MyLab Statistics for Statistical Reasoning -- Student Access Kit

The course is designed for providing statistics students with a deeper understanding of statistical concepts through hands-on and discovery-based learning.

MyStatLab for Statistical Reasoning is part of the series of MyMathLab/MyStatLab courses built to support the New Mathways Project developed by the Charles A. Dana Center. The New Mathways Project embodies the Dana Center’s vision for a systemic approach to improving student success and completion through implementation of processes, strategies, and structures built around three mathematics pathways and a supporting student success course.MyStatLab for Statistical Reasoning is the college-level course in the Statistics pathway for non-STEM students, designed with an active-learning approach that connects statistical concepts to hands-on and discovery-based activities for students. The MyStatLab course designed for use with Statistical Reasoning provides:

• Interactive content to help prepare students for active classroom time
• In-Class Interactive Lessons to support students through an active classroom experience, accompanied by notebook PDFs.
• Homework assignments designed to assess conceptual understanding of important skills and concepts.
• Additional resources for instructors to help facilitate an interactive and engaging classroom

Built in MyStatLab

Content developed by the Charles A. Dana Center at The University of Texas at Austin will be delivered through MyStatLab. MyStatLab is an online homework, tutorial, and assessment program that engages students and improves results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts.

MyStatLab for Statistical Reasoning was developed by the Charles A. Dana Center at The University of Texas—Austin. The Dana Center brings together experienced faculty from two- and four-year institutions to author, review, field-test, and revise the New Mathways Project curricular materials.

The Dana Center develops and scales effective math and science innovations to support educators, administrators, and policy makers in creating seamless transitions throughout the K14 system for all students. Their work, based on research and two decades of experience, focuses on K—16 mathematics and science education with an emphasis on strategies for improving student engagement, motivation, persistence, and achievement. They develop innovative curricula, tools, protocols, and instructional supports and deliver powerful instructional and leadership development.

Curriculum Overview

Before the Semester Begins: Suggestions for Prep Week

Lesson 1: Introduction to Statistics

1.A: Birth Dates and Personality Traits

1.B: Our Learning Community

1.C: Statistical Investigations

1.D: The Statistical Analysis Process

1.E: Types of Statistical Studies and Scope of Conclusions

Lesson 2: Sampling

2.A: Collecting Data by Sampling

2.B: Random Sampling

2.C: Other Sampling Methods (Optional)

2.D: Sources of Bias in Sampling

Lesson 3: Experiments

3.A: Planning an Experiment

3.B: Random Assignment

3.C: Control Groups and Placebos

3.D: Drawing Conclusions from Statistical Studies

3.E: Forming Effective Study Groups

Lesson 4: Univariate Data Displays and Measures of Center

4.A: Dotplots for Quantitative Data

4.B: Constructing Histograms for Quantitative Data

4.C: Weight Gain

4.D: Dotplots, Histograms, and Distributions

Lesson 5: Variability

5.A: Variability Relative to the Mean

5.B: Boxplots

5.C: The Modified Boxplot

5.D: Do the Parties Differ?

Lesson 6: Scatterplots and Correlation

6.A: Introduction to Two-Variable Relationships

6.B: Direction and Strength of the Relationship

6.C: Correlation Coefficient

6.D: Correlation and Cause-and-Effect Conclusions

Lesson 7: Lines of Best Fit

7.A: Using Lines to Make Predictions

7.B: Exploring Lines of Best Fit

7.C: Least Squares Regression (LSR) Line

7.D: Impact of Outliers on Correlations and Regression

Lesson 8: Least Squares Regression Line

8.A: Investigating the Numbers in a Line’s Equation

8.B: Building the LSR Line’s Equation (Optional)

8.C: Determining If a Line Is a Good Fit

8.D: Determining If a Line Is an Appropriate Model

Lesson 9: Bivariate Categorical Data

9.A: Categorical Data and Two-Way Tables

9.B: Comparative Bar Charts

9.C: Case Study

Lesson 10: Probability

10.A: Introduction to Probability

10.B: Study on Blood Pressure

10.C: Probability Rules

10.D: Conditional Probability and Independence

10.E: The Multiplication and Addition Rules

Lesson 11: Probability Distributions

11.A: Simulation

11.B: Probability Distributions of Discrete Random Variables

11.C: Probability Distributions of Continuous Random Variables

11.D: Constructing Probability Distributions

Lesson 12: The Normal Distribution

12.A: The Normal Distribution

12.B: z-Scores and Normal Distributions

12.C: Probability and Critical Values

12.D: Probability and Critical Values (continued)

Lesson 13: Sampling Variability

13.A: Sampling Variability

13.B: What Is a Sampling Distribution?

