11/21/2024 6:31:27 AM	MATH 15 Course Outline as of Summer 2019	Changed Course
CATALOG INFORMATION
Discipline and Nbr:  MATH 15	Title:  ELEMENTARY STATISTICS
Full Title:  Elementary Statistics
Last Reviewed:1/9/2024

Units		Course Hours per Week			Nbr of Weeks	Course Hours Total
Maximum	4.00	Lecture Scheduled	4.00		17.5 max.	Lecture Scheduled	70.00
Minimum	4.00	Lab Scheduled	0		6 min.	Lab Scheduled	0
		Contact DHR	0			Contact DHR	0
		Contact Total	4.00			Contact Total	70.00
		Non-contact DHR	0			Non-contact DHR Total	0

Total Out of Class Hours:  140.00

Total Student Learning Hours: 210.00

Title 5 Category:  AA Degree Applicable
Grading:  Grade or P/NP
Repeatability:  00 - Two Repeats if Grade was D, F, NC, or NP
Also Listed As: 
Formerly: 

Catalog Description:

Exploration of concepts in statistics, descriptive statistics, probability theory, Central Limit Theorem, estimation of population parameters from a sample, hypothesis testing, correlation and linear regression, introduction to analysis of variance, and computer simulations.

Prerequisites/Corequisites:
Completion of MATH 161 OR MATH 156 OR MATH 154 OR MATH 155 or AB705 placement into Math Tier 1 or higher

Recommended Preparation:

Limits on Enrollment:

Schedule of Classes Information
Description:

Exploration of concepts in statistics, descriptive statistics, probability theory, Central Limit Theorem, estimation of population parameters from a sample, hypothesis testing, correlation and linear regression, introduction to analysis of variance, and computer simulations.

(Grade or P/NP)
Prerequisites:Completion of MATH 161 OR MATH 156 OR MATH 154 OR MATH 155 or AB705 placement into Math Tier 1 or higher
Recommended:
Limits on Enrollment:
Transfer Credit:CSU;UC.
Repeatability:00 - Two Repeats if Grade was D, F, NC, or NP

ARTICULATION, MAJOR, and CERTIFICATION INFORMATION

Associate Degree:		Effective:	Fall 1981	Inactive:
Area:	B MC	Communication and Analytical Thinking Math Competency
CSU GE:	Transfer Area			Effective:	Inactive:
	B4	Math/Quantitative Reasoning		Fall 1990
IGETC:	Transfer Area			Effective:	Inactive:
	2A	Mathematical Concepts & Quantitative Reasoning		Fall 1993
CSU Transfer:	Transferable	Effective:	Fall 1989	Inactive:
UC Transfer:	Transferable	Effective:	Fall 1989	Inactive:
C-ID:
CID Descriptor: MATH 110	Introduction to Statistics	SRJC Equivalent Course(s): MATH15 OR PSYC9

Certificate/Major Applicable: Both Certificate and Major Applicable

COURSE CONTENT

Student Learning Outcomes:
At the conclusion of this course, the student should be able to:

1.  Use numerical and graphical methods to summarize, display, and interpret data sets.

2.  Estimate population parameters from sample statistics.

3.  Perform one and two sample hypothesis tests for population means and proportions.

 

Objectives:

At the conclusion of this course, the student should be able to:

1.   Create and use graphic displays of data and frequency distributions.

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

      each method.

3.   Distinguish among different scales of measurement and their implications.

4.   Define mean, median, mode, percentiles, variability and standard deviation, and compute

      each for sets of data.

5.   Use laws of probability.

6.   Apply concepts of sample space and probability distributions, including calculation of the

      mean and variance of a discrete distribution, and calculation of probabilities using normal

       and t distributions.

7.   Distinguish between sample and population distributions, and apply the Central

      Limit Theorem to calculate sampling distributions of means, proportions and standard error.

8.   Compute and interpret confidence intervals and required sample size.

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

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

11. Perform hypothesis testing for mean, proportion and variance.

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

13. Implement goodness of fit test, and the test for independence.

14. Use linear regression and Analysis of Variance (ANOVA) for estimation and inference, and

      interpret the associated statistics.

15. Use statistical software for evaluation of data and inference.

16. Process data sets from disciplines including business, social sciences, psychology, life

      sciences, health sciences and education.

Topics and Scope

I. Statistical Description

    A. Graphic display of univariate and bivariate data

    B. Levels of measurement

    C. Frequency distributions

         1. Shapes of distributions

         2. Empirical rule

    D. Measures of central tendency

    E. Measures of variation

    F. Measures of relative position

    G. Correlation

II. Probability Theory

    A. Sample space and laws of probability

    B. Random variables and expected value

    C. Probability distributions including, but not limited to

         1. Binomial

         2. Normal

         3. Student

         4. Chi squared

III. Statistical Inference

    A. Sampling methods and experimental design

    B. Sampling distributions of means and proportions

    C. Standard error

    D. Central Limit Theorem

    E. Estimation and confidence intervals

    F. Hypothesis testing

         1. Tests of proportions and means, including t-tests for one and two populations

         2. Chi square tests: goodness of fit and independence

         3. P-values, significance, type I and type II errors

    G. Required sample size

    H. Correlation and linear regression

    I.  Introduction to ANOVA (analysis of variance)

IV. Use of Statistical Software

    A. Analysis and evaluation of data

    B. Methods of simulations

V.  Use Data Sets from Disciplines, such as:

    A. Business

    B. Social sciences

    C. Behavioral sciences

     D. Life sciences

    E. Health sciences

    F. Education

Assignments:

1. Reading outside of class (0-50 pages per week)

2. Problem set assignments from required text(s) or supplementary materials chosen by the

    instructor (8-16)

3. Exams (2-4) and a final exam; quizzes (0-20)

4. Projects, e.g. computer activities, surveys or data collection and analysis (0-2)

Methods of Evaluation/Basis of Grade.

Writing: Assessment tools that demonstrate writing skill and/or require students to select, organize and explain ideas in writing.		Writing 0 - 0%
None
This is a degree applicable course but assessment tools based on writing are not included because problem solving assessments are more appropriate for this course.
Problem solving: Assessment tools, other than exams, that demonstrate competence in computational or non-computational problem solving skills.		Problem Solving 10 - 30%
Problem sets
Skill Demonstrations: All skill-based and physical demonstrations used for assessment purposes including skill performance exams.		Skill Demonstrations 0 - 0%
None
Exams: All forms of formal testing, other than skill performance exams.		Exams 70 - 80%
Objective exams, quizzes, final
Other: Includes any assessment tools that do not logically fit into the above categories.		Other Category 0 - 10%
Projects

Representative Textbooks and Materials:

Elementary Statistics: Picturing the World. 6th ed. Larson, Ron and Farber, Betsy. Pearson. 2015

Elementary Statistics. 12th ed. Triola, Mario. Pearson. 2014 (classic)

Elementary Statistics, A Step by Step Approach. 9th ed. Bluman, Allan. McGraw-Hill. 2013 (classic)

Modern Elementary Statistics. 12th ed. Freund, John and Perles, Benjamin. Pearson. 2007 (classic)

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