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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)

No information provided

English, one level below transfer (i.e., eligibility for English composition (C-ID ENGL 100) and reading a course with an exiting skill of ability to read a college level text.

1.     Introduction to statistics use health public health and biostatistics

2.     Descriptive verses inferential statistics

a.     Descriptive statistics:  

                                          i.    measurement

                                         ii.    measures of central tendency

                                        iii.    variation.

b.     Association or causality

c.     Criteria for causality

3.     Summarizing data graphically and numerically.

4.     Sample spaces and probability.

5.     Random variables and expected value.

6.     Types of studies

7.     Sampling and sampling distributions.

8.     Discrete distributions – Binomial.

9.     Continuous distributions – Normal.

10.  The Central Limit Theorem.

11.  Estimation and confidence intervals.

12.  Hypothesis Testing and inference,

a.     t-tests for one and two populations, and

b.     Chi-square test

13.  Correlation, regression lines, and analysis of variance (ANOVA);

14.  Applications using data from at least four of the following disciplines: social science, psychology, life science, physical science, health science, information technology, and education.

15.  The use of technology for statistical analysis.

No information provided

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

1.     Interpret data displayed in tables and graphically.

2.     Describe the usefulness of descriptive and inferential statistics, demonstrating an understanding of the association versus causality.

3.     Apply concepts of sample space and probability.

4.     Calculate measures of central tendency and variation for a given data set.

5.     Identify the standard methods of obtaining data and identify advantages and disadvantages of each.

6.     Calculate the mean and variance of a discrete distribution.

7.     Describe different types of research study designs and the pros and cons of each.

8.     Calculate probabilities using normal and t-distributions.

9.     Distinguish the difference between sample and population distributions and analyze the role played by the Central Limit Theorem.

10.  Construct and interpret confidence intervals.

11.  Determine and interpret levels of statistical significance including p-values.

12.  Interpret the output of a technology-based statistical analysis.

13.  Identify the basic concept of hypothesis testing including Type I and II errors.

14.  Formulate hypothesis tests involving samples from one and two populations.

15.  Select the appropriate technique for testing a hypothesis and interpret the result.

16.  Use regression lines and ANOVA for estimation and inference, and interpret the associated statistics; and

17.  Use appropriate statistical techniques to analyze and interpret applications based on data from at least four of the following disciplines: social science, psychology, life science, physical science, health science, information technology, and education.

May include:

Exams

Quizzes

Written Assignments

Projects

Sullivan, L. M. Biostatistics for Population Health: A Primer.  

Gerstman, B. B. Basic Biostatistics: Statistics for Public Health Practice 

OER:

Illowski, B. & Dean, S. Introductory Statistics. Openstax. Available at https://openstax.org/details/books/introductory-statistics