13.C: The Sampling Distribution of a Sample Proportion

13.D: Effect of Sample Size and the Value of the Population Proportion

13.E: The Standard Error

Lesson 14: Estimating a Population Proportion

14.A: Estimating a Population Proportion

14.B: Calculating a Margin of Error

14.C: Evaluating a Claim–Part 1

14.D: Evaluating a Claim–Part 2

Lesson 15: Confidence Intervals for a Population Proportion

15.A: Confidence Intervals and Confidence Levels

15.B.A: Confidence Interval for a Population Proportion

15.C: Interpreting Confidence Intervals and Margin of Error

Lesson 16: Hypothesis Testing About a Population Proportion

16.A: The Logic of Hypothesis Testing

16.B: Steps in Hypothesis Testing

16.C: Testing Hypotheses About a Population Proportion–Part 1

16.D: Testing Hypotheses About a Population Proportion–Part 2

Lesson 17: Sampling Distribution of a Sample Mean

17.A: Sampling Distribution of a Sample Mean

17.B: Effect of Sample Size and Variability in the Population

17.C: General Properties of the Sampling Distribution of a Sample Mean

Lesson 18: Inference About a Population Mean

18.A: t-Distributions

18.B: One Sample Confidence Interval for a Population Mean–Part 1

18.C: One Sample Confidence Interval for a Population Mean–Part 2

18.D: One Sample Hypothesis Test for a Population Mean–Part 1

18.E: One Sample Hypothesis Test for a Population Mean–Part 2

Lesson 19: Comparing Two Populations

19.A: Hypotheses About a Difference in Population Means or Proportions

19.B: Two-Sample Test for a Difference in Population Proportions

19.C: Paired Versus Independent Samples

19.D: Two-Sample Test for a Difference in Population Means Using Independent Samples

19.E: Inference for Paired Samples

19.F: Two-Sample Confidence Interval for a Difference in Population Proportions

19.G: Two-Sample Confidence Interval for a Difference in Population Means

Lesson 20: Analysis of Categorical Data: Chi-Square Goodness of Fit (Optional)

20.A: Quantifying the Strength of the Evidence

20.B: Conducting the Chi-Square Test

20.C: The Chi-Square Test and Degrees of Freedom

Lesson 21: Analysis of Categorical Data: Chi-Square Tests for Two-Way Tables (Optional)

21.A: Introduction to Chi-Square Tests for Two-Way Tables

21.B: Chi-Square Test for Independence

21.C: Chi-Square Test for Homogeneity

Lesson 22: Inference for Regression (Optional)

22.A: Sampling Distributions of Sample Slopes

22.B: Confidence Intervals for a Population Slope

22.C: Testing Claims About a Population Slope

Lesson 23: One-Way Analysis of Variance (Optional)

23.A: One-Way Analysis of Variance–Equal Sample Sizes

23.B: One-Way Analysis of Variance–Unequal Sample Sizes

23.C: Interpreting a One-Way Analysis of Variance

Answers (Odd)

Resources

• 5-Number Summary and Boxplots
• Categorical Data
• The Chi-Square (χ²) Distribution
• The Chi-Square (χ²) Goodness-of-Fit Test
• The Chi-Square (χ²) Test for Homogeneity
• The Chi-Square (χ²) Test for Independence
• Confidence Intervals for Means
• Confidence Intervals for Proportions
• Experimental Studies
• Fractions, Decimals, and Percentages
• Hypothesis Testing for Means
• Hypothesis Testing for Proportions
• Inference for Regression
• Introduction to Statistical Studies
• Mean, Mode, and Median
• The Normal Distribution
• One-Way Analysis of Variance
• Probability Rules
• Sampling Methods
• t-Distributions
• Technology: Summary Statistics and LSR Line
• Variance and Standard Deviation
• Writing Principles

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