# SRJC Course Outlines

8/3/2020 7:33:45 PM | MATH 15 Course Outline as of Fall 2017
| Changed Course |

CATALOG INFORMATION |
||

Discipline and Nbr: MATH 15 | Title: ELEMENTARY STATISTICS | |

Full Title: Elementary Statistics | ||

Last Reviewed:10/22/2018 |

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 |

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 154 or MATH 155 or higher; or Qualifying Placement from Math Assessment. See Student Success & Assessment Services for more information about the assessment process.

**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 154 or MATH 155 or higher; or Qualifying Placement from Math Assessment. See Student Success & Assessment Services for more information about the assessment process.

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 |

Certificate/Major Applicable: Major Applicable Course

__COURSE CONTENT__**Outcomes and Objectives:**

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

Upon successful completion of the course, students will 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 the difference 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 statistics 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 t

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 including:

A. Business

B. Social sciences

C. Psychology

D. Life sciences

E. Health sciences

F. Education

**Assignments:**

1. Daily 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 F. Pearson. 2014

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

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