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Course: MATH 444, Fall 2008
School: University of Montana
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Word Count: 730

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YOU WHAT NEED TO KNOW - Chapters 1-4.3 1. Know the basic steps involved in statistical methodology: collecting, summarizing, analyzing, and presenting data. 2. Know how to identify the population and sample in a study, and what the benets and drawbacks of sampling are. 3. Understand what is meant by a survey, a scientic study, and an observational study, and be able to distinguish them. 4. Understand what is meant...

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YOU WHAT NEED TO KNOW - Chapters 1-4.3 1. Know the basic steps involved in statistical methodology: collecting, summarizing, analyzing, and presenting data. 2. Know how to identify the population and sample in a study, and what the benets and drawbacks of sampling are. 3. Understand what is meant by a survey, a scientic study, and an observational study, and be able to distinguish them. 4. Understand what is meant by a simple random sample, and why variations of the SRS such as stratied random sampling, cluster sampling, or systematic random sampling are needed. 5. Be able to identify advantages and disadvantages of various types of data collection. 6. Understand the basic goals involved in experimental design, and the specic situations which lead to completely randomized, randomized block, and Latin Square designs. 7. What is the fundamental dierence in terms of what inferences can be drawn for an experimental and observational design? 8. Understand what factorial experiments are used for, and be able to make qualitative statements regarding the interaction between factors. 9. What is the placebo eect and how do we control for this eect? 10. Understand the dierence between response variables and explanatory variables. 11. Know some of the basic principles of experimental design, such as control, randomization, and replication, and understand what each of these does. 12. Understand the dierence between parameters and statistics and be able to identify them for a particular application. 13. Be able to explain how randomization guards against some types of bias. 14. Know what is meant by precision and accuracy of an estimator (statistic). 15. What is the relationship between sample size and sampling variability? 16. Know the basic goal of data summary. 17. Understand the dierence between values and variables. 18. Understand the dierence between quantitative and categorical random variables, and what types of summaries are appropriate for each. 19. Know when to use bar charts, stemplots, and histograms, and be able to interpret them. 20. Know how to identify long-term and seasonal trends in time series plots. 21. Be able to use these graphical tools to describe a given set of data in terms of its center, shape, variability, and outlying Know values. 22. how to nd measures of center such as the sample mode, sample mean, and sample median, and understand exactly what each of these tells you. 23. Be able to compute each of these for grouped data as well as ungrouped data. 24. Understand the relationship between the mean and median in the presence of outliers and in the presence of skewness. 25. Know how to compute trimmed means and be able to explain why they are computed in place of the mean. 26. Know how to compute the range, interquartile range (IQR), and standard deviation as measures of variability. 27. Know what are meant by percentiles and quartiles. 28. Be able to nd the 5-Number summary for a set of data and display it in a boxplot. 29. Understand what the quartiles relationship to the median and to the extreme values tell us about the shape of the data. 30. Be able to use the 1.5*IQR rule to test for outlying data values. 31. Understand the basic idea behind computing the standard deviation, and why we prefer the standard deviation to the variance as a measure of variation. 32. Understand what are meant by resistant and sensitive measures, and know which measures have these qualities. 33. Know what the Empirical Rule says, when to apply it, and how to interpret it. 34. What is the range approximation used for, and how accurate is it? 35. Be able to compute and ...

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University of Montana - MATH - 241
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University of Montana - MATH - 447
University of Montana - MATH - 444
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