# Statistics: Learning from Data with Learning Objective-Based Assessments 1st edition

Roxy Peck
Publisher: Cengage Learning

## eBook

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• Chapter 1: Collecting Data In Reasonable Ways
• 1.1: Statistical Studies: Observation and Experimentation (10)
• 1.2: Collecting Data: Planning an Observational Study (66)
• 1.3: Collecting Data: Planning an Experiment
• 1.4: The Importance of Random Selection and Random Assignment: What Types of Conclusions are Reasonable?
• 1.5: Avoid These Common Mistakes

• Chapter 2: Graphical Methods for Describing Data Distributions
• 2.1: Selecting an Appropriate Graphical Display (17)
• 2.2: Displaying Categorical Data: Bar Charts and Comparative Bar Charts (11)
• 2.3: Displaying Numerical Data: Dotplots, Stem-and-Leaf Displays, and Histograms (52)
• 2.4: Displaying Bivariate Numerical Data: Scatterplots and Time Series Plots (6)
• 2.5: Graphical Displays in the Media (12)
• 2.6: Avoid These Common Mistakes

• Chapter 3: Numerical Methods for Describing Data Distributions
• 3.1: Selecting Appropriate Numerical Summaries
• 3.2: Describing Center and Spread for Data Distributions That Are Approximately Symmetric (11)
• 3.3: Describing Center and Spread for Data Distributions That Are Skewed or Have Outliers (23)
• 3.4: Summarizing a Data Set: Boxplots (10)
• 3.5: Measures of Relative Standing: z-scores and Percentiles (10)
• 3.6: Avoid These Common Mistakes

• Chapter 4: Describing Bivariate Numerical Data
• 4.1: Correlation (20)
• 4.2: Linear Regression: Fitting a Line to Bivariate Data (10)
• 4.3: Assessing the Fit of a Line (16)
• 4.4: Describing Linear Relationships and Making Predictions—Putting It All Together
• 4.5: Avoid These Common Mistakes (6)

• Chapter 5: Probability
• 5.1: Interpreting Probabilities (10)
• 5.2: Calculating Probabilities (19)
• 5.3: Probabilities of More Complex Events: Unions, Intersections, and Complements (60)
• 5.4: Conditional Probability (10)
• 5.5: Probability as a Basis for Making Decisions
• 5.6: Estimating Probabilities Empirically and Using Simulation (Optional) (18)

• Chapter 6: Random Variables and Probability Distributions
• 6.1: Random Variables (3)
• 6.2: Probability Distributions for Discrete Random Variables (10)
• 6.3: Probability Distributions for Continuous Random Variables (6)
• 6.4: Mean and Standard Deviation of a Random Variable (30)
• 6.5: Normal Distributions (30)
• 6.6: Checking for Normality
• 6.7: Binomial and Geometric Distributions (Optional) (38)
• 6.8: Using the Normal Distribution to Approximate a Discrete Distribution (Optional) (6)

• Chapter 7: An Overview of Statistical Inference—Learning from Data
• 7.1: Statistical Inference—What You Can Learn From Data (11)
• 7.2: Selecting an Appropriate Method—Four Key Questions
• 7.3: A Five-Step Process for Statistical Inference

• Chapter 8: Sampling Variability and Sampling Distributions
• 8.1: Statistics and Sampling Variability
• 8.2: The Sampling Distribution of a Sample Proportion (6)
• 8.3: How Sampling Distributions Support Learning from Data

• Chapter 9: Estimating a Population Proportion
• 9.1: Selecting an Estimator
• 9.2: Estimating a Population Proportion—Margin of Error (8)
• 9.3: A Large Sample Confidence Interval for a Population Proportion (10)
• 9.4: Choosing a Sample Size to Achieve a Desired Margin of Error (10)
• 9.5: Avoid These Common Mistakes

• 10.1: Hypotheses and Possible Conclusions (10)
• 10.2: Potential Errors in Hypothesis Testing (10)
• 10.3: The Logic of Hypothesis Testing—An Informal Example
• 10.4: A Procedure for Carrying Out a Hypothesis Test (2)
• 10.5: Large-Sample Hypothesis Tests for a Population Proportion (7)
• 10.6: Avoid These Common Mistakes

• 11.1: Estimating the Difference Between Two Population Proportions (8)
• 11.2: Testing Hypotheses About the Difference Between Two Population Proportions (18)
• 11.3: Avoid These Common Mistakes (1)

• 12.1: The Sampling Distribution of the Sample Mean (10)
• 12.2: A Confidence Interval for a Population Mean (30)
• 12.3: Testing Hypotheses About a Population Mean (20)
• 12.4: Avoid These Common Mistakes

