|General Course Description
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.
|Any rationale or comments
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)
- Summarizing data graphically and numerically;
- Descriptive statistics: measurement, measures of central tendency, and variation;
- Sample spaces and probability;
- Random variables and expected value;
- Sampling and sampling distributions;
- Discrete distributions – Binomial;
- Continuous distributions – Normal;
- The Central Limit Theorem;
- Estimation and confidence intervals;
- Hypothesis Testing and inference, including t-tests for one and two populations, and Chi-square test;
- Correlation, regression lines, and analysis of variance (ANOVA);
- 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
- Technology based statistical analysis.
Upon successful completion of the course, students will be able to:
- Interpret data displayed in tables and graphically;
- Apply concepts of sample space and probability;
- Calculate measures of central tendency and variation for a given data set;
- Identify the standard methods of obtaining data and identify advantages and disadvantages of each;
- Calculate the mean and variance of a discrete distribution;
- Calculate probabilities using normal and t-distributions;
- Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem;
- Construct and interpret confidence intervals;
- Determine and interpret levels of statistical significance including p-values;
- Interpret the output of a technology-based statistical analysis;
- Identify the basic concept of hypothesis testing including Type I and II errors;
- Formulate hypothesis tests involving samples from one and two populations;
- Select the appropriate technique for testing a hypothesis and interpret the result;
- Use regression lines and ANOVA for estimation and inference, and interpret the associated statistics; and
- 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.
|Methods of Evaluation
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.