QUEENSBOROUGH COMMUNITY COLLEGE
THE CITY UNIVERSITY OF NEW YORK
Department of Mathematics and Computer Science
Dr. Sylvia M. Svitak Section Only
MA-336 COMPUTER ASSISTED STATISTICS (Writing Intensive)
Pre-requisite MA 119 with a C or better, or MA 114 with a C or better, or satisfactory score on the
Mathematics Placement Test, Level II
Hours 3 Class Hours 1 Lab Hour 3 Credits
Course Description The course is an introduction to statistics and statistical reasoning. The course is writing
intensive; students will do low stakes writing in learning statistics and high stakes projects that require writing.
Topics include descriptive statistics, experimental design and sampling, correlation, probability and probability
distributions, confidence intervals, tests of hypotheses, and statistical reporting. Students will use calculators
and/or statistical software programs to store, organize, and analyze data arising from real life contexts.
Curricula for which the course is required/recommended
A.S. Degree Programs in Liberal Arts and Sciences (Science and Mathematics), Criminal Justice, Education,
Engineering and Technical Sciences, Environmental Health, Health Sciences.
General Education Objectives The effective use of analytical reasoning skills to identify issues or problems
and evaluate evidence in order to make informed decisions; the ability to reason quantitatively and
mathematically as required in fields of interest and in everyday life; successful integration of knowledge and
skills in programs of study; the effective use of information management and technology skills for research and
lifelong learning.
Course Objectives/ Expected Student Learning Outcomes Students will (1) think critically about data, (2)
select and use appropriate graphical and numerical summaries, (3) apply appropriate standard statistical
inference procedures, (4) draw conclusions from these analyses, and (5) write suitable statistical reports.
Textbook Statistics, Fourth Edition (2007)
Authors David Freedman, Robert Pisani, Roger Purves and Ani Adhikari
Publisher W. W. Norton and Company, New York
Methods by which student learning will be evaluated with the following approximate percent
distributions
In-Class Work and Outside-Class Assignments, Class
50%
Exams
Reading/Writing Assignments; Course Writing Project
25%
Final Examination
25%
Academic Integrity: Academic honesty is taken extremely seriously and is expected of all students. All
assignments must be the original work of the student (and partners or group, if applicable). All questions or
concerns regarding ethical conduct should be brought to the course instructor. “It is the official policy of the
College that all acts or attempted acts that are violations of academic integrity be reported to the Office of
Student Affairs (OSA). At the faculty member’s discretion and with the concurrence of the student or students
involved, some cases, though reported to the OSA, may be resolved within the confines of the course and
department. The instructor has the authority to adjust the offender’s grades as deemed appropriate, including
assigning an F to the assignment or exercise or, in more serious cases, an F to the student for the entire course.”
(Taken from the QCC Academic Integrity Policy, 2/14/2005)
NOTE: Any student who feels that he/she may need an accommodation based upon the impact of a disability
should contact the instructor privately to discuss his/her specific needs. Please contact the office of Services for
Students with Disabilities in Science Building, room 132 (718 631 6257) to coordinate reasonable
accommodations for students with documented disabilities.
Math 336 Course Syllabus, Dr. Sylvia M Svitak’s Section ONLY
TOPICS
(Note that writing to learn strategies and writing assignments will be
incorporated into the course material as part of the writing intensive
requirement for this course. Note also that graphing calculators will be
required for calculations and data analysis, and they may be used on
exams.)
PART I: EXPLORING DATA
CHAPTERS
* see note that
follows
HOURS
* see note that
follows
CLASS
LAB
1. Presenting Variables and Distributions: Data Classification, Data
Collection, Experimental Design
1, 2
4
1
2. Descriptive Statistics: Graphical Display, Numerical Descriptions
3, 4
4
1
3. Exploring Relationships: Scatter-plots and Correlation; Regression
8−10, 12
4
2
13−14
4
2
5. Discrete Probability Distributions: Binomial and Normal Distributions
15
4
2
6. Normal Probability Distributions: Determining Probabilities and Values;
Central Limit Theorem, Normal Approximations to Binomial
Distributions
5
4
2
21
4
2
26, 27
4
2
Provided by
Instructor
4
PART II: FROM EXPLORATION TO PROBABILITY
4. Probability: Basic Concepts, Conditional Probability, Multiplication and
Addition Rules, Additional Topics in Probability and Counting
PART III: INFERENCE ABOUT VARIABLES
7. Confidence Intervals for the Mean: Large and Small Samples
8. Hypothesis Testing for the Mean: Introduction, Using Large and Small
Samples
9. Statistical Study Writing Project
Assessment: Writing Assignments, Reviews and Class Exams, and Final
Examination
10
Total
46
* Note: The chapters and the approximate hours per chapter are guidelines and are at the discretion of the
instructor. The instructor is responsible for making assignments and scheduling examinations.
SMS/cs FALL 2015
14