Math 96

Cuyamaca College
Math 96 – Prep for Elementary Statistics
Section 9162 – Spring 2015
Instructor: Dan Curtis
Class Times: MW 1:00-3:20 pm
MW 3:30-4:50 pm
Office Hours: Monday
9:00 – 9:50 am
9:00 – 9:50 am
2:00 – 3:00 pm
Wednesday 9:00 – 9:50 am
9:00 – 9:50 am
Room: H138
Room: H119
Office: H115
Prerequisites: None.
Text and Materials:
 Textbook: None
 Calculator: A scientific (or graphing) calculator is required, for example, the TI30X IIS.
 Computer Software: The following computer software is required for this
course. All listed software is available on the computers in the H-building open
lab for your use. If your schedule allows you to work on some of your homework
at the College, you don’t need to purchase these items.
o MS Word
o Adobe Reader
o TinkerPlots 2
 Other Materials: Please bring these items to class with you each day.
o A 3-ring notebook (no spiral notebooks) just for this class.
o Loose-leaf notebook paper.
o 3-hole punched quarter-inch graph paper (optional)
o A straight edge (ruler)
o Colored pens or pencils (optional)
o Headphones for use in the lab (optional)
Course Description: An accelerated one-semester course to transfer level Elementary
Statistics (Math 160) covering core concepts from arithmetic, pre-algebra,
elementary and intermediate algebra, and descriptive statistics that are needed to
understand the basics of college-level statistics. Concepts are taught through the
context of descriptive data analysis. The core arithmetic and algebra skills needed
to understand the concepts, formulas, and graphs used in transferlevel statistics
are investigated in a “just-in-time” approach rather than the standard sequence
found in the traditional algebra path. This course is NOT intended for math,
science, computer science, business, or engineering majors.
Important Dates:
Last day to add classes/Last day to drop
and qualify for a refund and to drop without
receiving a “W”
Friday, Feb 6
Last day to file a petition for credit/no credit
Friday, Feb 27
Last day to drop with a ‘W’
Friday, Apr 24
Final Exam
Wed., May 27
It is the student’s responsibility to take care of any administrative procedures involved in
dropping should he/she stop attending class.
Attendance: Good attendance is a must for success in this class. College policy states that
a student may be dropped from the course for excessive absences or tardies.
My Policy: Four absences during the first four weeks or six absences during the
entire semester and you may be dropped – arriving significantly late or leaving
significantly early counts as half an absence.
Disability Support Services: Academic accommodations are available for students with
disabilities. Please identify yourself to your instructor and to DSPS staff so that the
appropriate accommodations can be ensured. DSPS is at A-300, LRC (660-4239)
Academic Honesty: Academic dishonesty of any type by a student provides grounds for
disciplinary action by the instructor or college. If you cheat, there will be
consequences: I may give you a zero on the assignment or a zero in the course, or
other additional consequences, regardless of whether you were the giver or receiver
of the cheating.
Misconduct: Disruptive or threatening behavior or any conduct that interferes with my
ability to teach or another student’s ability to learn will not be tolerated. Such
actions could result in a warning, removal from the class, or referral to the Dean for
disciplinary action. Please turn off your cell phones during class.
STEM Achievement Center: To support your efforts to succeed in this class, I refer you
to the STEM Achievement Center (H-Building). The STEM Achievement Center is
a resource center that provides individual assistance in mathematics and science.
Instructors and student tutors are available to answer homework questions, give
confidence, and support math students. Students also have access to graphing
calculators, textbooks, instructional videos, and computer tutorial programs.
Computers are also available for student use. The STEM Achievement Center is
open Monday through Thursday 9:00 am - 7:00 pm, and Friday from 9:00 am to
Schedule: The course will consist of four units, each lasting approximately four weeks.
During each unit, expect daily homework and group work and one to three quizzes.
Each unit will culminate in an exam covering the material learned during that unit,
as well as a paper based on the data presented. Due dates for all assignments will be
posted on the Math 96 website.
Unit 1: (Weeks 1-4) Categorical Variables vs. Categorical Variables
Unit 2: (Weeks 5-8) Categorical Variables vs. Quantitative Variables
Unit 3: (Weeks 9-12) Quantitative Variables vs. Quantitative Variables
Unit 4: (Weeks 13-16) Study Design and Statistical Analysis
Final Exam: Wednesday, May 29, from 1:00-3:00 pm
Grading: This course is graded Pass/No Pass only. No letter grade will be given. You
must earn at least 70% to earn a passing grade.
Papers & Exams (Papers 20%; Exams 20%): At the end of each unit you'll write a paper
incorporating the concepts and skills covered in the unit. Additionally, you may
have a midterm at the end of each unit. I'll post the date of the midterm and the due
date of the paper in advance in the "Upcoming Due Dates" section of Math 96
homepage. No makeups for exams, but if you contact me before the day of an
exam, I may be able to make arrangements for an alternate time for you to take the
Final Exam (20%): The comprehensive final exam is scheduled for Thursday, Dec 13
from 1:00-3:00 pm.
