# Statistics: The Art and Science of Learning from Data 1st edition

Alan Agresti and Christine Franklin
Publisher: Pearson Education

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

Higher Education Single Term \$29.95
High School \$10.50

Online price per student per course or lab, bookstore price varies. Access cards can be packaged with most any textbook, please see your textbook rep or contact WebAssign

• Chapter 1: Statistics: The Art and Science of Learning from Data
• 1.1: How Can You Investigate Using Data? (4)
• 1.2: We Learn about Populations Using Samples (4)
• 1.3: What Role do Computers Play in Statistics? (2)
• 1: Chapter Problems (2)

• Chapter 2: Exploring Data with Graphs and Numerical Summaries
• 2.1: What Are the Types of Data? (5)
• 2.2: How Can We Describe Data Using Graphical Summaries? (7)
• 2.3: How Can We Describe the Center of Quantitative Data? (7)
• 2.4: How Can We Describe the Spread of Quantitative Data? (6)
• 2.5: How Can Measures of Position Describe Spread? (8)
• 2.6: How Are Descriptive Summaries Misused? (2)
• 2: Chapter Problems (10)

• Chapter 3: Association: Contingency, Correlation, and Regression
• 3.1: How Can We Explore the Association between Two Categorical Variables? (5)
• 3.2: How Can We Explore the Association between Two Quantitative Variables? (6)
• 3.3: How Can We Predict the Outcome of a Variable? (5)
• 3.4: What are Some Cautions in Analyzing Associations? (6)
• 3: Chapter Problems (10)

• Chapter 4: Gathering Data
• 4.1: Should We Experiment of Should We Merely Observe? (5)
• 4.2: What Are Good Ways and Poor Ways to Sample? (6)
• 4.3: What Are Good Ways and Poor Ways to Experiment? (3)
• 4.4: What Are Other Ways to Perform Experimental and Observational Studies? (4)
• 4: Chapter Problems (11)

• Chapter 5: Probability in Our Daily Lives
• 5.1: How Can Probability Quantify Randomness? (3)
• 5.2: How Can We Find Probabilities? (6)
• 5.3: Conditional Probability: What's the Probability of A, Given B? (9)
• 5.4: Applying the Probability Rules (8)
• 5: Chapter Problems (6)

• Chapter 6: Probability Distributions
• 6.1: How Can We Summarize Possible Outcomes and Their Probabilities? (5)
• 6.2: How Can We Find Probabilites for Bell-Shaped Distributions? (8)
• 6: How Can We Find Probabilities When Each Observation Has Two Possible Outcomes? (6)
• 6.4: How Likely Are the possible Values of a Statistic? The Sampling Distribution (5)
• 6.5: How Close Are Sample Means to Population Means? (3)
• 6.6: How Can We Make Inferences About a Population? (2)
• 6: Chapter Problems (9)

• Chapter 7: Statistical Inference: Confidence Intervals
• 7.1: What Are Point and Interval Estimates of Population Parameters? (4)
• 7.2: how Can We Construct a Confidence Interval to Estimate a Population Proportion? (5)
• 7.3: How Can We Construct a Confidence Interval to Estimate a Population Mean? (6)
• 7.4: How Do We Choose the Sample Size for a Study? (5)
• 7.5: How Do computers Make New Estimation Methods Possible? (1)
• 7: Chapter Problems

• Chapter 8: Statistical Inference: Significance Tests About Hypotheses
• 8.1: What Are the Steps for Performing a Signigicance Test? (2)
• 8.2: Significance Tests About Proportions (4)
• 8.3: Significance Tests About Means (2)
• 8.4: Decisions and Types of Errors in Significance Tests (2)
• 8.5: Limitations of Significance Tests (2)
• 8.6: How Likely Is a Type II Error (Not Rejecting Ho, Even though it's False)? (1)
• 8: Chapter Problems

• Chapter 9: Comparing Two Groups
• 9.1: Categorical Response: How Can We Compare Two Proportions? (6)
• 9.2: Quantitative Response: How Can We Compare Two Means? (6)
• 9.3: Other Ways of Comparing Means and Comparing Proportions (5)
• 9.4: How Can We Analyze Dependent Samples? (2)
• 9.5: How Can We Adjust for Effects of Other Variables? (2)
• 9: Chapter Problems