• 13.1: Testing Hypotheses About the Difference between Two Population Means Using Independent Samples (15)
• 13.2: Testing Hypotheses About the Difference between Two Population Means Using Paired Samples (10)
• 13.3: Estimating the Difference Between Two Population Means (14)
• 13.4: Avoid These Common Mistakes

• Chapter 14: Learning from Experimental Data
• 14.1: Variability and Random Assignment
• 14.2: Testing Hypotheses About Differences in Treatment Effects (1)
• 14.3: Estimating the Difference in Treatment Effects (6)
• 14.4: Avoid These Common Mistakes

• Chapter 15: Learning from Categorical Data
• 15.1: Chi-Square Tests for Univariate Categorical Data (10)
• 15.2: Tests for Homogeneity and Independence in a Two-Way Table (20)
• 15.3: Avoid These Common Mistakes

• Chapter 16: Understanding Relationships—Numerical Data Part 2
• 16.1: The Simple Linear Regression Model
• 16.2: Inferences Concerning the Slope of the Population Regression Line

• 17.1: The Analysis of Variance—Single-Factor ANOVA and the F Test
• 17.2: Multiple Comparisons

Statistics: Learning From Data, 1st edition, with Learning-Objective Based Assessments includes questions for over 85 learning objectives. This course was intentionally and carefully created to integrate a balance of conceptual and mechanical assessments in order to promote successful learning outcomes for students. The WebAssign component for this text engages students with an interactive eBook and a diverse set of assessment questions with real-world data sets and examples covering a variety of major-specific interests. This provides the context students need to connect the dots to the statistical concepts at hand.

## Product Features

• Read It links under each question quickly jump to the corresponding section of a complete, interactive eBook that lets students highlight and take notes as they read.

## Homework Question Features

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

## Questions Available within WebAssign

All learning objective-based questions correlated to this textbook are available in WebAssign. Whenever possible, variables, numbers, or words have been randomized so that each student receives a unique version of the question. This list is updated nightly.

