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Purchase Benefits
What is included with this book?
For a one-semester alternative to the traditional two-semester developmental algebra sequence that prepares students specifically for an Introductory Statistics course.
Looking for a new path in algebra?
Using authentic data to make math meaningful to students, Jay Lehmann’s A Pathway to Introductory Statistics provides a one-semester alternate path through developmental algebra to accelerate and prepare non-STEM students for introductory statistics. For many students’ majors, the most fitting college-level math course is statistics. Tailoring their developmental sequence–in both content and approach–to prepare students for this course of study can only improve their success. Infused with highly relevant data sets throughout, Lehmann presents students with both an introduction to descriptive statistics and the requisite algebra topics needed for a statistics course, while demonstrating the close link between the two subjects. This text equips students to reason statistically as they discover the skills and concepts they’ll need for statistics.
Note: You are purchasing a standalone product; MyMathLab does not come packaged with this content. Students, if interested in purchasing this title with MyMathLab, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.
If you would like to purchase boththe physical text and MyMathLab, search for:
0134310039 / 9780134310039 Pathway to Introductory Statistics, A, Access Card Package
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0134107179 / 9780134107172 Pathway to Introductory Statistics, A
0321431308 / 9780321431301 MyMathLab -- Glue-in Access Card
0321654064 / 9780321654069 MyMathLab Inside Star StickerJay Lehmann has taught at College of San Mateo for more than twenty years, where he has received the Shiny Apple Award for excellence in teaching. He has worked on a NSF-funded grant to study classroom assessment and has performed research on collaborative directed-discovery learning. Jay has served as the newsletter editor for CMC3 (California Mathematics Council, Community College) for twelve years. He has presented at more than seventy-five conferences, including AMATYC, ICTCM, and T3, where he has discussed curve fitting and sung his "Number Guy" song.
Jay plays in a rock band called The Procrastinistas, who play at various clubs in the San Francisco Bay Area, where Jay, his wife Keri, and son Dylan reside. He plays a number of instruments including bass, guitar, piano, violin, and baritone. In addition to his elementary, intermediate, and combined algebra textbooks, Jay is currently writing a heist novel for high school students, which he hopes will be published before Dylan outgrows it. Dylan, a devoted drummer and artist, drafted many of the cartoons that are included in Jay's textbooks.
In the words of the author:
Before writing my algebra series, it was painfully apparent that my students couldn't relate to the applications in the course. I was plagued with the question, "What is this good for?" To try to bridge that gap, I wrote some labs, which facilitated my students in collecting data, finding models via curve fitting, and using the models to make estimates and predictions. My students really loved working with the current, compelling, and authentic data and experiencing how mathematics truly is useful.
My students' response was so strong that I decided to write an algebra series. Little did I know that to realize this goal, I would need to embark on a 15-year challenging journey, but the rewards of hearing such excitement from students and faculty across the country has made it all worthwhile! I'm proud to have played even a small role in raising peoples' respect and enthusiasm for mathematics.
I have tried to honor my inspiration: by working with authentic data, students can experience the power of mathematics. A random-sample study at my college suggests that I am achieving this goal. The study concludes that students who used my series were more likely to feel that mathematics would be useful in their lives (P-value 0.0061) as well as their careers (P-value 0.024).
The series is excellent preparation for subsequent courses; in particular, because of the curve fitting and emphasis on interpreting the contextual meaning of parameters, it is an ideal primer for statistics. In addition to curve fitting, my approach includes other types of meaningful modeling, directed-discovery explorations, conceptual questions, and of course, a large bank of skill problems. The curve-fitting applications serve as a portal for students to see the usefulness of mathematics so that they become fully engaged in the class. Once involved, they are more receptive to all aspects of the course.
Chapter 1 Performing Operations and Evaluating Expressions
1.1 Variables, Constants, Plotting Points, Inequalities
1.2 Expressions
1.3 Operations with Fractions and Proportions; Converting Units
1.4 Absolute Value and Adding Real Numbers
1.5 Change in a Quantity and Subtracting Real Numbers
1.6 Ratios, Percents, and Multiplying and Dividing Real Numbers
1.7 Exponents, Square Roots, Order of Operations, and Scientific Notation
Chapter 2 Designing Observational Studies and Experiements
2.1 Simple Random Sampling
2.2 Systematic, Stratified, and Cluster Sampling
2.3 Observational Studies and Experiments
Chapter 3 Graphical and Tabular Displays of Data
3.1 Frequency Tables, Relative Frequency Tables, and Bar Graphs
3.2 Pie Charts and Two-Way Tables
3.3 Dotplots, Stemplots, and Time-Series Plots
3.4 Histograms
3.5 Misleading Graphical Displays of Data
Chapter 4 Summarizing Data Numerically
4.1 Measures of Center
4.2 Measures of Spread
4.3 Boxplots
Chapter 5 Computing Probabilities
5.1 Meaning of Probability
5.2 Complement and Addition Rules
5.3 Conditional Probability and the Multiplication Rule for Independent Events
5.4 Finding Values for a Normal Distribution
5.5 Finding Values of Variables for Normal Distributions
Chapter 6 Describing Associations of Two Variables Graphically
6.1 Scatterplots
6.2 Determining the Four Characteristics of an Association
6.3 Modeling Linear Associations
Chapter 7 Graphing Equations of Lines and Linear Models; Rate of Change
7.1 Graphing Equations of Lines and Linear Models
7.2 Rate of Change
7.3 Using Slope to Graph Equations Lines and Linear Models
7.4 Functions
Chapter 8 Solving Linear Equations and Inequalities to Make Predictions
8.1 Simplifying Expressions
8.2 Solving Linear Equations in One Variable
8.3 Solving Linear Equations to Make Predictions
8.4 Solving Formulas
8.5 Solving Linear Inequalities to Make Predictions
Chapter 9 Finding Equations of Linear Models
9.1 Using Two Points to Determine an Equation of a Line
9.2 Using Two Points to Find an Equation of a Linear Model
9.3 Linear Regression Model
Chapter 10 Using Linear Functions and Formulas to Make Predictions
10.1 Statistics, Geometry, and Algebra Formulas
10.2 Graphing Linear Equations
10.3 Functions
10.4 Using Function Notation to Make Predicions