Introduction to Statistics
  • Final
  • Mathematics
  • Introduction to Statistics
  • 3.0
  • The use of probability techniques, hypothesis testing, and predictive techniques to facilitate decision-making. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-square and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings.  Applications using data from a broad range of disciplines.

  • 110
  • Typically satisfies general education quantitative reasoning requirement (CSU GE B4).

  • Intermediate Algebra or any CSU accepted* statistics pathway curriculum prerequisite

    *At present there are two mechanisms to become accepted:

    • the proposed statistics course has been accepted to meet CSU General Education Breadth Area B4
    • the pathway has been accepted by the CSU Chancellor's Office process per its October 20, 2015 memo (Statistics Pathways in CSU Quantitative Reasoning)

    1. Summarizing data graphically and numerically;
    2. Descriptive statistics:  measurement, measures of central tendency, and variation;
    3. Sample spaces and probability;
    4. Random variables and expected value;
    5. Sampling and sampling distributions;
    6. Discrete distributions – Binomial;
    7. Continuous distributions – Normal;
    8. The Central Limit Theorem;
    9. Estimation and confidence intervals;
    10. Hypothesis Testing and inference, including t-tests for one and two populations, and Chi-square test;
    11. Correlation, regression lines, and analysis of variance (ANOVA);
    12. Applications using data from at least four of the following disciplines: business, economics, social science, psychology, political science, administration of justice, life science, physical science, health science, information technology, and education; and
    13. Technology based statistical analysis.

  • Upon successful completion of the course, students will be able to:

    1. Interpret data displayed in tables and graphically;
    2. Apply concepts of sample space and probability;
    3. Calculate measures of central tendency and variation for a given data set;
    4. Identify the standard methods of obtaining data and identify advantages and disadvantages of each;
    5. Calculate the mean and variance of a discrete distribution;
    6. Calculate probabilities using normal and t-distributions;
    7. Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem;
    8. Construct and interpret confidence intervals;
    9. Determine and interpret levels of statistical significance including p-values;
    10. Interpret the output of a technology-based statistical analysis;
    11. Identify the basic concept of hypothesis testing including Type I and II errors;
    12. Formulate hypothesis tests involving samples from one and two populations;
    13. Select the appropriate technique for testing a hypothesis and interpret the result;
    14. Use regression lines and ANOVA for estimation and inference, and interpret the associated statistics; and
    15. Use appropriate statistical techniques to analyze and interpret applications based on data from at least four of the following disciplines:  business, economics, social science, psychology, political science, administration of justice, life science, physical science, health science, information technology, and education.

  • Tests, examinations, homework or projects where students demonstrate their mastery of the learning objectives and their ability to devise, organize and present complete solutions to problems.

  • A college level text supporting the learning objectives of this course.

  • Last vetting was from 9/16/12 to 10/31 approved 12/15/12

  • October 19, 2016
  • October 19, 2016