Statistics Question Collection 1st edition

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• Chapter 1: Concepts of Statistics
  • 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.
  
  
• Chapter 2: Experiments and Types of Studies
  • 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.
  
  
• Chapter 3: Sampling Methods
  • 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.
  
  
• Chapter 4: Numerical Measures
  • 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.
  
  
• Chapter 5: Tabular Representations
  • 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.
  
  
• Chapter 6: Graphical Representations
  • 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.
  
  
• Chapter 7: Concepts of Probability
  • 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.
  
  
• Chapter 8: Conditional Probability
  • 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.
  
  
• Chapter 9: Counting
  • 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.
  
  
• Chapter 10: Discrete Probability Distributions and Binomial Distribution
  • 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.
  
  
• Chapter 11: More Discrete Probability Distributions
  • 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.
  
  
• Chapter 12: Continuous Probability Distribution and Normal Distribution
  • 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.
  
  
• Chapter 13: More Continuous Probability Distributions
  • 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.
  
  
• Chapter 14: Sampling Distributions
  • 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.
  
  
• 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.
  • 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.
  
  
• 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.
  • 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.
  
  
• Chapter 17: Hypothesis Tests for One Sample
  • 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.
  
  
• 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.
  • 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.
  
  
• Chapter 19: Chi-Square Tests
  • 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.
  
  
• Chapter 20: Correlation and Regression
  • 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.
  
  
• Chapter 21: Multiple Regression
  • 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.
  
  
• Chapter 22: One-Way Analysis of Variance
  • 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.
  
  
• Chapter 23: Two-Way Analysis of Variance
  • 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.
  
  
• Chapter 24: Nonparametric Methods
  • 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.
  
  
• Chapter PJT: Project
  • PJT.1: Project