Class Notes (1,100,000)
CA (650,000)
SFU (10,000)
PSYC (1,000)
Lecture 1

PSYC 210 Lecture Notes - Lecture 1: Data Matrix, Statistical Inference


Department
Psychology
Course Code
PSYC 210
Professor
Joe Thompson
Lecture
1

This preview shows half of the first page. to view the full 2 pages of the document.
PSYC 210
Lecture 1
Jan. 8th
Definitions:
Random variable: a property that can take on different (at least 2) values (in it varies). These
values have associated probabilities and we can this talk about their associated probability
distributions
Symbolized as X Y
Discrete random variables are those that can only take on particular values that are made up
of disjointed categories
E.g., random variables cannot be something like 3.1
Not necessarily represented by numbers
Continuous random variables can take on values along an entire interval of the number line
and these values are not disjointed
E.g. random variable Y that can take on values between 2 and 5 and everything in
between (such as 4.14)
Data: numerical (or sometimes non-numerical) information collected by the researcher -
these are usually the observed values on random variables
Data matrix: organizes data in an array of columns/rows with order n x p
n = number of rows of the matrix (usually # of observations)
p = number of columns of the matrix (usually # of variables)
If p=1, the data is univariate
Population versus Sample
Census population: all individuals or objects of interest to a researcher
Almost impossible to achieve (?)
Statistical population: the entire set of possible outcomes on a variable of interest
and their associated probabilities and frequencies
Usually what we are talking about when we are talking about population
Sample: a subset or portion of scores or measurements taken from a population
Parameter versus Statistic
Parameters: numerical properties that describe statistical populations
E.g. parameters of the population - for example, population mean, population
variance, etc.
Statistics: real-valued quantities that describe various features of a data set (the
sample)
E.g. sample variance
Parameters are to populations as statistics are to samples
Empirical vs. Theoretical Distributions:
Empirical distributions: based on observed (raw) data
find more resources at oneclass.com
find more resources at oneclass.com
You're Reading a Preview

Unlock to view full version