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Stats 245.3

Stats 245.3. Introduction to Statistical Methods Course Information. Instructor:. W.H.Laverty. Office:. 235 McLean Hall. Phone:. 966-6096. Lectures:. M W F 11:30am - 12:20pm Arts 241 Lab: M 3:30 - 4:20 Arts 143. Evaluation:. Assignments, Labs, Term tests - 40%

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Stats 245.3

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  1. Stats 245.3 Introduction to Statistical Methods Course Information

  2. Instructor: W.H.Laverty Office: 235 McLean Hall Phone: 966-6096 Lectures: M W F 11:30am - 12:20pm Arts 241 Lab: M 3:30 - 4:20 Arts 143 Evaluation: Assignments, Labs, Term tests - 40% Every 2nd Week (approx) – Term TestFinal Examination - 60%

  3. Dates for midterm tests: • Monday, Jan 20 (in the lab, 3:30pm) • Monday, Feb 03 (in the lab, 3:30pm) • Monday, Feb 24 (in the lab, 3:30pm) • Monday, Mar 09 (in the lab, 3:30pm) • Monday, Mar 23 (in the lab, 3:30pm) • Monday, Apr 05 (in the lab, 3:30pm) Each test and the Final Exam are Open Book Students are allowed to take in Notes, texts, formula sheets, calculators (No laptop computers.) The tests and the Final Exam are multiple choice and computer marked – Students need an HB pencil and to identify their paper with their student number.

  4. Computer Assignments – due dates and time • Wednesday, February 05 • Wednesday, February 26 • Wednesday, March 11 • Wednesday, March 25 Computer Assignments It is important to learn to use at least one of the powerful statistical Packages – SPSS, Minitab, S-plus, SAS, R Very quickly statistical computations become outside the range of feasibility of simple computing devices (hand-held calculators, computer spreadsheets) These assignments are designed to give some initial experience with these packages.

  5. Computer Assignments will be accepted and given a mark if they are submitted after the due date and time, however assignments that are submitted late will not be returned.

  6. Text • The lectures will be given in Power Point • These will be posted on the Stats245 website • Tables that are required will be posted on the Stats 245 website • A text is not be required • I will post a list books in the library can be consulted

  7. To download lectures • Go to the stats 245 web site • Through PAWS or • by going to the website of the department of Mathematics and Statistics -> people -> faculty -> W.H. Laverty -> Stats 245-> Lectures. • Then • select the lecture • Right click and choose Save as

  8. To print lectures • Open the lecture using MS Powerpoint • Select the menu item File -> Print Stat 245.3

  9. The following dialogue box appear

  10. In the Print what box, select handouts

  11. Set Slides per page to 6 or 3.

  12. 6 slides per page will result in the least amount of paper being printed 1 2 3 4 5 6

  13. 3 slides per page leaves room for notes. 1 2 3

  14. Course Outline

  15. Introduction • Populations, samples • Variables • Data Collection

  16. Introduction

  17. Questions arise about a phenomenon Conclusion are drawn from the analysis A decision is made to collect data A decision is made as how to collect the data The data is summarized and analyzed The data is collected The circular process of research:

  18. What is Statistics? It is the major mathematical tool of scientific inference (research) – with an interest in drawing conclusion from data. Data that is to some extent corrupted by some component of random variation (random noise)

  19. Random variation or (random noise) can be defined to be the variation in the data that is not accounted for by factors considered in the analysis.

  20. Example Suppose we are collecting data on Blood Pressure Height Weight Age

  21. Suppose we are interested in how Blood Pressure is influenced by the following factors Height Weight Age

  22. Blood Pressure will not be perfectly predictable from : • Height • Weight • Age There will departures (random variation) from a perfect prediction because of other factors the could affect Blood pressure (diet, exercise, hereditary factors)

  23. Another Example In this example we are interested in the use of: • antidepressants, • mood stabilizing medication, • anxiety medication, • stimulants and • sleeping pills. The data were collected for n = 16383 cases

  24. Age 20-29, 30-39,40-49, 50-59, 60-69, 70+ In addition we are interested in how the use these medications is affected by: • Gender Male, female • Education • < Secondary, • Secondary Grad., • some Post-Sec., • Post-Sec. Grad.

  25. Income • Low, Low Mid, Up Mid, High • Role • parent, partner , worker • parent, partner • parent, worker • partner, worker • worker only • parent only • partner only • no roles

  26. Some questions of interest • How are the dependent variables (antidepressant use, mood stabilizing medication use, anxiety medication use, stimulants use, sleeping pill use) interrelated? • How are the dependent variables (drug use) related to the independent variables (age, gender, income, education and role)?

  27. Again the relationships will not be perfect • Because of the effects of other factors (variables) that have not been considered in the experiment • If the data is recollected, the patterns observed at the second collection will not be exactly the same as that observed at the first collection

  28. The data appears in the following Excel file Drug data

  29. In Statistics • Questions • About some scientific, sociological, medical or economic phenomena • Data • The purpose of the data is to find answers to the questions • Answers • Because of the random variation in the data (the noise). Conclusions based on the data will be subject to error.

  30. Statistics Statistics In what part of this process does statistics play a role? Questions arise about a phenomenon The circular process of research: Conclusion are drawn from the analysis A decision is made to collect data Experimental Design A decision is made as how to collect the data The data is summarized and analyzed The data is collected

  31. Statistical Theory is interested in • The design of the data collection procedures. (Experimental designs, Survey designs). The experiment can be totally lost if it is not designed correctly. • The techniques for analyzing the data.

  32. In any statistical analysis it is important to assess the magnitude of the error made by the conclusions of the analysis.

  33. Consider the following statement: You can prove anything with Statistics.

  34. In fact: One is unable to “prove” anything with Statistics.

  35. At the end of any statistical analysis there always is a possibility of an error in any of the decisions that it makes.

  36. The success of a research project does not depend on the its conclusions The success of a research project depends on the accuracy of its conclusions

  37. If one is testing the effectiveness of a drug There is two possible conclusions: 1. The drug is effective: 2. The drug is not effective:

  38. The success of a this project does not depend on the its conclusions The successdepends on the accuracy of its conclusions

  39. For this reason: It is extremely important in any study to assess the accuracy of its conclusions

  40. Some definitions important to Statistics

  41. A population: this is the complete collection of subjects (objects) that are of interest in the study. There may be (and frequently are) more than one in which case a major objective is that of comparison.

  42. A case (elementary sampling unit): This is an individual unit (subject) of the population.

  43. A variable: a measurement or type of measurement that is made on each individual case in the population.

  44. Types of variables Some variables may be measured on a numerical scale while others are measured on a categorical scale. The nature of the variables has a great influence on which analysis will be used. .

  45. For Variables measured on a numerical scale the measurements will be numbers. Ex: Age, Weight, Systolic Blood Pressure For Variables measured on a categoricalscale the measurements will be categories. Ex: Sex, Religion, Heart Disease

  46. Note Sometimes variables can be measured on both a numerical scale and a categorical scale. In fact, variables measured on a numerical scale can always be converted to measurements on a categorical scale.

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