Class Notes (810,035)
SOAN 2120 (389)
Lecture

September 25.docx

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School
University of Guelph
Department
Sociology and Anthropology
Course
SOAN 2120
Professor
David Walters
Semester
Fall

Description
September 25, 2012 Standards Deviants - measure of central tendency - measures of dispersion - population versus sample - z-scores and probability Video Statistics – language test – simple math – relationship b/w terms - Taking a portion out of that data and studying it - Small groups tell us about big groups - How to make sense of data Statistical Problems - exists when something is unknown about a population - a population is the group you are interested in learning about o population can be animate or inanimate – it’s just a group o large group you gather the data from - inferences – drawing conclusions from a piece of data - sample – randomly selected portion of the population - step 1 – Identify question being asked - step 2 – Determine design of experiment (use whole population or sample?) - step 3 – establish a method to collect and Analyze data o data set - step 4 – choose a procedure for making Inferences - step 5 – see how Reliable your inferences are (test accuracy) - “IDAIR” – identify, determine, analyze, inference, reliable Graphical Display - relative frequency histogram o divides the data into intervals/classes o put data into those classes o box-like display of data o classes, class width, class boundaries, class frequency, class relative frequency etc. o knowing the range = how many classes to use  too few classes = not good. Values very different in size are limped together and then the histogram is too big  too many classes = most empty, histogram becomes misleading o class width – how wide classes are --- divide range by the number of classes we want – if it’s not a whole number, round up  all classes need to be of equal width  class boundaries – where class starts and ends - distribution curve o essentially the same as histogram o bigger data sets, looks like a bell - class relative frequency = number of measurements in a class divided by total number of measurements - when the data set is larger, we use a distribution curve Stem a
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