| Teaching Style |
The course has been designed to permit students to become self sufficient using statistics and to achieve high course grades.
The course emphasizes understanding of the material, above memorization. In order to succeed in the course students must have the time to devote to the course material and must be willing to seek assistance, from their instructor, as needed.
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| Lectures |
Lectures provide students with an opportunity to review course material, which they have studied prior to each class.
Lectures provide an opportunity to assess content comprehension, through question and answering sessions and practice problem sets. Questions and problems sets will be presented both verbally or through clickers; which allow anonymous surveying of the whole class.
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| Lab sessions |
Data analysis and problem solving skills will be refined during lab sessions, using the Socratic method, in which students answer their own questions with the over site of the teaching assistants.
During the first hour of lab sessions, students will split into small groups and will review questions pertinent to the analysis and understanding of larger novel data sets. Statistical software packages will be used to simplify analysis.
During the second hour of the lab session students will review and present their answers to the weekly assignments, using the Socratic method.
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| Class Preparation |
For students to be comfortable and prepared for lectures and labs, it is vital that students review the course material both before and after each class.
Students are not expected to learn the material from the course text on their own, but are expected to have reviewed new material prior to each class and to have extensively reviewed and understood previously presented material.
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| Study Techniques |
Statistics can be a challenging subject to master. To help ensure that students do well in the courses, students are adviced to:
- chat with your instructor, early, if you have any questions
- ask questions in lectures, lab sessions and review sessions
- read before and after every lecture and lab session
- complete all assignments
- study frequently, but for short periods of time
- frequently review the material
- review and follow the problem solving steps
- review returned problems and attend the second lab hour
- review the formulae sheet and tables,
- formiliarity with these will really help in the exams
- re-read chapters 1 - 7 after we cover the t-test,
- this will really help you to integrate the material
- write lists of definitions and symbols
- write lists of the inferential tests and when they can be used
- draw concept maps and diagrams to help integrate the material
- ensure you can calculate means and SDs on your calculator
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| Assignments |
Students will practice and refine their quantitative problem solving skills through weekly exercises. Weekly exercises will take about 4 hours to complete.
Weekly problem sets will prepare students for the course exams. Students should focus on presenting their solutions to problems in a clear, concise and descriptive fashion. |
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| Research Projects |
To aid with the integration and application of the course material students will conduct a group research project.
The group research project is designed to emphasize how to; design a research study, collect research data, present findings and evaluate research results.
Students will present research findings as a PowerPoint presentation to the whole class and produce a graded written scientific report.
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| Exams |
Midterm exams are designed to aid students in becoming self sufficient with problem solving and statistical interpretation of information.
Initial exams will assess student's ability to carry out specific problem solving skills, while the latter exams encourage students to make decisions regarding how to appropriately present and analyze data sets without guidance.
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