • 7.1B: Calculate the probability of an event when outcomes are equally likely.
  • 7.1C: Given a description of an experiment, define its sample space.
  • 7.1D: Calculate the probability of an event using a sample space.
  • 7.1E: Calculate the probability of a complement of an event.
  • 7.1F: Calculate the probability of a union of two or more events.
  • 7.1G: Calculate the probability of an intersection of two events.
  • 7.1H: Calculate the probability of two or more mutually exclusive events.
  • 7.1I: Determine if two events are independent.
  • 7.1J: Calculate the probability of two or more independent events using the Multiplication Rule.
  • 7.1K: Calculate the probability of an event using a simulation technique.
  • 7: Labs
  • 7: Test Bank
  
  
• Chapter 8: Conditional Probability
  • 8: Concept Explorations
  • 8.1A: Calculate the conditional probability of an event.
  • 8.1B: Use Bayes' Rule and the Law of Total Probability to calculate a conditional probability.
  • 8: Labs
  • 15.104: 1D Define the point estimate and calculate its margin of error for estimating a population mean when σ is unknown.
  • 15.105: 1E Calculate a confidence interval for a population mean when σ is unknown.
  • 15.106: 1F Calculate the sample size for estimating a population mean when σ is unknown.
  • 15.201: 2A Define the point estimate and calculate its margin of error for estimating a population proportion.
  • 15.202: 2B Calculate a confidence interval for a population proportion.
  • 15.203: 2C Calculate the sample size for estimating a population proportion.
  • 15.301: 3A Define the point estimate for estimating the population variance and population standard deviation.
  • 15.302: 3B Calculate a confidence interval for the population variance and population standard deviation.
  • 15: JMP Simulations
  • 15: Labs
  • 15: Test Bank
  
  
• Chapter 9: Counting
  • 9: Concept Explorations
  • 9.1A: Calculate the number of outcomes for an experiment by using the Multiplication Rule.
  • 9.1B: Calculate the number of outcomes for an experiment by using combinations.
  • 9.1C: Calculate the number of outcomes for an experiment by using permutations.
  • 9.1D: Calculate the probability of an event using counting techniques.
  • 9.1E: Recognize the difference in sampling with or without replacement and the implications of this distinction in calculating probabilities.
  • 9: Labs
  • 16: Test Bank
  
  
• Chapter 10: Discrete Probability Distributions and Binomial Distribution
  • 10: Concept Explorations
  • 10.1A: Construct a probability distribution of a discrete random variable.
  • 10.1B: Calculate probabilities for a discrete random variable given its probability distribution.
  • 10.1C: Find the cumulative distribution function for a discrete random variable.
  • 10.1D: Calculate the median of a discrete random variable.
  • 10.1E: Calculate the expected value or mean of a discrete random variable.
  • 10.1F: Calculate the expected value or mean of a function of a discrete random variable.
  • 10.1G: Calculate the variance and standard deviation of a discrete random variable.
  • 10.1H: Calculate the variance and standard deviation of a linear function of a discrete random variable.
  • 10.1I: Calculate the mean of a linear combination of independent random variables.
  • 10.1J: Calculate the variance and standard deviation of a linear combination of independent random variables.
  • 10.2A: Determine if a random variable has a binomial distribution.
  • 10.2B: Calculate the probability of a binomial random variable using the formula for the binomial distribution.
  • 10.2C: Calculate the probability of a binomial random variable using technology or a table.
  • 10.2D: Calculate the mean and standard deviation of a binomial random variable.
  • 10: Labs
  • 10: Test Bank
  • 10: Labs
  • 10: Test Bank
  