##### Question Group Key
DS: Data Set Problem
CH: Challenge Problem
CHDS: Challenge Data Set Problem

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

Group Quantity Questions
Chapter 1: Collecting Data In Reasonable Ways
1.1 10 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007 LO.008 LO.009.CH LO.010.CH
1.2 66 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007 LO.008.DS LO.009.CH LO.010.CH LO.011 LO.012 LO.013 LO.014 LO.015.DS LO.016 LO.017 LO.018.CH LO.019 LO.020.CH LO.021 LO.022 LO.023 LO.024 LO.025.CH LO.026.CH LO.027 LO.028 LO.029 LO.030 LO.031.CHDS LO.032 LO.033 LO.034.CH LO.035 LO.036 LO.037 LO.038 LO.039 LO.040 LO.041.CHDS LO.042 LO.043 LO.044.CH LO.045 LO.046.CH LO.047 LO.048 LO.049 LO.050.CH LO.051.DS LO.052 LO.053.CH LO.054.CH LO.055.CH LO.056.CH LO.057 LO.058 LO.059 LO.060 LO.061 LO.062 LO.063 LO.064.DS LO.065.CH LO.066.CH
Chapter 2: Graphical Methods for Describing Data Distributions
2.1 17 LO.001 LO.002 LO.003.DS LO.004 LO.005 LO.006 LO.007.CH LO.008 LO.009 LO.010.DS LO.011 LO.012 LO.013.DS LO.014 LO.015 LO.016.DS LO.017
2.2 11 LO.001 LO.002 LO.003.DS LO.004.CHDS LO.005.CHDS LO.006.CHDS LO.007 LO.008 LO.009.CHDS LO.010.CHDS LO.011.CHDS
2.3 52 LO.001 LO.002.DS LO.003.DS LO.004.DS LO.005.DS LO.006.DS LO.007.DS LO.008.DS LO.009.CHDS LO.010.CHDS LO.011 LO.012 LO.013 LO.014.CHDS LO.015.CHDS LO.016.CHDS LO.017 LO.018 LO.019 LO.020.DS LO.021.CHDS LO.022.CHDS LO.023.DS LO.024 LO.025 LO.026.DS LO.027 LO.028 LO.029 LO.030 LO.031.CHDS LO.032.CHDS LO.033.CHDS LO.034.CHDS LO.035 LO.036 LO.037.DS LO.038.DS LO.039.CHDS LO.040.CHDS LO.041 LO.042 LO.043 LO.044 LO.045.CHDS LO.046.CHDS LO.047 LO.048 LO.049.DS LO.050.DS LO.051.CHDS LO.052.CHDS
2.4 6 LO.001 LO.002 LO.003 LO.004.DS LO.005.CHDS LO.006.CHDS
2.5 12 LO.001 LO.002 LO.003 LO.004.DS LO.005.CHDS LO.006.CHDS LO.007 LO.008 LO.009 LO.010 LO.011.CH LO.012.CH
Chapter 3: Numerical Methods for Describing Data Distributions
3.2 11 LO.001.DS LO.002 LO.003 LO.004.DS LO.005 LO.006.DS LO.007.DS LO.008.DS LO.009.DS LO.010.CHDS LO.011.CHDS
3.3 23 LO.001 LO.002 LO.003 LO.004.CH LO.005.CHDS LO.006.CH LO.007.DS LO.008 LO.009 LO.010.DS LO.011.DS LO.012.CHDS LO.013.CHDS LO.014 LO.015 LO.016 LO.017.DS LO.018.DS LO.019.DS LO.020.CHDS LO.021.DS LO.022.DS LO.023.CHDS
3.4 10 LO.001 LO.002 LO.003 LO.004.DS LO.005.DS LO.006.DS LO.007.CHDS LO.008.DS LO.009.DS LO.010.CHDS
3.5 10 LO.001 LO.002.DS LO.003.DS LO.004.DS LO.005.DS LO.006.DS LO.007.CHDS LO.008.DS LO.009.DS LO.010.CHDS
Chapter 4: Describing Bivariate Numerical Data
4.1 20 LO.001 LO.002 LO.003.DS LO.004.DS LO.005.DS LO.006.DS LO.007.DS LO.008.DS LO.009.CHDS LO.010.CHDS LO.011 LO.012 LO.013.DS LO.014 LO.015.DS LO.016.DS LO.017.DS LO.018.DS LO.019.CHDS LO.020.CHDS
4.2 10 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CH LO.008.CHDS LO.009.CHDS LO.010.CHDS
4.3 16 LO.001 LO.002 LO.003 LO.004 LO.005.CHDS LO.006.CHDS LO.007 LO.008 LO.009 LO.010.DS LO.011 LO.012.DS LO.013 LO.014 LO.015.CHDS LO.016.CHDS
4.5 6 LO.001 LO.002 LO.003 LO.004.CH LO.005.CHDS LO.006.CHDS
Chapter 5: Probability
5.1 10 LO.001.DS LO.002.DS LO.003 LO.004 LO.005.DS LO.006.DS LO.007.DS LO.008.DS LO.009.CH LO.010.CHDS
5.2 19 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CHDS LO.008 LO.009 LO.010.CH LO.011 LO.012 LO.013 LO.014 LO.015 LO.016 LO.017 LO.018.CH LO.019.CH
5.3 60 LO.001 LO.002.DS LO.003 LO.004 LO.005.DS LO.006.DS LO.007 LO.008 LO.009.CHDS LO.010.CH LO.011 LO.012 LO.013 LO.014 LO.015.DS LO.016 LO.017 LO.018.CHDS LO.019.CHDS LO.020.CH LO.021 LO.022 LO.023 LO.024.DS LO.025.DS LO.026.DS LO.027.CH LO.028.DS LO.029.CHDS LO.030.CH LO.031 LO.032 LO.033 LO.034 LO.035.DS LO.036.CHDS LO.037 LO.038.DS LO.039.CH LO.040.CH LO.041 LO.042 LO.043 LO.044 LO.045.DS LO.046 LO.047 LO.048.CH LO.049 LO.050.CH LO.051 LO.052 LO.053 LO.054 LO.055 LO.056 LO.057 LO.058 LO.059.CHDS LO.060.CHDS
