PERALTA COMMUNITY COLLEGE DISTRICT COURSE OUTLINE COLLEGE BERKELEY

COLLEGE:

Berkeley City College

DATE OF OUTLINE

06/27/2012

ORIGINATOR:

Mary Jennings

DATE OF CURRICULUM
COMMITTEE APPROVAL:

EFFECTIVE START DATE:

Spring 2013

DIVISON/DEPARTMENT:

MATH

1.

REQUESTED CREDIT CLASSIFICATION:(check one only)

Community Services (Fee-based)
[  ]

Degree Credit
[X]

Non-Degree Credit
[  ]

Non-Credit
[  ]

2.

DEPT/COURSE NO:

3.

COURSE TITLE:

MATH 206

Algebra for Statistics

4.

COURSE:

BCC New Fee Based Course[  ]

BCC Course Changes in Catalog Info[  ]

BCC New Course[X]

BCC Course Changes only in Non-Catalog Info[  ]

TOP NO.

1701.00

5.

UNITS: 5

HRS/WK LEC: 6 Total: 105

HRS/WK LAB: 0 Total: 0

HRS/WK TBA: 0 Total:

6.

NO. OF TIMES OFFERED AS SELECTED TOPIC:

AVERAGE ENROLLMENT: 35

7.

JUSTIFICATION FOR COURSE

As the number of levels of pre-transfer-level mathematics an entering community college student must complete increases, the likelihood that the student will ever successfully complete a transfer-level mathematics course decreases according to large research studies conducted both inside and outside of California. By offering “Algebra for Statistics” the mathematics department aims to provide students with an opportunity to follow a more fruitful path into transfer-level Math 13, Introduction to Statistics. Contextualizing the algebra curriculum and focusing instruction on the skills, methods and ways of thinking needed for understanding statistical applications is expected to ignite student interest, increase retention and success, and prepare students to succeed in Math 13 the following semester. Not for science, technology, engineering, mathematics, nursing or business majors. 

8.

COURSE/CATALOG DESCRIPTION

Integrated mathematics for statistics: Exploratory data analysis and principles of data collection and calculation; ratios, rates, and proportional reasoning; fractions, decimals and percents; evaluating expressions; analyzing algebraic expressions of statistical measures; modeling bivariate data with linear and exponential functions; graphical and numerical descriptive statistics for quantitative and categorical data. Not for science, technology, engineering, mathematics, nursing or business majors.

9.

OTHER CATALOG INFORMATION:

1. Modular:  Yes [  ]  No [X]   If yes, how many modules:

2. Open entry/open exit:   Yes [  ]  No [X]

3. Grading Policy:   Both Letter Grade or Pass/No Pass [  ]   Pass/No Pass [  ]   Letter Grade Only [X]  

4. Eligible for credit by Exam:   Yes [  ]  No [X]

5. Repeatable according to state guidelines: Yes [  ]  No [X] If yes, number of allowable repeats:

6. Required for degree/certificate (specify):  

7. Meets GE/Transfer requirements (specify):  

8. Are there prerequisites/corequisites/recommended preparation for this course?   Yes [X]  No [  ]
   Date of last prereq/coreq validation: 06/27/2012

10.

LIST STUDENT PERFORMANCE OBJECTIVES (EXIT SKILLS): (Objectives must define the exit skills required of students and include criteria identified in Items 12, 14, and 15 - critical thinking, essay writing, problem solving, written/verbal communications, computational skills, working with others, workplace needs, SCANS competencies, all aspects of the industry, etc.)(See SCANS/All Aspects of Industry Worksheet.)

Students will be able to:

1. formulate questions that can be addressed with data, then collect, organize, display, and analyze relevant data to address these questions and communicate findings.

2. use the properties of algebra to simplify expressions, solve equations and answer questions in context.

3. apply numerical, algebraic and geometric reasoning skills to statistical analysis.

4. construct, use, and interpret mathematical models, including linear and exponential functions, that represent relationships in quantitative data.

11A.