  
• Chapter 11: More Discrete Probability Distributions
  • 11: Concept Explorations
  • 11.1A: Determine if a random variable has a geometric distribution.
  • 11.1B: Calculate the probability of a geometric random variable using the formula for the geometric distribution.
  • 11.1C: Calculate the mean and standard deviation of a geometric random variable.
  • 11.2A: Determine if a random variable has a hypergeometric distribution.
  • 11.2B: Calculate the probability of a hypergeometric random variable using the formula for the hypergeometric distribution.
  • 11.2C: Given a small sample size, calculate the probability for a hypergeometric random variable using the binomial distribution formula.
  • 11.2D: Calculate the mean and standard deviation of a hypergeometric random variable.
  • 11.3A: Determine if a random variable has a Poisson distribution.
  • 11.3B: Calculate the probability of a Poisson random variable using the formula for the Poisson distribution.
  • 11.3C: Calculate the mean and standard deviation of a Poisson random variable.
  • 11.3D: Use the Poisson distribution to approximate the probability of a binomial random variable.
  • 11.4A: Determine if a set of random variables has a multinomial distribution.
  • 11.4B: Calculate the probability of a set of multinomial random variables using the formula for the multinomial distribution.
  • 11: Labs
  
  
• Chapter 12: Continuous Probability Distribution and Normal Distribution
  • 12: Concept Explorations
  • 12.1A: Determine if a function is a probability distribution of a continuous random variable.
  • 12.1B: Calculate the probability of a continuous random variable given its probability density curve.
  • 12.2A: Calculate the z-score for a normal random variable, given its mean and standard deviation.
  • 12.2B: Calculate the probability of a standard normal random variable using the Empirical Rule.
  • 12.2C: Calculate the probability of a normal random variable using the Empirical Rule.
  • 12.2D: Calculate the probability of a standard normal random variable using technology or a table.
  • 12.2E: Calculate probabilities for a normal random variable using technology or a table.
  • 12.2F: Find percentiles for a standard normal random variable using technology or a table.
  • 12.2G: Find percentiles for a normal random variable using technology or a table.
  • 12.2H: Find the value of a standard normal random variable given a probability using technology or a table.
  • 12.2I: Find the value of a normal random variable given a probability using technology or a table.
  • 12.2J: Determine whether data have a normal distribution using a histogram and normal probability plot.
  • 12.2K: Determine whether transformed data have a normal distribution using a histogram and normal probability plot.
  • 12.2L: Use the normal distribution to approximate the probability of a binomial random variable.
  • 12: JMP Simulations
  • 12: Labs
  • 12: Test Bank
  • 12: Labs
  • 12: Test Bank
  
  
• Chapter 13: More Continuous Probability Distributions
  • 13: Concept Explorations
  • 13.1A: Calculate the probability of a random variable with a t distribution using technology or a table.
  • 13.1B: Find the value of a random variable with a t distribution given a probability using technology or a table.
  • 13.2A: Calculate the probability of a random variable with a chi-square distribution using technology or a table.
  • 13.2B: Find the value of a random variable with a chi-square distribution given a probability using technology or a table.
  • 13.3A: Calculate the probability of a random variable with an F distribution using technology or a table.
  • 13.4A: Find the probability of a uniform random variable.
  • 13: Labs
  • 13: Test Bank
  • 20.304: 3D Calculate the prediction interval for predicting a single value for a response variable given a least squares regression line.
  • 20.305: 3E Conduct a hypothesis test for the slope of the least squares regression line.
  • 20.306: 3F Calculate the confidence interval for the slope of a least squares regression line.
  • 20.307: 3G Use a residual plot to analyze the appropriateness of a linear regression model for a given data set.
  • 20: JMP Simulations
  • 20: Labs
  • 20: Test Bank
  
  
• Chapter 14: Sampling Distributions
  • 14: Concept Explorations
  • 14.1A: Define the sampling distribution of a sample mean for a normal random variable.
  • 14.1B: Define the sampling distribution of a sample mean using the Central Limit Theorem.
  • 14.1C: Calculate the probability of a sample mean, given a normal or approximately normal sampling distribution.
  • 14.2A: Define the mean and standard deviation of the sampling distribution of a sample proportion.
  • 14.2B: Determine if the sampling distribution of the sample proportion is approximately normal.
  • 14.2C: Calculate the probability of a sample proportion, given a normal or approximately normal sampling distribution.
  • 14.3A: Define the mean and standard deviation of the sampling distribution of a difference of two sample means.
  • 14.3B: Define the mean and standard deviation of the sampling distribution of a mean difference for paired data.
  • 14.3C: Define the mean and standard deviation of the sampling distribution of a difference of two sample proportions.
  • 14: Labs
  • 14: Test Bank
  