5.4 10 LO.001.DS LO.002.DS LO.003.DS LO.004.DS LO.005.DS LO.006.DS LO.007.DS LO.008.DS LO.009.CHDS LO.010.CHDS
5.6 18 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CH LO.008 LO.009 LO.010.CH LO.011 LO.012 LO.013 LO.014 LO.015 LO.016.CH LO.017.CH LO.018.CH
Chapter 6: Random Variables and Probability Distributions
6.1 3 LO.001 LO.002 LO.003.CH
6.2 10 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007 LO.008.CH LO.009.CH LO.010.CHDS
6.3 6 LO.001 LO.002 LO.003 LO.004 LO.005.CH LO.006.CH
6.4 30 LO.001 LO.002 LO.003.DS LO.004 LO.005 LO.006.DS LO.007 LO.008.CHDS LO.009.CHDS LO.010 LO.011 LO.012 LO.013 LO.014 LO.015 LO.016 LO.017 LO.018 LO.019.CHDS LO.020.CH LO.021 LO.022 LO.023 LO.024 LO.025 LO.026 LO.027 LO.028 LO.029.CHDS LO.030.CH
6.5 30 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007 LO.008.CH LO.009.CH LO.010.CH LO.011 LO.012 LO.013 LO.014 LO.015 LO.016 LO.017 LO.018 LO.019.CH LO.020.CH LO.021 LO.022 LO.023 LO.024 LO.025 LO.026 LO.027 LO.028 LO.029.CH LO.030.CH
6.7 38 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CH LO.008 LO.009 LO.010.CH LO.011 LO.012 LO.013 LO.014 LO.015.DS LO.016.DS LO.017.DS LO.018 LO.019.CH LO.020.CH LO.021 LO.022 LO.023 LO.024 LO.025 LO.026 LO.027.CH LO.028.CH LO.029.CH LO.030.CH LO.031 LO.032 LO.033 LO.034 LO.035 LO.036 LO.037.CH LO.038.CH
6.8 6 LO.001 LO.002 LO.003 LO.004 LO.005.CH LO.006.CH
Chapter 7: An Overview of Statistical Inference—Learning from Data
7.1 11 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CH LO.008.CH LO.009 LO.010 LO.011
Chapter 8: Sampling Variability and Sampling Distributions
8.2 6 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006
Chapter 9: Estimating a Population Proportion
9.2 8 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CH LO.008.CHDS
9.3 10 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CHDS LO.008 LO.009 LO.010.CH
9.4 10 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007 LO.008.DS LO.009.CH LO.010.CH
10.1 10 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007 LO.008 LO.009.CHDS LO.010.CH
10.2 10 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CH LO.008 LO.009 LO.010.CH
10.4 2 LO.001 LO.002
10.5 7 LO.001 LO.002 LO.003.DS LO.004.CH LO.005.CH LO.006.CH LO.007.CH
11.1 8 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006.CH LO.007.CH LO.008.CH
11.2 18 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.DS LO.008 LO.009.CH LO.010.CH LO.011 LO.012 LO.013 LO.014 LO.015 LO.016 LO.017.CHDS LO.018.CHDS
11.3 1 LO.001
12.1 10 LO.001 LO.002 LO.003 LO.004 LO.005.DS LO.006.DS LO.007.DS LO.008 LO.009.CHDS LO.010.CH
12.2 30 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007 LO.008 LO.009 LO.010 LO.011 LO.012 LO.013 LO.014 LO.015 LO.016 LO.017 LO.018.DS LO.019.CH LO.020.CH LO.021 LO.022 LO.023 LO.024 LO.025 LO.026.CHDS LO.027 LO.028.DS LO.029 LO.030.CHDS
12.3 20 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007 LO.008 LO.009.CHDS LO.010.CH LO.011 LO.012 LO.013 LO.014 LO.015 LO.016 LO.017 LO.018 LO.019.CH LO.020.CH
13.1 15 LO.001 LO.002 LO.003 LO.004 LO.005 LO.006 LO.007.CH LO.008 LO.009 LO.010 LO.011 LO.012 LO.013 LO.014 LO.015.CH
13.2 10 LO.001 LO.002 LO.003 LO.004 LO.005.DS LO.006 LO.007.DS LO.008 LO.009.CH LO.010.CHDS
13.3 14 LO.001 LO.002 LO.003.DS LO.004.CH LO.005 LO.006 LO.007 LO.008.CHDS LO.009 LO.010.CH LO.011.CHDS LO.012.CH LO.013.CH LO.014.CHDS
Chapter 14: Learning from Experimental Data
14.2 1 LO.001
14.3 6 LO.001 LO.002.CHDS LO.003.CH LO.004.CH LO.005.CHDS LO.006.CH
Chapter 15: Learning from Categorical Data
15.1 10 LO.001 LO.002 LO.003.DS LO.004 LO.005.DS LO.006 LO.007 LO.008.DS LO.009.CHDS LO.010.CHDS
15.2 20 LO.001.DS LO.002.DS LO.003.DS LO.004.DS LO.005.DS LO.006.DS LO.007.DS LO.008.DS LO.009.CHDS LO.010.CHDS LO.011.DS LO.012.DS LO.013.DS LO.014.DS LO.015.DS LO.016.DS LO.017.DS LO.018.DS LO.019.CHDS LO.020.CHDS
Chapter 16: Understanding Relationships—Numerical Data Part 2
16 0