Lecture4-note - Key concepts from week 1 Two types of...

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1 Key concepts from week 1 ± Observation, experiment ± Always select individuals randomly! ± Stratified design is for sampling/survey ± Block design is for experiment Two types of variables A variable can be either ± quantitative ± Something that can be counted or measured for each individual and then added, subtracted, averaged, etc., across individuals in the population. ± Example: How tall you are, your age, your blood cholesterol level, the number of credit cards you own. or ± categorical ± Something that falls into one of several categories. What can be counted is the count or proportion of individuals in each category. ± Example: Your blood type (A, B, AB, O), your hair color, your ethnicity, whether you paid income tax last tax year or not. Ways to chart categorical data ± Bar graphs Each category is represented by a bar. ± Pie charts Each category is represented by a slice that is proportional to its percentage. 0 100 200 300 400 500 600 700 800 Hea rt dise a se s Cancers Cerebrovascular Chronic respiratory Acc id e nts Di be te mel itu F lu & pn umon ia Alzheim r's disease Kidne y d is orde r Septicemia Counts (x1000) Bar graph sorted by rank Æ Easy to analyze Top 10 causes of death in the U.S., 2001 0 100 200 300 400 500 600 700 800 Accidents Alzheimer's disease C re b rov c u la Chronic sp irator D iabe m lit us Flu & p neu mo nia H ea rt d es Kidney disorders S pt icemia Sorted alphabetically Æ Much less useful Percent of deaths from top 10 causes Percent of deaths from all causes Make sure your labels match the data. Make sure all percents add up to 100. Not summarized enough Too summarized Ways to chart quantitative data
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2 Most common distribution shapes ± A distribution is symmetric if the right and left sides of the histogram are approximately mirror images of each other. Symmetric distribution Complex, multimodal distribution ± Not all distributions have a simple overall shape, especially when there are few observations. Skewed distribution ± A distribution is skewed to the right if the right side of the histogram (side with larger values) extends much farther out than the left side. It is skewed to the left if the left side of the histogram extends much farther out than the right side. Outliers An important kind of deviation is an outlier. Outliers are observations that lie outside the overall pattern of a distribution. Always look for outliers and try to explain them. IMPORTANT NOTE: Your data are the way they are. Do not try to force them into a particular shape. It is a common misconception that if you have a large enough data set, the data will eventually turn out nice and symmetrical. Line graphs: time plots This time plot shows a regular pattern of yearly variations. These are seasonal variations in fresh orange pricing most likely due to similar seasonal variations in the production of fresh oranges.
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Lecture4-note - Key concepts from week 1 Two types of...

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