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Lecture 3

Crim 320 - week 3.docx

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Simon Fraser University
CRIM 320
Rebecca Carleton

Crim 320 – Week 3 Objectives - Identify and correct violations of the assumption regarding normality of data - Detect and remedy outliers - Check the accuracy of your data - Cope with missing data - All of the above fall under diagnostics Internal validity, measuring what you’re supposed External validity = generizable to the greater population Assumptions not met = results are garbage T-tests = compare two averages, two means Rely on the mathematical average being the best measure of central tendency Information about skewness and kurtosis (see table in handout) i Not trying to make variable perfectly normal, it just needs to be close enough. How do we determine close enough Lepto kurtosis = when the data is pushed up in the center, also is positive kurtosis versus plateo kurtosis = pushed down, is also negative 95% CI = statistic± 1.96*(SE of statistic)  Important formula Example of 95% CI - Skewness - 95% CI =5.063 ± 1.96 *(0.241) - =5.063 ± 0.472 (both add and subtract) - =4.591 to 5.535 (produces a range, zero needs to falls within that range) - Kurtosis - 95%CI =30.197 ± 1.96 *(0.478) - =30.197 ± 0.937 - =29.26 to 31.13 Remedies for skewness and Kurtosis, two solutions for this class 1. Converting to rates 2. Transformations Converting to rates has additional benefits; it reduces t
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