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

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

## 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.