Meets: Room: Madeleva 353 Time Section 1: 10:00 - 10:50, Section 2: 1:00 - 1:50
Instructor: Charles Peltier
Office : Mad 332 Phone: 4498(H:232-4951)email cpeltier@saintmarys.edu
Office Hours M 11 - 12 T 11 - 12 W 3 - 4 TH 2 - 3 F 11 - 12 or see/call me to make an appointment
Sullivan, Fundamentals of Statistics 3d edition, Prentice Hall, 2011.
Minitab manual: (on-line) Describes use of the statistical package for this course [Minitab]
Calculators: (on-line) Describes use of popular calculators for basic statistical calculations
A Blackboard site is maintained for this course and will contain links to materials necessary or useful for the course.
You will need a calculator that can perform two-variable statistical calculations (mean, standard deviation, correlation coefficient, regression line) -the work will be more difficult without a graphing calculator [TI-83 series is the best current choice] - and you will be using the Minitab statistics package which is available on campus network (Windows only).
The mathematical content of this course is statistics: "the science of collecting, organizing, summarizing, and analyzing information to draw conclusions or answer questions" [Sullivan p.3]. It is intended to prepare you to use statistical thinking to understand the world around you, to understand and judge statistical arguments that arise every day, and to learn the uses of statistics in your chosen field. You will be making extensive use of the MINITAB statistics package available in the computer lab for calculations, and will be expected to use a calculator with basic statistical calculations built-in for work in class. The topics are:
1. Descriptive Statistics (weeks 1-4)
* Data Collection: design of experiments, data collection methods
* Data Summary: data organization, graphical summary, numerical summary
* Misrepresentation of data
* Descriptions of bivariate data (relations) - regression2. Mathematical Theory for Statistics (weeks 5-9)
* Probability: addition rule, multiplication rule, conditional probability, independence
* Discrete and Continuous Probability Distributions: binomial, uniform, normal and standard normal
* Applications of Normal Distribution: probabilities, normal approximation to the binomial distribution
* Sampling Distributions and the Central Limit Theorem3. Inferential Statistics (weeks 10-15)
* Confidence Intervals: population mean and proportion
* Hypothesis Testing: single population mean and proportion, two means (independent and dependent samples
* Chi-Square Distribution and Test for IndependenceThe primary (non-technical) theme for the course is the improvement of the learning process by practicing learning skills in the study of statistics. Only about 5% of the knowledge gained in college is actually used on the job after you have been on the job for five years. Information is increasing exponentially so a person must become an expert at acquiring and applying knowledge to solve new problems.
Course Objectives:
This course is part of the General Education program at Saint Mary's and as such the following are the goals for the course that relate to the general education requirements:
- To improve your ability to think clearly about complex problems by modeling word problems in mathematical form and carrying out the solution;
- To improve your quantitative skills by carrying out the computations required to produce numerical solutions to problems;
- To improve your communication skills for technical material by reading mathematical material and writing solutions and explanations using correct language, notation, and organization;
- To appreciate the mathematical way of thinking and working, in particular, persistence, independence, and precision in the use of mathematical language and concepts.
The goals that relate specifically to the course are as follows:
Learning Outcomes: A successful student in Math 114 will be able to:
- To acquire statistical literacy: understand the basic language of statistics; recognize statistical terms and symbols; be able to read statistical graphs; understand fundamental ideas of statistics.
- To learn and use statistical reasoning: be able to make proper data summary and proper interpretation of the statistical information; conceptual understanding of important ideas and the connection among these ideas.
- To learn and use statistical thinking: the type of thinking that statisticians use when approaching or solving statistical problems; understand the need for data, the importance of data production, the presence of variability, and the quantification and explanation of variability
- define and use correctly statistical terms, language, nomenclature, and methods introduced in the course;
- identify sources of errors and misuses of descriptive and inferential statistics;
- appropriately select and use various sampling methods; design statistical experiments;
- describe and summarize data using graphical and numerical means; interpret graphical and numerical description of data;
- define probability; distinguish between classical, empirical, and subjective probabilities; apply the rules for computing probabilities;
- define random variables and probability distribution; compute probabilities, expected value, and standard deviation
for discrete and continuous random variables, in particular the binomial, uniform and normal random variables;- define sampling distribution and standard error of a statistic; calculate the mean and standard deviation of a sampling distribution for mean and proportion; recognize when a sampling distribution for sample mean and sample proportion are approximately normal, including use of the Central Limit Theorem;
- demonstrate understanding of confidence intervals and hypothesis testing; use these concepts correctly for statistical inference related to population mean, proportion, and independence of variables;
- select and use statistical methods required to solve real world problems;
- communicate ideas and work clearly, correctly, and with precision;
- effectively use statistical software to solve statistical problems.
We will use cooperative group learning, discovery learning, applied critical thinking, problem solving, and regular self assessment.