  
• Chapter 15: Estimation for One Sample
  • 15: Concept Explorations
  • 15.1A: Define the point estimate and calculate its margin of error for estimating a population mean when σ is known.
  • 15.1B: Calculate a confidence interval for a population mean when σ is known.
  • 15.1C: Calculate the sample size for estimating a population mean when σ is known.
  • 15.1D: Define the point estimate and calculate its margin of error for estimating a population mean when σ is unknown.
  • 15.1E: Calculate a confidence interval for a population mean when σ is unknown.
  • 15.1F: Calculate the sample size for estimating a population mean when σ is unknown.
  • 15.2A: Define the point estimate and calculate its margin of error for estimating a population proportion.
  • 15.2B: Calculate a confidence interval for a population proportion.
  • 15.2C: Calculate the sample size for estimating a population proportion.
  • 15.3A: Define the point estimate for estimating the population variance and population standard deviation.
  • 15.3B: Calculate a confidence interval for the population variance and population standard deviation.
  • 15: JMP Simulations
  • 15: Labs
  • 15: Test Bank
  • 15: Labs
  • 15: Test Bank
  
  
• Chapter 16: Estimation for Two Samples
  • 16: Concept Explorations
  • 16.1A: Define the point estimate and calculate its margin of error for estimating for the difference of population means from two independent samples.
  • 16.1B: Calculate a confidence interval for a difference of two population means.
  • 16.2A: Define the point estimate and calculate its margin of error for estimating a population mean difference for paired data.
  • 16.2B: Calculate a confidence interval for a population mean difference for paired data.
  • 16.3A: Define the point estimate and calculate its margin of error for estimating a difference of two population proportions.
  • 16.3B: Calculate a confidence interval for a difference of two population proportions.
  • 16: JMP Simulations
  • 16: Labs
  • 16: Test Bank
  • 16: Labs
  • 16: Test Bank
  
  
• Chapter 17: Hypothesis Tests for One Sample
  • 17: Concept Explorations
  • 17.1A: Given a study scenario involving a population mean, write the null and alternative hypotheses.
  • 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.
  • 17.1C: Conduct a hypothesis test for a population mean using the normal distribution.
  • 17.1D: Conduct a hypothesis test for a population mean using a Student's t distribution.
  • 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.
  • 17.1F: Determine the outcome of a hypothesis test for a population mean using an appropriate critical value.
  • 17.1G: Calculate the power of a hypothesis test for a population mean.
  • 17.1H: Discuss the connection between confidence intervals and hypothesis tests.
  • 17.2A: Given a study scenario for the test of a population proportion, write the null and alternative hypotheses.
  • 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.
  • 17.2C: Conduct a hypothesis test for a population proportion.
  • 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.
  • 17.3A: Given a study scenario for testing a population variance, write the null and alternative hypotheses.
  • 17.3B: Conduct a hypothesis test for a population variance.
  • 17: JMP Simulations
  • 17: Labs
  • 17: Test Bank
  • 17: Labs
  • 17: Test Bank
  
  
• Chapter 18: Hypothesis Tests for Two Samples
  • 18: Concept Explorations
  • 18.1A: Given a study scenario for the test of the difference of two population means, write the null and alternative hypotheses.
  • 18.1B: Conduct a hypothesis test for the difference of two population means.
  • 18.2A: Given a study scenario for the test of a population mean difference using paired samples, write the null and alternative hypotheses.
  • 18.2B: Conduct a hypothesis test for the population mean difference using paired samples.
  • 18.3A: Given a study scenario for the test of the difference of two population proportions, write the null and alternative hypotheses.
  • 18.3B: Conduct a hypothesis test for the difference of two population proportions.
  • 18.4A: Given a study scenario for a test of two population variances, write the null and alternative hypotheses.
  • 18.4B: Conduct a hypothesis test for the equality of two population variances.
  • 18: JMP Simulations
  • 18: Labs
  • 18: Test Bank
  • 18: Labs
  • 18: Test Bank
  
