BUSN5000 Midterm 1

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Business

54 Terms

1

Mutate

Tidyverse function for creating new variables.

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2

Filter

Tidyverse function for including data matching a certain condition

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3

Select

Tidyverse function for keeping/dropping variables

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4

Group_By

Tidyverse function for categorizing data

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5

Summarise

Tidyverse function for computing the basic summary statistics

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6

Population Average

The big average we’re trying to figure out

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7

Sample average

The average of the population we have.

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8

records

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

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9

fields

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

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10

Panel Data

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

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11

Acquire, Transform, Analyze, Communicate

The 4 stages of analysis

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12

Cross Section

Many units observed at a particular time

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13

Time Series

A single unit observed over multiple time periods

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14

Data Set

Multiple data tables structured for a particular analysis

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15

Database

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

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16

Volume

A word to describe the literal size and scale of data

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17

Velocity

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

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18

Variety

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

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19

Veracity

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

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20

Content

What a variable measures

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21

Validity

Whether a variable measures what its supposed to measure

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22

Reliability

Whether repeated measurements return the same value

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23

Comparability

Whether a variable is measured the same way across units

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24

Coverage

Whether all units intended for inclusion are included

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25

Selection

Whether selected units are representative of those not covered

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26

Data Schema

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

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27

Vector

A sequence of data elements of the same type

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28

Matrix

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

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29

Data Frame

A tabular data structure

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30

List

An ordered collection of objects

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31

Factor

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

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32

Array

A multidimensional collection of same-type data elements.

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33

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

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34

Law of Large Numbers

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

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35

Estimand

The thing we want to estimate

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36

Estimator

The formulas we use to make an estimate

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37

Estimate

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

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38

Measurement error

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

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39

CEF

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

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40

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)

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41

Covariance

Indicates the strength of a relationship

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42

Human Capital Theory

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

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43

Consistency

The bias and sampling error approach zero as sample size increases

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44

Central Limit Theory

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

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45

Confidence Interval

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

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46

Item Nonresponse

When data is missing because respondents refused to provide it

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47

Unit Nonresponse

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

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48

Missing Completely at Random

Sampling error is completely independent of X and Y.

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49

Missing at Random

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

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50

Exogenous

Anything that went wrong with sampling is external.

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51

Endogenous

Anything that went wrong with sampling is internal.

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52

Imputation

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

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53

Simpson’s Paradox

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

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54

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.

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