PSYC 305 Lecture Notes - Lecture 2: Descriptive Statistics, Categorical Variable, Standard Deviation

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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
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Document Summary

Population: the entire set of thing of interest: parameter: a property descriptive of the population (i. e. population mean) 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. 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.

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