Analytics Exam 1

Statistics

a way in which to understand the world

Descriptive Stats

used to organize/summarize a set of data

Inferential Stats

used to make interpretation based on data (draw conclusions about how the world works)

Population

a group of ppl or things that have some critical characteristic in common

• can’t be directly studied

solution to being limited by population

study a small group taken from a population (a sample)

Sample

a small subset of a population that is intended to be representative of the larger population

General Approach

collect data from sample —> analyze those sample data —> draw inferences

statistics

values measured from a sample

X

single score within sample

x̄

average (mean) of sample

μ

average mean of the population

Xi

X = score within the sample

i = position within the sample

Σx

the sum of all the scores in the sample

Σx²

the sum of all the squared scores in the sample

(Σx)²

sum of all the scores in the sample, squared

Order of Operations: PEMDAS

Parenthesis

Exponent

Multiplication

Division

Addition

Subtraction

Parameters

values from a population

Random Sampling

all members of population are equally likely to become part of the sample

• more likely sample is representative of the population

• (hard to do unless pop. is small and manageable)

Convenience Sampling

sample among individuals who are conveniently available to study

• this method is easier/accepted w/ discipline

• may not be representative of population

Poor Sampling

careless sampling = questionable results

Data Collection Characteristics

Independent Variable (or Quasi-Independent Variable)

Dependent Variable

Variable

characteristic or property of an event, person, etc, whose value is not constant

Dependent Variable (DV)

a variable that experimenter measures

• DP scores = “data” that gets analyzed

• Ex) response time, accuracy, etc

Independent Variable (IV)

variable experimenter manipulates

• determines whether IV has affect on DV

• takes on 2+ different values

• each value is a LEVEL or CONDITION of IV

Quasi-Independent Variable

variable researcher thinks will predict scores on the DV, but researcher does not manipulate

• don’t have same level of control as IV

• treated same as IV in this class (just know definition)

• Ex) ethnicity, age, gender

Continuous Variable

a theoretically infinite number of values exist between adjacent units

• Ex) reaction time, length, age

Discrete Variables

no values exist between adjacent units

• data = whole numbers or category labels

• Ex) college major, # of correct responses, etc.

Measurement Scales

variables can be measured using different ones of these

• Nominal Scales

• Ordinal

• Interval

• Ratio

Nominal Scale

values on the scale do not represent amounts of some variable

indicate category membership ONLY

• any value used to represent it are arbitrary

• Ex) hair color, occupation, major, fav food

IV focused, not really DV

Ordinal Scale

scores on the scale represent rank order (ordered categories)

• scores represent relative magnitude, NOT absolute magnitude

• Ex) ranking singers best to worst —> tells you who I like, not who is actually the best

Interval Scale

each score on the scale represents actual value

equal interval separating each score

can include negative values (but doesn’t mean absence)

• Ex) Fahrenheit scale

Ratio Scale

similar to an interval scale, except that it contains a true zero

NO negative values

can make ratio statements

• Ex) “length of A is twice the length of B”