COURSE CONTENT: (List major topics in sequence; address objectives listed in #11 above. Degree applicable course must be taught at college level; see definition. List percent of time spent on each topic. Also, differentiate content of each level, when levels are assigned.) Lecture and lab content are to be listed separately.

LECTURE CONTENT:

10% Review of Relevant Arithmetic

A.  Recognizing and finding equivalent forms of fractions, decimals and percents

B.  Comparing fractions, decimals and percents

D.  Graphing fractions, decimals and signed numbers on a number line

E.  Unit measure and conversion between units

F.  Ratios and rates

G.  Operations with real numbers

H.  Absolute values

I.  Exponents and roots
 
J.  Scientific notation

20% Introduction to Algebra

A.  Variables and formulas

B. Summation notation, including subscripts

C.  Sketching and interpreting graphs in the Cartesian plane including those of points, lines and probability distributions

D.  Finding the equation of a line given its graph or its slope and one point on the line or two points on the line.

E.  Solving systems of linear equations in two or three variables

F.  Solving quadratic equations

G.  Solving simple rational and square root equations

H. Introduction to functions

I. Linear functions

J. Exponential and logarithmic functions

10% Introduction to Logic

A.  Venn diagrams

B.  The scientific method

15% Categorical Variables

A.  Constructing and reading graphs of distributions of categorical data: bar graphs, line graphs and pie charts

B.  Analyzing two-way tables; rules of probability; mutually exclusive and independent events; calculation of joint, marginal and conditional probabilities

15% Quantitative Variables

A.  Graphs of univariate distributions of quantitative data: histograms and boxplots

B.  Measures of central tendency: mean, median and mode

C.  Descriptions and measures of spread: variance, standard deviation, quartiles, percentiles

D. Measure of symmetry: skewness

10% Geometric and Graphical Interpretation of Algebraic Structures

A.  Signed distance from the mean

B.  Geometric interpretation of standard deviation

C.  Symmetry

10% Bivariate Distributions of Quantitative Variables

A.  Creating and analyzing scatterplots

B.  Intuitive introduction to least squares regression using linear and exponential models

C.  Intuitive and graphical introduction to correlation coefficient r

10% Data Collection

A.  Sample surveys

B.  Observation vs. experiment

11B.

LAB CONTENT:

12.

METHODS OF INSTRUCTION (List methods used to present course content.)

1. Lecture

2. Discussion

3. Projects

13.

ASSIGNMENTS: 10 hours/week. (List all assignments, including library assignments. Requires two (2) hours of independent work outside of class for each unit/weekly lecture hour. Outside assignments are not required for lab-only courses, although they can be given.)

Out-of-class Assignments: Problem sets including problems equivalent to those covered in lectures and original problems which require the synthesizing of various concepts. Research project and paper summarizing statistical findings.

ASSIGNMENTS ARE: (Check one. See definition of college level):

14.

STUDENT ASSESSMENT: (Grades are based on): (Check as many boxes as are applicable. Note: For degree credit, AT LEAST ONE of the first three boxes must be checked. If "ESSAY" is not checked, please explain why here.)

[X]  

ESSAY (Includes "blue book" exams and any written assignment of sufficient length and complexity to require students to select and organize ideas, to explain and support the ideas, and to demonstrate critical thinking skills.)

[X]  

COMPUTATION SKILLS

[X]  

NON-COMPUTATIONAL PROBLEM SOLVING (Critical thinking should be demonstrated by solving unfamiliar problems via various strategies.)

[X]  

SKILL DEMONSTRATION

[X]  

MULTIPLE CHOICE

[  ]  

OTHER (Describe)

15.

TEXTS, READINGS, AND MATERIALS:

A.

Textbooks:

*Date is required: Transfer institutions require current publication date(s) within 5 years of outline addition/update.

B.

Additional Resources:

C.

Readings listed in A and B above are: (Check one. See definition of college level):

16.

Designate Occupational Code (check ONE only):

1a. Prerequisites/Corequisites/Recommended Preparation: 
PREREQUISITE(S):

• MATH 253: Pre-Algebra
  or

• appropriate placement through multiple measures assessment process
  Subject course and pre/corequisite is: