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Lecture 2

Lecture 2 - Jan 11.doc

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PSYC 305
Heungsun Hwang

PSYC305 Lecture 2 - Jan. 11 Population: The entire set of thing of interest • Parameter: A property descriptive of the population (i.e. population mean) Sample: The part of the population. Typically this provides the data we will look at • Estimate: A property of a sample (i.e. sample mean) Descriptive Statistics: • Summarize/describe the properties of samples (or populations when they are completely known) Inferential Statistics: Draw conclusions/make inferences about the properties of populations from sample data • Variable: • Something that varies • A condition or characteristic that can have different values • A constant is not a variable Types of Variables: • Nominal - cannot be ranked, non numeric, categorical (discrete/qualitative) • Ordinal - can be ranked, non numeric, categorical (discrete/qualitative) • Ratio - ranked, numeric, true zero, numerical (continuous/quantitative) Interval - ranked, numeric, no true zero, numerical (continuous/quantitative) • • Dependent variables (Y): • Outcomes/Responses • Predicted variables • Independent variables (X): • aka, factors in experimental designs • aka, predictors/covariates In this course, we focus on the relationships between one dependent variable and one/multiple indepen- dent variables: • DV - Continuous (normally distributed) • IVs - Categorical/continuous • Molson Ad: • DV = Continuous (Preference: 1-10) IV = Categorical (Ad: 0/1) • When looking at descriptive statistics, we look at: • Where is the center? (central tendency) • Mean • Median • Mode • What is the range? (variation) • Range • Variance • Standard Deviation • What is the shape of the distribution? (shape) • Skewness Mean: It is the
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