• Chapter 10: Analyzing the Association Between Categorical Variables
• 10.1: What Is Independence and What Is Association?
• 10.2: How Can We Test Whether Categorical Variables are Independent?
• 10.3: How Strong Is the Association?
• 10.4: How Can Residuals Reveal the Pattern of Association?
• 10.5; What is the Sample Size is Small? Fisher's Exact Test
• 10: Chapter Problems

• Chapter 11: Analyzing Association Between Quantitative Variables: Regression Analysis
• 11.1: How Can We "Model" How Two Variables Are Related?
• 11.2: How Can We Describe Strength of Association?
• 11.3: How Can We Make Inferences About the Association?
• 11.4: What Do We Learn from How the Data Vary Around the Regression Line?
• 11.5: Exponential Regression: A Model for Nonlinearity
• 11: Chapter Problems

• Chapter 12: Multiple Regression
• 12.1: How Can We Use Several Variables to Predict a Response?
• 12.2: Extending the Correlation and R-Squared for Multiple Regression
• 12.3: How Can We use multiple Regression to Make Inferences?
• 12.4: Checking a Regression Model Using Residual Plots
• 12.5: How Can Regression Include Categorical Predictors?
• 12.6: How Can We Model a Categorical Response?
• 12: Chapter Problems

• Chapter 13: Comparing Groups: Analysis of Variance Methods
• 13.1: How Can We Compare Several Means? One-Way ANOVA
• 13.2: How Should We Follow Up and ANOVA F Test?
• 13.3: What if There Are Two Factors? Two Way ANOVA
• 13: Chapter Problems

• Chapter 14:Nonparametric Statistics
• 14.1: How Can We Compare Two Groups by Ranking?
• 14.2: Nonparametric Methods for Several Groups and for Matched Pairs
• 14: Chapter 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
E - End of chapter Exercise

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

Group Quantity Questions
Chapter 1: Statistics: The Art and Science of Learning from Data
1.E 12 001 003 003.alt 004 007 009 010 013 016 017 022 026
Chapter 2: Exploring Data with Graphs and Numerical Summaries
2.E 45 003 004 006 007 008 010 011 015 016 021 023 025 029 030 031 034 035 038 041 043 044 046 047 049 051 058 062 063 064 067 069 070 071 074 077 081 085 092 094 101 102 105 108 111 115
Chapter 3: Association: Contingency, Correlation, and Regression
3.E 32 001 003 005 006 007 010 013 014 015 016 020 024 026 028 029 033 039 042 043 047 048 050 057 059 060 064 067 069 071 080 082 093
Chapter 4: Gathering Data
4.E 29 001 005 007 008 009 014 015 020 021 025 029 032 034 036 039 040 042 048 052 057 064 067 069 072 074 075 087 090 096
Chapter 5: Probability in Our Daily Lives
5.E 32 004 006 008 013 015 018 019 023 025 026 027 028 029 031 033 034 036 042 043 044 048 049 050 052 054 056 062 065 067 068 070 079
Chapter 6: Probability Distributions
6.E 38 003 005 006 009 010 013 015 016 017 019 021 023 027 029 031 032 035 036 037 045 046 047 048 050 053 057 060 062 063 067 071 074 086 091 098 106 109 115
Chapter 7: Statistical Inference: Confidence Intervals
7.E 21 004 006 007 008 012 013 015 020 021 025 027 029 030 035 040 042 044 047 048 051 055
Chapter 8: Statistical Inference: Significance Tests About Hypotheses
8.E 13 004 008 011 015 019 021 029 033 042 043 046 048 058
Chapter 9: Comparing Two Groups
9.E 21 001 002 003 004 005 011 012 016 019 021 023 024 026 028 029 032 037 039 048 052 054
Chapter 10: Analyzing the Association Between Categorical Variables
10 0
Chapter 11: Analyzing Association Between Quantitative Variables: Regression Analysis
11 0
Chapter 12: Multiple Regression
12 0
Chapter 13: Comparing Groups: Analysis of Variance Methods
13 0
Chapter 14:Nonparametric Statistics
14 0
Total 243