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Lecture 1 Outline: Tue, Jan 13

Lecture 1 Outline: Tue, Jan 13. Introduction/Syllabus Course outline Some useful guidelines Case studies 1.1.1 and 1.1.2 . Course objectives.

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Lecture 1 Outline: Tue, Jan 13

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  1. Lecture 1 Outline: Tue, Jan 13 • Introduction/Syllabus • Course outline • Some useful guidelines • Case studies 1.1.1 and 1.1.2

  2. Course objectives • Understand distinctions between various types of studies (e.g., observational studies, controlled experiments), the questions they can address and what types of statistical methods are appropriate for analyzing them. • Statistical tools: two sample methods, several sample methods (ANOVA), multiple comparisons, simple linear regression, multiple linear regression • Hands on experience analyzing data and computing with data (using JMP). • Interpretation and communication of results

  3. Guidelines • The lectures will be used to present the basic ideas, illuminate key concepts, present examples and have class discussion. • You are responsible for both the material presented in lecture and the reading. The reading for each lecture is on the lecture schedule (check web page for updates to schedule).

  4. Guidelines (Contd.) • At the end of each chapter, the book contains conceptual questions with answers which you should use to test your understanding of material. • JMP-IN • Used extensively for assignments. • Familiarity with output needed for exams • Recommended JMP-IN text is a good reference

  5. Guidelines (Contd.) • The final grade is determined based on the assignments, midterms, final project and final. • Preparation for exams • Review lectures. • Review reading. • Review assignments.

  6. Guidelines (Contd.) • Feedback on lecture style, assignments, other aspects of course is encouraged. • Constant interaction encouraged to better understand the material. • I encourage you to come see me at least once during semester to chat about your background, interest and concerns about the class.

  7. Chapter 1 topics • Study design and its impact on what statistical inferences can be drawn • Measurements of statistical uncertainty (p-values and confidence intervals) • Graphical methods for displaying data • Discussed in context of two sample problem

  8. Two Sample Problems • Compare the response variables of two groups based on samples from the groups. Two types of problems • (1) Two distinct populations (e.g, the opinions of men vs. women on President Bush’s job performance) • (2) Two different treatments applied to one population (e.g., the effect of taking a drug vs. a placebo on depression).

  9. Case Study 1.1.1: Motivation and Creativity • Broad scientific questions of interest: Do grading systems promote creativity in students? Do ranking systems and awards increase productivity among employees? Do rewards and praise stimulate children to learn? • Experiment: Students in a creative writing class were randomly assigned to one of two groups. One received an “intrinsic” and the other an “extrinsic” questionnaire. Afterwards, they wrote Haiku poems that were scored for creativity.

  10. Descriptive Statistics, Graphs in JMP • motivcreat.JMP • Click Analyze, Distribution. Put Score into Y, Columns and put Group into By and click OK. • JMP will display for each group a histogram and box plot, quantiles of the sample data and summary statistics for the sample data (mean and standard deviation).

  11. Statistical Analysis • Specific question of interest: Is there any evidence that creativity scores tend to be affected by the type of motivation (intrinsic/extrinsic) induced by the questionnaire? • Statistical tools for addressing this question • Graphical methods • Hypothesis test (p-value) • Confidence interval

  12. Things to Think About • Can we infer that the difference in creativity scores was caused by the difference in motivational questionnaires? • The poems were given to judges in random order. Why was that important? • To what extent does this experiment address the real scientific question of interest, do external incentives promote creativity?

  13. Case study 1.1.2: Sex discrimination in employment • Legal/scientific question of interest: Did a bank discriminatorily pay higher starting salaries to men than women? • The data: Starting salaries were available for all male and female entry-level clerical employees of the Harris bank. • Specific question: Did male starting salaries tend to be larger than female starting salaries? (more so than could be explained by chance?)

  14. Things to think about • Can we infer that discrimination has occurred on the basis of the evidence that men received larger starting salaries than women? • Suppose we also had available information about education and experience of the employees. How could we use this information? Would it allow us to infer that discrimination has occurred?

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