Attendance: Since you will often be working with your team in class, it is essential that you attend every meeting of the class to fulfill your role in the team. Tests, quizzes, or activities missed because of absence will result in a grade of 0 unless the absence is excused (via the Advising office or discussion - preferably before the event - with me). In any case, you are responsible for material covered in any class meeting and for handing in work on time. In particular, note that March 9, March 19, April 4 and April 11 are regular class days and there is likely to be graded work required on any such day.
Assignments: You should expect to be working about 6-8 hours per week outside of class time on this course. There will be regular reading assignments in preparation for the next class and written assignments for learning the material. You are responsible for remaining current, and for asking questions when they arise. Written work will be collected at the next class, and will be corrected and returned for your studying [Late assignments will be graded and returned, but will not receive credit]. You are encouraged to work and study together, but each of you is responsible for her own work and understanding. The list of assignments will be available through the Blackboard site.
Academic honesty policy: Read the statement on Academic Honesty in the Student Handbook and also accessible through the Academic Affairs and First Year Studies web page (includes procedures, appeal policies etc. - you really should read it enough to know what's there). In this course academic dishonesty will result in a grade of 0 for the work involved, and notification of the Academic Affairs and First Year Studies Office. A second occurrence will result in failure for the course, with notice to Academic Affairs. On tests, no assistance (including other people, books, notes, etc.) is permitted. You should work together and/or seek help from the instructor on regular assignments, but each student must hand in her own work and is responsible for her own understanding. Copying of results is never acceptable. For more information, see the Math Department Honesty Policy
Team Participation: Each student will be a member of a three - to - five member learning team. A good portion of your working time - including some in-class time for learning activities - will be spent working with your team. You are expected to attend all sessions scheduled by your team. Every team report should include an effort report detailing each team member's actual contributions.
In-class learning activities:
There will be a number of in-class learning activities (about one a week) to be carried out in your learning teams. There are several specific roles
[see the document "Team Roles and Performance Criteria"] to be filled in performing this activity, and the team will present a report and written work at the end of the activity. Each activity will also include several critical thinking questions [going beyond the specific task of the activity] to be answered by each team member in her learning journal. At the end of the activity, the team will hand in a report containing (at least)1. table of contents
2. recorders report (including any written work products)
3. reflector's report * [see below]
4. Team's grade (0.0-5.0) for their teamwork on the activity [based on criteria for success listed on the activity]. I will also assign a grade (0.0-5.0 pts) - if the grade you assign is unreasonably high, there will be a penalty in my grade*The reflector's report must include:
1. Role, strength and area for improvement of each participating team member;
2. Greatest strength of the team as a whole [used in this work] and an areas for improvement[as shown in this work]Data analysis project: Each team will complete a data analysis project including selection of a sample, description and analysis of data, interpretation and reporting of results. All members of the team must be involved in the work of the project, which will be assigned in stages during the semester.
Disabilities policy: Any student who is eligible for accommodations to complete the requirements and expectations of this course because of a disability is invited to make her needs known to the instructor and should also contact Iris Giamo, in the Disability Resource Office (x4262) or e-mail igiamo@saintmarys.edu for an appointment to review documentation and arrange for appropriate and legal accommodations. Students who suspect they may have a disability are also encouraged to contact the Disabilities Resource Office.
Student achievement will be evaluated with two in-class tests, a cumulative final examination, in-class activities, a team project, regular written exercises and class participation, weighted as shown here:
Category 2 tests @ 100 [Dates: 2/15, 3/28] Cumulative final examination [5/7 at 1:45 pm] in-class activities [team] Team project Written exercises & participation Total Letter grades will be based on : 100% ≥ A ≥ 90% > B ≥80% > C ≥ 70%> D ≥ 60% > F with + and - grades occupying the top and bottom 2% of the ranges (no A+ or D- grades).
Week
Dates
Text Sections
Topics
1/16 - 1/20 Basic concepts - data, sampling, experiments, variables 1/23 - 1/27 Summarizing and describing data 1/30 - 2/3 Numerical description of data 2/6 - 2/10
Description of bivariate data (relations) - regression 2/13 - 2/17 Probability basic concepts Test 1 on chapters 1 - 4 Wed 2/15 2/20 - 2/24 Basic probability methods and concepts 2/27 - 3/2 Discrete random variables and the binomial family 3/5 - 3/9 Continuous random variables and the "normal" family 3/19 - 3/23 Normal approx to binomial, sampling distributions 3/26 - 3/30 Confidence interval [theory] for the mean Test 2 on chapters 5 - 8 Wed 3/28 4/2 - 4/4 Confidence intervals [practice] mean and proportion 4/11 - 4/13 Testing for significance on the mean 4/16 - 4/20 Tests on mean and proportion 4/23 - 4/27 Inference on differences of means and proportions 4/30 - 5/2 Inference for distributions 5/7 at 1:45 pm Cumulative - Emphasis on inference (Chapters 9 - 12)