Download Course Syllabus

Course Syllabus
Department: Mathematics
Date: Fall 2014
I. Course Prefix and Number: MAT 121
Course Name: Statistics I
Credit Hours and Contact Hours: 3 Credit Hours, 3 Contact Hours
Catalog Description including pre- and co-requisites:
A first course in statistics designed to introduce descriptive statistics of one and two variables, and
probability; and to assimilate those concepts into an understanding of probability distributions. Topics
include central tendency, variability, graphing, linear correlation, and regression, dependent and
independent probability, discrete and continuous probability distributions.
Prerequisite: MAT 095 or Placement into Math Level 1 or higher.
II. Course Outcomes and Objectives
Student Learning Outcomes:
Upon completion of the course the participant will be able to:
1. Use the language of statistics; reading and interpreting statistical presentations; critically
analyzing the use of statistics in decision making; understanding and recognizing the misuses
and limitations of statistics.
2. Analyze single variable data including producing and interpreting statistical measures, graphical
representations and frequency distributions.
3. Analyze bivariate data using linear correlation and regression using technology.
4. Make applicable predictions using linear regression analysis.
5. Apply the basic principles of probability.
6. Apply probability distribution to applied problems with an emphasis on the binomial distribution
and normal distribution.
7. Use technology for statistical purposes.
8. Evaluate their results for reasonableness.
Relationship to Academic Programs and Curriculum:
This course is a service course that fulfills mathematics/science course requirements for many A.A. and
A.A.S. degrees. A student should verify the appropriateness of this course for his program with his
advisor.
College Learning Outcomes Addressed by the Course:
writing
computer literacy
oral communications
ethics/values
reading
citizenship
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mathematics
critical thinking
global concerns
information resources
III. Instructional Materials and Methods
Types of Course Materials:
1. Textbook: Selected by department.
2. Computer statistical software package: Selected by department.
Methods of Instruction (e.g. Lecture, Lab, Seminar …):
1.
2.
3.
4.
Lectures
Discussions
Demonstrations
Experiments
5. Use of Computers
IV. Assessment Measures (Summarize how the college and student learning outcomes
will be assessed):
Student Learning Outcomes will be assessed through a variety of activities. The Mathematics
department believes that each instructor should determine the grading system and evaluation methods
that will be used in their sections of the course. Any grading system used in the course must be
consistent with the College Catalog. These methods must be communicated to students the first week
of the semester in writing. Possible evaluation methods include quizzes, tests, portfolios, collected
assignments, group activities, et. al. Such evaluations and related assignments will develop a student’s
ability to read problems carefully, perform mathematics, use critical thinking techniques and develop
computer literacy through the use of statistical software. Course policies with respect to attendance,
late work, plagiarism, etc. must be communicated to the student.
V. General Outline of Topics Covered:
Note: The following is a list of topics. The order in which the material is covered need not follow the
ordering below.
1) Fundamental Terminology and Concepts
a) Population vs. Sample
b) Parameter vs. Statistic
c) Variable
d) Data – Quantitative vs. Qualitative; Discrete vs. Continuous; (Nominal vs. Ordinal - Optional)
e) Appropriate Use and Misuse of Statistics
2) Sampling
a) Methods
b) Simple Random Sample
c) Cluster
d) Stratified
e) Systematic
f) Convenience
g) Sampling Bias
3) Organization of Data
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a)
b)
c)
d)
e)
f)
g)
Graphical
Quantitative Data : Histogram, Dot plot, Box plot, Stem-and-Leaf
Qualitative Data : Bar Chart, Pie Chart, Pareto
Tabular
Ungrouped vs. Grouped Frequency Distribution
Relative
Cumulative
4) Distribution of Quantitative Data
a) Shape
b) Skewed vs. Symmetrical
c) Bimodal
d) Uniform
e) Normal
f) (J-shaped – Optional)
g) Understanding The Relationship Between Graphs And Measures of Central Tendency, Position
and Dispersion
5) Computing and Interpreting Measures of Central Tendency
a) Mean
b) Median
c) Mode
d) Midrange (Optional)
e) Mid-quartile (Optional)
6) Computing and Interpreting Measures of Position
a) Quartiles
b) Percentiles
c) Standard score (z-score)
d) Outliers
7) Computing and Interpreting Measures of Dispersion
a) Range
b) Deviation from the Mean
c) Mean Absolute Deviation (Optional)
d) Variance
e) Standard Deviation
f) Inter-Quartile Range
g) Chebyshev's Theorem
h) Empirical Rule
8) Probability
a) The Concept of Probability
b) Sample Space Representations : List, Table, Tree diagrams
c) Simple vs. Compound Events
d) Types of Probability : Empirical, Theoretical, Subjective
e) Compound events
f) Mutually exclusive
g) Independent/dependent events
h) Introduction to General Addition And Multiplication Rules
i) Introduction to Conditional probability
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9) Probability Distributions of A Random Variable
a) Random Variable : Discrete vs. Continuous
b) The Concept of a Probability Distribution
c) Representations of a Probability Distribution
d) Computing and Interpreting Mean/Standard Deviation For Probability Distribution
e) Binomial Probability Distribution
f) Characteristics Of A Binomial Experiment
g) Computing Binomial Probabilities
h) Computing and Interpreting the Mean and Standard Deviation (Optional)
10) Normal Distribution
a) Characteristics of A Normal Distribution
b) Computing Normal Probabilities
c) Interpreting the Mean and Standard Deviation (Optional)
d) Normal Distribution Approximations of A Binomial Distribution (Optional)
11) Bivariate Data
a) Scatter plot
b) Types of Correlation
c) Linear (main focus)
d) Strength
e) Direction
f) Existence Of Non-Linear Correlations
g) Correlation Vs. Causation
h) Using Linear Regression To Make Predictions
i) Restrictions On Using Linear Regression.
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