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Lecture

# STAT 151 (09/06/13) - Lecture 1

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School
Department
Statistics
Course
STAT151
Professor
Paul Cartledge
Semester
Fall

Description
STAT 151 – Lecture Notes (September 6, 2013) Lecture 1 Statistics: 1. Numerical facts 2. A field of discipline ***In this course, statistics is about variation*** 3 Main Aspects: 1) Design ("Think”): Plan what kind of data-gathering should take place in order to be able to answer questions. 2) Description (“Show”): Summary made from obtained data 3) Inference (“Tell”): Use the data gathered to make decisions and predictions. Definitions: Population – consists of all elements who have characteristics that are being studied Sample – a portion of the population selected for a study (ex. Science students out of all students) Parameter – summary measure calculated for POPULATION data Statistic – summary measure calculated for SAMPLE data (, y, x)  always given Latin letters for values Variable – a characteristic that is recorded for the purpose of a study Qualitative – (aka categorical) – something that cannot assume a numerical value BUT can be sorted into 2 or more categories  Ex. gender, smell, colour Quantitative – (numerical) – measured numerically  Two types: o Discrete: only certain numerical values are available and not intermediate values (Ex. # of days each month) o Continuous: includes any numerical value over a certain interval or intervals (Ex. GPA, gas prices) ***KEY*** POPULATION goes with PARAMETER SAMPLE goes with STATISTIC (They start with the same letters) Types of Statistics Descriptive – method to view a given dataset  Favored graphs: Averages and histograms Inferential – method that uses SAMPLE results to infer a predicted conclusion about a LARGER population  Favored graphs: t-tests, simple linear reg
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