Introduction. Visualizing Data

Question 1

What is spurious correlation?

Answer 1

A spurious correlation occurs when two variables are correlated but don’t have a causal relationship

Question 2

Omitted variable bias

Answer 2

It occurs when we do not include an independent variable in the model which has a causal effect on dependent variable

Question 3

Simpson’s Paradox

Answer 3

It is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined

Question 4

Unit of analysis

Answer 4

The observation described by a set of data. For example, voters,
parties, bills, elections, voting decisions, legislative output. Very often our data have multiple levels of analysis (e.g., individuals, regions, countries), calling for different statistical techniques

Question 5

Variables

Answer 5

Any characteristic related to the unit of analysis. A variable can take on different values for different observations

Question 6

Types of variables

Answer 6

e.g., nominal (e.g., political party), ordinal (e.g., school grades),
interval (e.g., GDP), ratio (e.g., duration)

Question 7

Data set

Answer 7

Set of variables for a given set of observations. Should come with a codebook

Question 8

Hypothesis

Answer 8

Statement about the nature of the social and political world, often
expressed as statements about relationships between variables (e.g., “The lower X,the higher Y”)

Question 9

Cross-section data

Answer 9

Sample of voters, governments, countries, or other units, taken at a given point in time. Observations are typically assumed to be independent

Question 10

Time series data

Answer 10

Observations on units over time, e.g., number of conflicts in country X. Because past events can influence future events and lags in behavior are prevalent in social sciences, time is an
important dimension in such a data set. Observations are not independent across time (serial correlation)

Question 11

Pooled time series cross-section data

Answer 11

Data consist of comparable time series data observed on
a variety of units. For instance, units are countries, and for each country we observe annual data on a variety of political and economic variables. Typically, we have few units, but long time series. Pooling the data increases the number of observations and makes it possible to control for exogenous shocks.
Observations are usually not independent.

Question 12

Panel data

Answer 12

A large number of the same cross-sectional units, e.g., survey respondents, are observed
repeatedly over a number of “waves” (interviews). With panel data, the time series is usually very short.
Common in studies of political behavior. For example, German Socio-Economic Panel (SOEP) or the GIP
(German Internet Panel) in Mannheim

Question 13

A histogram

Answer 13

It shows the distribution of the measurements of a variable, bar graph in which the height of the bar shows how many observations fall in particular subintervals (bins), plotted along the horizontal axis

Question 14

Density plot

Answer 14

Address the deficiencies
of histograms by averaging and smoothing, probability density function from the random variable X

Question 15

Measures of Central Tendency

Answer 15

Mode, Median, Mean

Question 16

Mode

Answer 16

Most frequently occurring value
of X

Question 17

Median

Answer 17

Value of X that falls in the
middle position when the observations
are ordered from smallest to largest.
Median = 50th percentile = 2nd quartile

Question 18

Mean

Answer 18

x =∑ni=1xi/n

Question 19

When mean=mode=median

Answer 19

In a perfectly symmetric distribution, e.g., normal distribution

Question 20

In right-skewed(positive skew) distribution what is greater: median or mean?

Answer 20

mean>median

Question 21

In left-skewed(negative skew) distribution what is greater: median or mean?

Answer 21

median > mean

Question 22

Who is sensitive to outliers: mean or median?

Answer 22

mean

Question 23

Sample Variance: definition and formula

Answer 23

Average of the squared deviations from the mean
S^2=sum of all(xi-(x_hat)) / n-1

Question 23

Sample Variance: definition and formula

Answer 23

Average of the squared deviations from the mean
S^2=∑i=1^n(xi-(x_hat))^2 / n-1

Question 24

Standard Deviation:definition and formula

Answer 24

Square-root of sample variance
s=√s^2

Question 25

Range: definition and formula

Answer 25

Difference between largest and smallest measurement:
RANGE = xMax − xMin

Question 26

Interquartile Range (IQR): definition and formula

Answer 26

Difference between upper and lower quartiles (range of
the middle 50% of the distribution)
QR = xQ3 − xQ1

Question 27

Q1 in boxplot

Answer 27

25 percentile

Question 28

Q3 in boxplot

Answer 28

75 percentile

Question 29

Q2 in boxplot

Answer 29

Median or 50 percentile

Question 30

Q0 in boxplot

Answer 30

0th percentile, lowest datapoint excluding outliers

Question 31

Q4 in boxplot

Answer 31

100th percentile, highest datapoint excluding outliers

Question 32

Lower Wisker

Answer 32

Q1-1.5(IQR)

Question 33

Upper Wisker

Answer 33

Q3+1.5(IQR)