BUSN5000 Midterm 1

Mutate

Tidyverse function for creating new variables.

Filter

Tidyverse function for including data matching a certain condition

Select

Tidyverse function for keeping/dropping variables

Group_By

Tidyverse function for categorizing data

Summarise

Tidyverse function for computing the basic summary statistics

Population Average

The big average we’re trying to figure out

Sample average

The average of the population we have.

records

A data set is made up of _____ that contain information on a specific entity.

fields

Each record is made of _____ that contain measurements of known types.

Panel Data

Data collected over time on multiple entities, such as individuals, firms, or countries.

Acquire, Transform, Analyze, Communicate

The 4 stages of analysis

Cross Section

Many units observed at a particular time

Time Series

A single unit observed over multiple time periods

Data Set

Multiple data tables structured for a particular analysis

Database

A collection of tables where each table has some known and meaningful relationship to the other tables.

Volume

A word to describe the literal size and scale of data

Velocity

A word to describe the speed of generation, collection, and storage of data

Variety

A word to describe the complexity of sources and forms of data

Veracity

A word to describe the degree of consistency and completeness of data.

Content

What a variable measures

Validity

Whether a variable measures what its supposed to measure

Reliability

Whether repeated measurements return the same value

Comparability

Whether a variable is measured the same way across units

Coverage

Whether all units intended for inclusion are included

Selection

Whether selected units are representative of those not covered

Data Schema

A representation of the data structure that comprises all the attributes of the data and their data types

Vector

A sequence of data elements of the same type

Matrix

A two-dimensional array of data elements of the same type

Data Frame

A tabular data structure

List

An ordered collection of objects

Factor

A vector that can contain only predefined values, and is used to store categorical data

Array

A multidimensional collection of same-type data elements.

Frequentism

The approach to thinking that states the probability of some event happening is the number of times it happens over the number of random trials

Law of Large Numbers

The idea that the more trials you use, the closer your data gets to being exactly accurate.

Estimand

The thing we want to estimate

Estimator

The formulas we use to make an estimate

Estimate

Our best guess for something, with bias and sampling error.

Measurement error

When a variable’s empirical measurement does not accurately capture the thing we are interested in

CEF

The workhorse of data science, has the expected value (average) of a variable given another variable.

Law of Iterated Expectations

The law that states the unconditional expectation is equal to the weighted average of conditional expectations. E(Y) = E(Y|X)

Covariance

Indicates the strength of a relationship

Human Capital Theory

Models education as an investment much like you would for any other capital asset, predicts age-earnings profile will be concave.

Consistency

The bias and sampling error approach zero as sample size increases

Central Limit Theory

THe Theory that under random sampling, given enough data, a random variable will approach a normal distribution.

Confidence Interval

how likely an estimate is close to its target in the population

Item Nonresponse

When data is missing because respondents refused to provide it

Unit Nonresponse

When data is missing because of people that the data was not collected from

Missing Completely at Random

Sampling error is completely independent of X and Y.

Missing at Random

Selection into a dataset depends on X, but not other unobserved factors.

Exogenous

Anything that went wrong with sampling is external.

Endogenous

Anything that went wrong with sampling is internal.

Imputation

The process of filling in the missing values based on data you observe

Simpson’s Paradox

The idea that there is a lurking third variable that effects correlations

Bayes’ Rule

A mathematical formula used to update the probability of a hypothesis based on new evidence or information. It calculates the probability of an event occurring given prior knowledge and new data.