Heartland Community College
Master Course Syllabus
Division Name: STEM and Business
Course Prefix and Number: MATH 141
Course Title: Introduction to Statistics
DATE PREPARED: 8/2/95
DATE REVIEWED: March 2014
DATE REVISED: March, 2014
PCS/CIP CODE: 1.1-270501
IAI NO. (if available): M1 902
EFFECTIVE DATE OF FIRST CLASS: Summer 2014
CREDIT HOURS: 4
CONTACT HOURS: 4
LECTURE HOURS: 4
LABORATORY HOURS: 0
CATALOG DESCRIPTION (Include specific prerequisites):
Prerequisite:
- Completion of MATH 093 and Math 098 with grade of C or better, or assessment.
This course focuses on mathematical reasoning and the solving of real-life problems, rather
than on routine skills and appreciation. Descriptive methods (frequency distributions,
graphing and measures of location and variation), basic probability theory (sample spaces,
counting, factorials, combinations, permutations, and probability laws), probability
distributions (normal distributions and normal curve, binomial distribution, and random
samples and sampling techniques), statistical inference (estimation, hypothesis testing, t-test,
and chi-square test, and errors), correlation and regression, and f-test and analysis of
variance. An emphasis is placed on calculating statistical results using appropriate
technology, and interpreting those results in context, rather than using formulas and tables.
TEXTBOOKS:
Moore, David S. (1999). Basic Practice of Statistics, 2nd Ed. New York, NY: W. H. Freeman
and Company
or a comparable text that addresses at a minimum the topics listed in the Course Outline and that
provides students with the opportunity to achieve the learning outcomes for this course.
RELATIONSHIP TO ACADEMIC DEVELOPMENT PROGRAMS AND
TRANSFERABILITY:
MATH 141 fulfills 4 of the semester hours of credit in Mathematics required for the A.A. or
A.S. degree. This course should transfer as part of the General Education Core Curriculum
described in the Illinois Articulation Initiative to other Illinois colleges and universities
participating in the IAI. However, students should consult an academic advisor for transfer
information regarding particular institutions. Refer to the IAI web page for information as
well at www.itransfer.org.
LEARNING OUTCOMES:
Course Outcomes
Organize and present data using statistical charts and
graphs.
Obtain or generate data.
Generate a random sample.
Graph data and determine if the graph is enough to
answer the question of interest.
Analyze and summarize data sets.
Find measures of center using technology.
Construct and analyze frequency distributions for a
set of data.
Demonstrate why association is not causation
Apply probability theory to determine probabilities
based on sample data.
Assess whether a data set is compliant with the
assumptions associated with statistical procedures.
Know and apply sampling techniques used to
generate sampling distributions.
Determine probabilities associated with sampling
distributions.
Properly analyze a statistical question using
hypothesis testing, significance levels and p-values
using technology for calculations.
Demonstrate why statistical significance does not
necessarily imply practical importance.
Use technology to properly analyze a statistical
question using confidence intervals, including the
interpretation of confidence intervals.
Communicate the results of a statistical analysis.
Analyze the relationship between two variables to
determine if there is a significant correlation between
them and if so, determine the linear relation between
the two variables.
Test a claim regarding three or more means using
one-way ANOVA.
Essential
Competencies
Range of
Assessment Methods
Throughout the
semester, the
following assessment
methods will be used
to measure the course
and Essential
Competencies:
Homework; Quizzes;
Discussion Postings;
Exams; Projects
CT 2
PS 2
CO 2
PS2: Student identifies the type of problem and use a framework to solve the problem.
CT2: Students determine value of multiple sources or strategies and select those most
appropriate in a given context.
CO2: Students effectively deliver a message via various channels/modalities.
COURSE/LAB OUTLINE:
1.
2.
3.
4.
5.
6.
7.
8.
Graphical Descriptions of Data
Measures of Central Tendency and Variability
Introduction to Probability and Probability Distributions
Sampling Techniques and Distributions
Confidence Intervals
Hypothesis Testing
Correlation and Regression
ANOVA
METHOD OF EVALUATION (Tests/Exams, Grading System):
Instructors may determine the most appropriate methods of evaluation for their course. These
methods of evaluation might include but are not limited to unit test(s), quiz(zes), homework,
project(s), and a comprehensive final exam.
GRADING SCALE:
90  S.P.  100  A
80  S.P.  90  B
70  S.P.  80  C
60  S.P.  70  D
00  S.P.  60  F
S.P. = student performance
REQUIRED WRITING AND READING:
Required writing will be part of most assignments and tests. Students will be expected to explain
how they found the solution, describe the solution graphically, and interpret the answer in the
context of the problem. Students are expected to read the material in the textbook for each
section studied, typically 10-15 pages per section.