  
• Chapter 19: Chi-Square Tests
  • 19: Concept Explorations
  • 19.1A: Calculate the sample chi-square statistic using observed and expected frequencies.
  • 19.1B: Conduct a goodness-of-fit test and decide if sample data are consistent with a given distribution.
  • 19.2A: Conduct a chi-square test of homogeneity and decide if two or more populations are not homogeneous.
  • 19.3A: Given data in a contingency table, calculate the sample chi-square statistic.
  • 19.3B: Conduct a chi-square test of independence and decide if two random variables are not independent.
  • 19: JMP Simulations
  • 19: Labs
  • 19: Test Bank
  
  
• Chapter 20: Correlation and Regression
  • 20: Concept Explorations
  • 20.1A: Construct a scatterplot given bivariate data.
  • 20.1B: Use a scatterplot to decide if a linear relationship exists between two variables.
  • 20.2A: Calculate the correlation coefficient from sample data.
  • 20.2B: Interpret a correlation coefficient calculated from sample data.
  • 20.2C: Given a correlation coefficient calculated from sample data, determine if it is significant.
  • 20.3A: Given sample data, calculate the least squares regression line.
  • 20.3B: Use the least squares regression line to predict the value of a response variable.
  • 20.3C: Use the coefficient of determination to interpret variation in the response variable.
  • 20.3D: Calculate the prediction interval for predicting a single value for a response variable given a least squares regression line.
  • 20.3E: Conduct a hypothesis test for the slope of the least squares regression line.
  • 20.3F: Calculate the confidence interval for the slope of a least squares regression line.
  • 20.3G: Use a residual plot to analyze the appropriateness of a linear regression model for a given data set.
  • 20: JMP Simulations
  • 20: Labs
  • 20: Test Bank
  • 20: Labs
  • 20: Test Bank
  
  
• Chapter 21: Multiple Regression
  • 21: Concept Explorations
  • 21.1A: Use a statistical calculator or computer program to develop a multiple regression model.
  • 21.1B: Given a multiple regression model, determine if its coefficients are significant.
  • 21.1C: Given a regression model, calculate the confidence interval for the mean value of a response.
  • 21: Labs
  • 21: Test Bank
  
  
• Chapter 22: One-Way Analysis of Variance
  • 22: Concept Explorations
  • 22.1A: Given a study scenario for a one-way ANOVA, write the null and alternative hypotheses.
  • 22.1B: Given sample data, calculate the between-groups and within-groups mean squares.
  • 22.1C: Calculate the sample F statistic for a one-way ANOVA.
  • 22.1D: Conduct a one-way ANOVA given sample data.
  • 22.1E: Conduct the Tukey–Kramer (T–K) multiple comparisons procedure to identify significant differences among different population means.
  • 22: JMP Simulations
  • 22: Labs
  • 22: Test Bank
  
  
• Chapter 23: Two-Way Analysis of Variance
  • 23: Concept Explorations
  • 23.1A: Given a study scenario for a two-way ANOVA, write the null and alternative hypotheses.
  • 23.1B: Construct and interpret an interaction plot.
  • 23.1C: Calculate the mean squares and the sample F statistic for each factor and interaction in a two-way ANOVA.
  • 23.1D: Conduct a two-way ANOVA given sample data.
  • 23: JMP Simulations
  • 23: Labs
  • 23: Test Bank
  
  
• Chapter 24: Nonparametric Methods
  • 24: Concept Explorations
  • 24.1A: Conduct a matched pairs sign test.
  • 24.1B: Conduct a single-sample sign test for a measure of central tendency.
  • 24.2A: Conduct a Wilcoxon Rank Sum Test for two independent samples.
  • 24.3A: Calculate the Spearman rank correlation coefficient for a sample.
  • 24.3B: Determine if the Spearman rank correlation coefficient is significant.
  • 24.4A: Conduct the Kruskal–Wallis Test for more than two independent samples.
  • 24.5A: Use McNemar's Test for matched pairs from two categories to test if two frequencies occur in the same proportion.
  • 24: Labs
  • 24: Test Bank
  
  
• Chapter PJT: Project
  • PJT.1: Project