Download Course Syllabus Department: Date: I. Course Prefix and Number

Course Syllabus
Department: Mathematics
Date: November 6, 2013
I. Course Prefix and Number: MAT 200
Course Name: Intermediate Statistics
Credit Hours and Contact Hours:
3 credit hours, 3 contact hours
Catalog Description including pre- and co-requisites:
This statistics course is designed for an experienced mathematics student. It is a one
semester course covering descriptive and inferential statistics. Topics included are
measures of center; measures of dispersion; hypothesis testing; estimations for population
means, proportions, and variance; determination of sample size; uses of the Chi-square
distribution; analysis of variance; linear correlation and linear regression; and statistical
research. The course will emphasize computer or calculator use (graphing calculator,
Minitab, Excel, or the like).
Prerequisite: MAT 145 College Algebra or test into Level 3
Relationship to Academic Programs and Curriculum:
This course is designed to fulfill a three-credit mathematics requirement for many A.S., A.A.,
and A.A.S. degree programs and is offered as a one-semester alternative to the existing twosemester sequence in statistics, MAT 121 and MAT 122. Students taking this course can take
additional program specific courses because they will not need to take Statistics 1 and 2 (MAT
121/122). Students should verify the appropriateness of this course for their program with their
advisor. This course fulfills the SUNY Gen. Ed. Requirement for mathematics.
II. Course Student Learning Outcomes: State the student learning outcome(s) for the course
(e.g. Student will be able to identify…)
Upon completion of the course the participant will be able to:
1) Use the language of statistics; read and interpret statistical presentations;
critically analyze the use of statistics in decision making; understand and
recognize the misuses and limitations of statistics.
2) Describe the relationship between sample statistics and population parameters.
3) Analyze bivariate data using linear correlation and regression using technology.
4) Make applicable predictions using linear regression analysis.
5) Apply probability distributions to applied problems with an emphasis on the
binomial and normal distributions.
6) Explain how the Central Limit Theorem applies to statistical analysis.
7) Determine the validity about the population parameter using the technique of
hypothesis testing.
8) Apply hypothesis testing to small samples, large samples, two samples, and
many samples.
9) Find and interpret the meaning of confidence intervals for many different sample
situations.
10) Use technology for statistical purposes.
11) Evaluate statistical results for reasonableness
College Learning Outcomes Addressed by the Course: (check each College Learning
Outcome addressed by the Student Learning Outcomes)
writing
oral communications
reading
mathematics
critical thinking
computer literacy
ethics/values
citizenship
global concerns
information resources
III. Assessment Measures (Summarize how the college and student learning
outcomes will be assessed): For each identified outcome checked, please provide the
specific assessment measure.
List identified College Learning Outcomes(s)
Mathematics
Specific assessment measure(s)
Possible evaluation methods include quizzes,
tests, portfolios, collected assignments, group
activities, projects, etc... All work throughout the
course will involve mathematics.
Reading
On most evaluation methods students will need to
read questions carefully. Students will also need
to use the textbook to complete homework
assignments.
Critical Thinking
On most evaluation methods students will be
required to approach problems in new ways,
carefully analyzing information given to decide the
most appropriate statistical concepts to apply in
solving the problem.
IV. Instructional Materials and Methods
Types of Course Materials:
1. Textbook: Selected by department.
2. Computer statistical software package or calculator: Selected by department.
Methods of Instruction (e.g. Lecture, Lab, Seminar …):
1. Lectures
2. Discussions
3. Demonstrations
4. Group activities
5. Computer Assignments
V. General Outline of Topics Covered:
1) Fundamental terminology and concepts
a) Population vs. sample
b) Parameter vs. statistic
c) Variables
d) Data – quantitative vs. qualitative; discrete vs. continuous
e) Sampling methods
2) Computing and interpreting measures of central tendency and dispersion
a) Mean
b) Variance and standard deviation
3) Probability distributions of random variables
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 of a probability distribution
e) Binomial probability distribution
i) Properties of a binomial experiment
ii) Computing binomial probabilities
4) Normal Distribution
a) Computing and interpreting standard score (z-score)
b) Empirical rule
c) Techniques for identifying outliers
d) Properties of a normal distribution
e) Computing the probability of a normal variable
f) Calculating the value of a normal variable from proportion/probability
g) Assessing normality of a sample
h) Normal distribution approximations of a binomial distribution (Optional)
5) Bivariate Data
a) Scatter plot
b) Types of correlation (IE: linear, quadratic, exponential, etc.)
c) Correlation vs. causation
d) Linear (main focus)
i) Identifying the direction of the relationship
ii) Measuring strength of the relationship
iii) Using linear regression to make predictions
iv) Restrictions on using linear regression.
v) Calculation and interpretation of residuals
6) Sample Variability
2
a) Sampling distributions for x-bar, p-hat, and s .
b) Central limit theorem with applications
7) Introduction to Statistical Inference
a) Estimation of population parameters via confidence intervals
b) Conducting hypothesis tests using Classical and Probability Value approaches
8) Inferences Involving One Population
a) Create and interpret confidence intervals for the population
i)
Mean
ii) Proportion
iii) Standard deviation/variance
b) Conduct hypothesis tests for the population
i) Mean
ii) Proportion
iii) Standard deviation/variance
c) Determine the sample size needed for a given margin of error at a given level of confidence for both
means and proportions.
9) Inferences Involving Two Populations
a) Independent and dependent samples
b) Create and interpret confidence intervals for the
i) difference between two independent means
ii) difference between two dependent means
iii) difference between two population proportions
c) Conduct hypothesis tests for
i) two independent means
ii) two dependent means
iii) two population proportions
iv) two population variances/standard deviations
10) Additional Applications of Chi-square
a) Goodness of fit test.
b) Test for Independence.
11) Analysis of Variance
a) Introduction to the Analysis of Variance technique
b) The logic behind ANOVA
c) Applications of Single-Factor ANOVA
12) Intro to Design of Experiments
a) Single vs. Double Blind
b) Designs of experiments
i) Randomized
ii) Matched pairs
iii) Randomized block