Groupwork, Homework, and Computer Labs (20%): Homework will be assigned and
collected regularly. Occasionally, I will collect your in-class groupwork and spotgrade it. Computer labs will be collected at the end of lab time each class. No makeups, but I'll drop your two lowest scores. It is important that you attend every class.
You will be responsible for any assignments during classes you miss.
Quizzes (20%): With each unit, you will have one to three quizzes (approximately one
quiz every two to three weeks). The quizzes will be taken during labtime, and I'll
post the date in advance in the "Upcoming Due Dates" section of the Math 96
homepage. No makeups, but I'll drop your lowest quiz score.
Course Objectives (Expected Student Learning Outcomes)
To successfully complete this course, students must demonstrate the ability to:
1) (Exploratory data analysis) Formulate questions that can be addressed with data, then
organize, display, and analyze relevant data to address these questions and
communicate results. Students will demonstrate that they can perform exploratory
data analysis if they:
a) graphically represent the distribution of categorical and quantitative data;
b) use graphical representations to investigate patterns and trends in data;
c) compare different graphical representations of the same data and evaluate how
well each representation shows important aspects of the data;
d) compare related data sets using numerical measures and appropriate graphical
representations and communicate findings in the context of the data;
e) investigate relationships in bivariate quantitative data, display a scatterplot,
describe its shape, and determine regression coefficients, regression equations,
and correlation coefficients using technological tools and communicate findings
in the context of the data;
f) set up two-way tables for bivariate categorical data and use appropriate marginal
and conditional percents to investigate relationships and answer questions.
2) (Data collection) Apply the basic principles of study design to develop and analyze
the validity of simple experiments and sampling plans related to a given situation and
goal. Students will demonstrate application of the basic principles of study design if
they can illustrate and/or explain:
a) the purpose of randomization in the experiment design;
b) explanatory, response, and confounding variables;
c) simple random sample;
d) statistical bias;
e) bias due to under-coverage, non-response, interviewer behavior or characteristics,
question wording, or aspects of the survey that influence responders; and
f) the difference between correlation and causation and the connection of these
concepts to observational studies and random, controlled experiments.
3) (Numerical and algebraic reasoning) Apply numerical and algebraic reasoning and
computational skills to support statistical analysis. Students will exhibit numerical
and algebraic reasoning and computational skills if they:
a) identify the place-value structure of the base-ten number system and are able to
represent and compare rational numbers (including negative rationals) in decimal
from and their approximate location on a number line;
b) recognize, generate, and fluently use equivalent forms of fractions, decimals, and
c) identify, compare, and explain the contextual meaning of fractions that represent
the marginal distribution of a single categorical variable;
d) identify, compare, and explain the contextual meaning of fractions that represent
the relationship of two categorical variables in conditional distribution;
e) explain and apply the concept of variables as representations of quantities;
f) explain and apply the concept of a function and interpret functions as
communicating relationships between variables;
g) recognize the difference between variables and parameters in general forms of
linear and exponential models;
h) identify relationships that are proportional, define the constant of proportionality
in the context of the problem, and use proportional reasoning to solve problems;
i) relate proportionality to linearity as well as the concept of the slope;
j) identify functions as linear or exponential and contrast their properties by relating
their symbolic form to verbal descriptions, tables of values, or graphs;
k) use the order of operations to evaluate statistical formulas by hand and with
technology; and
l) describe statistical measure (e.g. mean, variance, standard deviation, “least
squares”, correlation coefficient) and its characteristics by referencing symbolic
4) (Mathematical modeling with functions) Construct, use, and interpret mathematical
models, specifically linear functions of x, y, log(x), and log(y) to represent and
communicate relationships in quantitative data. Students will demonstrate that they
can model quantitative data using linear and exponential models if they:
a) identify trends in bivariate quantitative data and determine the class or classes of
functions (linear, exponential, or none of these) that could reasonably model the
b) define variables in context using appropriate units;
c) choose between linear and logarithmic scales to represent data;
d) use linear regression to find appropriate linear models;
e) analyze what the model assumes about how one variable changes with respect to
the other;
f) draw reasonable conclusions about a situation being modeled;
g) interpret the square of the correlation as the percent of variation in y that can be
explained by x; and
h) explain the difference between causation and correlation and identify the
confusion of these concepts as a fallacy.
5) (Effective learning strategies) Consistently apply effective learning strategies for
success in college. Students will demonstrate that they can apply effective learning
strategies if they:
a) attend class regularly;
b) turn in assignments on time;
c) work productively with peers on group assignments;
d) seek help from peers, teacher, and other resources when necessary;
e) set up and maintain their math notebook;
f) use rubric criteria to assess performance on assignments and make improvements;
g) meet with a counselor to develop an educational plan.