Print Version

Effective: Summer 2015

Prerequisites: Prerequisites: Satisfactory score on the mathematics placement test; MATH 230 or 230J.
Advisory: Advisory: Demonstrated proficiency in English by placement as determined by score on the English placement test OR through an equivalent placement process OR completion of ESLL 125 & ESLL 249.
Grade Type: Letter Grade, the student may select Pass/No Pass
Not Repeatable.
FHGE: Non-GE Transferable: None
10 hours lecture. (120 hours total per quarter)

Student Learning Outcomes -
  • Students will investigate the center, shape, and spread of distributions from many relevant contexts.
  • Students will develop conceptual understanding of populations, samples, and sampling distributions. They will demonstrate and communicate this understanding in a variety of ways, such as: reasoning with definitions and theorems, connecting concepts, and connecting multiple representations, as appropriate.
  • Students will demonstrate the ability to calculate probabilities, descriptive statistics, and z-scores.
Description -
The first of two courses in the Statway sequence. Covers concepts and methods of statistics with an emphasis on data analysis. Topics include methods for collecting data, graphical and numerical descriptive statistics, correlation, simple linear regression, basic concepts of probability, confidence intervals and hypothesis tests for means and proportions, chi-square tests, and ANOVA. Application problems will be taken from the fields of business, economics, medicine, engineering, education, psychology, sociology and from culturally diverse situations. This sequence is recommended for students with majors that require no mathematics beyond freshman-level statistics.

Course Objectives -
The student will be able to:
  1. Examine statistical studies and discuss an overview of the data analysis process.
  2. Analyze data graphically and numerically.
  3. Examine, use, and interpret bivariate data.
  4. Use and manipulate linear functions and expressions.
  5. Use and manipulate exponential functions and expressions.
  6. Compute basic probabilities.
  7. Use appropriate technology as a tool for doing statistics.
  8. Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including business, social sciences, psychology, life science, health science and education.
Special Facilities and/or Equipment -
  1. Graphing Calculator
  2. Access to Microsoft Excel software or the equivalent

Course Content (Body of knowledge) -
  1. Examine statistical studies and discuss an overview of the data analysis process.
    1. Types of statistical studies
      1. Observational
      2. Experimental
    2. Sampling methodologies and bias
      1. Simple random sampling
      2. Stratified sampling
      3. Systematic sampling
      4. Convenience sampling
    3. Experimental design
      1. Random assignment
      2. Lurking variables
      3. Confounding variables
    4. Data analysis process
      1. Formulate question
      2. Identify appropriate data
      3. Select an appropriate data collection strategy
      4. Collect, summarize, display data
      5. Draw a conclusion
      6. Interpret in context
    5. Vocabulary
      1. Variables
      2. Population
      3. Sample
      4. Quantitative
      5. Categorical
      6. Study
      7. Experiment
  2. Analyze data graphically and numerically.
    1. Graphical displays
      1. Bar charts
      2. Dot plots
      3. Histograms
      4. Box plots
    2. Measures of center
      1. Mean
      2. Median
      3. Mode
    3. Measure of variability
      1. Range
      2. Variance
      3. Standard deviation
    4. Measures of relative standing
      1. Percentiles
      2. Quartiles
    5. Comparing distributions
      1. Graphically
      2. Numerically
    6. Numeracy
      1. Ordering
      2. Comparing
      3. Estimating
      4. Rounding
      5. Units
      6. Proportional reasoning
      7. Unit analysis
      8. Rational numbers
      9. Square root of a number
      10. Exponents
      11. Solving equations
      12. Scientific notation
  3. Examine, use, and interpret bivariate data.
    1. Scatter plots
      1. Form
      2. Interpretations
      3. Residuals
    2. Correlation
      1. Strength
      2. Positive
      3. Negative
    3. Linear regression
      1. Interpretations
      2. Extrapolation
      3. Interpolation
    4. Linear and exponential models
      1. Interpret parameters
      2. Make predictions
      3. Multiple representations
        1. Tables
        2. Graphs
        3. Symbolic form
      4. Application problems
      5. Comparing models
      6. Residual Plots
  4. Use and manipulate linear functions and expressions
    1. 1-variable linear equations
      1. solve algebraically
    2. 1-variable inequalities
      1. graphs
    3. Linear Functions
      1. slope
      2. y-intercept
      3. equation of a line y=mx+b
      4. Interpretations
        1. slope
        2. y-intercept
  5. Use and manipulate exponential expressions and functions
    1. Indentify exponential expressions
    2. Apply properties of exponents
    3. Solve exponential equations
      1. graphically
      2. algebraically
      3. tables
    4. Graph exponential functions
  6. Compute basic probabilities
    1. Empirical probability
    2. Contingency tables
      1. Conditional probability
      2. Independence
      3. Dependence
    3. Probability rules
  7. Use appropriate technology as a tool for doing statistics.
    1. Computer lab assignments
    2. Excel
  8. Discuss mathematical problems and write solutions in accurate mathematical language and notation.
    1. Application problems from various disciplines
    2. Proper notation
  9. Interpret mathematical solutions.
    1. Explain the significance of solutions to application problems
Methods of Evaluation -
  1. Written homework
  2. Quizzes
  3. Midterms or module exams
  4. Proctored comprehensive final examination
Representative Text(s) -
Carnegie Foundation., 2011.
Richelle M. Blair.Introductory Algebra. Boston: Pearson Addison Wesley, 2006.
The Consortium for Foundation Mathematics, Mathematics in Action: an Introduction to Algebraic, graphical, and numerical problem solving. 3rd edition. Boston: Pearson Addison Wesley, 2008.
Martin-Gay, Green. Intermediate Algebra: A Graphing Approach, 4th ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2009.
Brase, H. and C. Brase. Understandable Statistics: Concepts and Methods. 9th ed. Houghton Mifflin, 2009.

Disciplines -
Method of Instruction -
  1. Lecture
  2. Discussion
  3. Cooperative learning exercises
Lab Content -
Not applicable.
Types and/or Examples of Required Reading, Writing and Outside of Class Assignments -
  1. Homework Problems: Homework problems covering subject matter from text and related material ranging from 30 - 60 problems per week. Students will need to employ critical thinking in order to complete assignments.
  2. Lecture: Ten hours per week of lecture covering subject matter from text and related material. Reading and study of the textbook, related materials and notes.
  3. Projects: Student projects covering subject matter from textbook and related materials. Projects will require students to discuss mathematical problems, write solutions in accurate mathematical language and notation and interpret mathematical solutions. Projects may require the use of Excel.
  4. Worksheets: Problems and activities covering the subject matter. Such problems and activities will require students to think critically. Such worksheets may be completed both inside and/or outside of class.