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Syllabus
Course Information:
Course Title: Statistics
Department Name: Department of Mathematics and Science
Course Number: STA 105 c
Semester Offered: Spring 2014
Course Meeting Days: Tuesday, Thursday
Course Meeting Time: 17:45 – 19:00
Course Meeting Places: BAC 203
Prerequisites: None
Cr.3. (6 ECTS Cr.) Offered every semester.
Blackboard Site: Statistics Forum, accessible under the condition that you are registered
for STA 105 c and for Blackboard at the link:
https://elearn.aubg.bg/webapps/login/
Instructor Information:
Name: Valentin Vankov Iliev
Office Phone: (073)-888497
e-Mail Address: [email protected]
Office Location: BAC, Room No 323
Office Hours: Tuesday, Thursday, 10:30-11:30, or by appointment.
Office hours are intended as time set aside to discuss any problems you might have in
understanding the material presented in this course. If you have a question, but cannot be
present during office hours, contact me via email. I will make every effort to answer your
questions quickly, but there are no guarantees. Office hours are best for this purpose.
Professor Homepage Website: http://home.aubg.bg/faculty/viliev/
Course Description:
This course is designed to give students the ability to interpret results that can be drawn
from data. It serves the student’s need in Business, Economics, and other Social Sciences
to be able to make sense of results of studies and surveys. At the end of the course
students will gain experience to communicate effectively using statistical ideas and
concepts. Both descriptive and inferential methods will be presented with sufficient
theory to assure understanding of the material.
Course Goals:
The students should understand: the correct ways of collecting data, how to summarize
and describe large data sets, how to make correct and meaningful conclusions for the
whole population using information contained in surveys and summaries.
General Education Goals:
Students will be able to:
Make direct observations in a “real world” situation when outcomes occur by chance,
Apply statistical methods in other areas of research and social experience,
Justify their conclusions using basic statistical knowledge,
Recognize wrong conclusions based on surveys of data,
Critique wrong conclusions using statistical arguments,
Communicate and argue using basic statistical concepts.
More generally, in the context of General Education Philosophy of the University,
the students will be able to:
Reason analytically, symbolically and quantitatively using abstract models of the physical
and social world;
Use symbols, models, numbers and quantities;
Find, analyze and apply information to solve problems through critical thinking and
creative synthesis;
Draw relationships using inductive and deductive reasoning in the solution of problems,
Appraise and evaluate in matters empirical and logical;
Identify important questions and formulate hypothesis and arguments to answer them
effectively;
Employ and critique quantitative and qualitative modes of statistical analysis.
Courses in Quantitative Reasoning:
Introduce students to a wide spectrum of mathematical models in different fields of
knowledge and teach by example, the use of mathematics and statistics as tools of thought;
Explore the scope and limits of mathematical reasoning;
Illustrate the use of deductive logic in testing theories;
Generate, by practice, confidence in the use of the language of mathematics.
Texts and Resources:
Mendenhall, Beaver, Beaver, Introduction to Probability and Statistics, 14-th ed.
Additional Reading:
Applied Statistics in Business and Economics, 4 ed., Doane (online textbook on
Blackboard).
Assessment/Assignments:
Four homeworks - 10%, Quiz 1 - 10 % , Midterm Test – 15%, Quiz 2 - 15 %, Two popquizzes – 5% + 5%, Final Exam - 40%
Quiz 1, Midterm, and the Final Exam are cumulative.
Note that all exams are open book. You will be allowed to bring 2 additional pages of
personal notes (8-1/2 x 11, front and back, if needed). Calculators are allowed, but cell
phones and computers are not allowed during the tests.
Grading:
Grading scale: g = grade = maximum 100 pts,
100 ≥ g ≥ 95: A, 95 > g ≥ 91: A − , 91 > g ≥ 87: B +, 87 > g ≥ 83: B
83 > g ≥ 79: B −, 79 > g ≥ 75: C +, 75 > g ≥ 70: C, 70 > g ≥ 65: C −
65 > g ≥ 60: D +, 60 > g ≥ 55: D, 55 > g : F
Amnesty:
In case you are not satisfied with your score on some of Quiz 1, Midterm, or Quiz 2, you
may choose exactly one of them explicitly by signing a list, and its score will be replaced
by the score of the Final Exam.
Class Policies:
Academic Honesty Policy: students are expected to adhere to the Academic Honesty
Honor Code stated in the Catalog.
Student Attendance and Participation:
Attendance is strongly recommended. During section you will be given feedback on
previous homework, quizzes, and exams; you will have an opportunity to ask questions
about the course material; and some new material will be presented. Class sessions are
your primary opportunity to ask questions of the teaching fellow. Participation in class
also is strongly recommended.
Course Schedule:
1st week: The language of sets. Populations, samples, variables, relative frequency
histograms. Numerical measures of a set of data: mean, median, mode.
2nd week: Numerical measures of a set of data: range, standard variance and deviation.
Work with calculator. Examples. Measures of relative standing, five-number summary,
box-plot.
3rd week: Tchebisheff’s theorem, Empirical rule. The role of probability in statistics,
sample space as the set of outcomes of an experiment. Examples. Pop-quiz 1
4th week: Some simple combinatorial analysis that is useful for counting probabilities –
permutations and combinations.
5th week: Event relations and probability rules, independent events, conditional
probability, Bayes’ rule. Quiz 1.
6th week: Discrete random variables and their probability distributions, mean and standard
deviation for a discrete random variable.
7th week: Two useful discrete distributions: the binomial probability distribution, the
Poisson probability distribution. The normal probability distribution, the normal
approximation to the binomial probability distribution.
8th week: Sampling distribution, central limit theorem, sample distribution of the sample
mean, the sample distribution of the sample proportion.
9th week: Large-sample estimation: large-sample confidence interval for a population
mean, large-sample confidence interval for a population proportion. Midterm test.
10th week: Large-sample estimation: estimating the difference between two population
means, estimating the difference between two population proportions, choosing the
sample size.
11th week: Testing hypotheses about population parameters, large-sample test about a
population mean, large-sample test about a population proportion, , large-sample test for
the difference between two population means, large-sample test for the difference
between two population proportions, p-value of the test. Pop-quiz 2
12th week: Small-sample inferences: small-sample inferences concerning a population
mean, small-sample inferences for the difference between two population means, p-value
of the test.
13th week: Small-sample inferences for the difference between two population means – a
paired-difference test. Inferences concerning a population variance. Quiz 2
14th week Linear regression and correlation, the method of least squares. Review of the
course.
February 20 - Quiz 1, March 20 - Midterm Test, April 17 - Quiz 2
Homework will be assigned approximately every third week and due Thursday next
week following the assignment. It is strongly suggested that you finalize your homework
well in advance of the deadline, because if something can go wrong, it will, especially
when a deadline is in play.
Material covered by the course:
Describing sets of measurements: Graphical techniques
Describing sets of measurements: Numerical techniques
Probability and probability distributions
Counting rules
Sampling distributions: The Central Limit Theorem
Large sample estimation and large sample tests of hypotheses
Inference from small samples
Inferences concerning a population variance
Linear regression and correlation
Students with disabilities:
Any student who feels he or she may need an accommodation based on the impact of a
disability should contact me privately to discuss his or her specific needs. Also contact
Health Center as soon as possible to better ensure that such accommodations are
implemented in a timely fashion.
Disclaimer:
This syllabus is not a binding contract, and the Professor reserves the right to make
changes during the semester.