DE Glossary

box and whisker plot

A graphic display of the range and quartiles of a distribution, where the first and third quartile form the ‘box’ and the maximum and minimum values form the ‘whiskers’ (DE). Introduced in 6. Measuring management practices Working in Excel, Getting started in R, 6. Measuring management practices Working in Google Sheets, Part 6.1 Looking for patterns in the survey data, 10. Characteristics of banking systems around the world Working in Excel, Part 10.1 Summarizing the data, Part 10.1 Summarizing the data, Part 10.1 Summarizing the data.

business cycle

Alternating periods of faster and slower (or even negative) growth rates. The economy goes from boom to recession and back to boom. Alternating periods of faster and slower (or even negative) growth rates. The economy goes from boom to recession and back to boom (Macro, DE, TE1, TESA). Introduced in Introduction, Macro, TE1, TESA. See also: short-run equilibrium.

causation

A direction from cause to effect, establishing that a change in one variable produces a change in another. While a correlation gives an indication of whether two variables move together (either in the same or opposite directions), causation means that there is a mechanism that explains this association. Example: We know that higher levels of CO2 in the atmosphere lead to a greenhouse effect, which warms the Earth’s surface. Therefore we can say that higher CO2 levels are the cause of higher surface temperatures (DE). Introduced in Part 1.3 Carbon emissions and the environment, Part 1.2 Variation in temperature over time, Part 1.3 Carbon emissions and the environment, Part 1.3 Carbon emissions and the environment.

conditional mean

An average of a variable, taken over a subgroup of observations that satisfy certain conditions, rather than all observations (DE). Introduced in Part 3.1 Before-and-after comparisons of retail prices, Getting started in R, Part 3.1 Before-and-after comparisons of retail prices, Getting started in Python, Part 9.1 Households that did not get a loan, Part 9.1 Households that did not get a loan, Part 9.1 Households that did not get a loan, Part 9.1 Households that did not get a loan.

confidence interval

A range of values centred around the sample mean value, with a corresponding percentage (usually 9%, 95%, or 99%). When we use a sample to calculate a 95% confidence interval, there is a probability of .95 that we will get an interval containing the true value of interest (DE). Introduced in Part 6.2 Do management practices differ between countries?, Part 6.2 Do management practices differ between countries?, Part 6.2 Do management practices differ between countries?, Part 6.2 Do management practices differ between countries?.

contingent valuation

A survey-based technique used to assess the value of non-market resources. Also known as: stated-preference model (DE, TE1, TESA). Introduced in Introduction, TE1.

correlation

Two variables in a sample of data are said to be correlated if we observe that they tend to change together. If high values of one variable (e.g. people’s earnings) commonly occur along with high values of another variable (e.g. years of education) the variables are positively correlated. When high values of one variable (e.g. ice cream sales) are associated with low values of the other variable (e.g. number of people wearing winter coats) there is a negative correlation. If variables are correlated, it doesn’t mean that there is a causal relationship between them: higher ice cream sales might not have caused fewer people to wear winter coats. A statistical association in which knowing the value of one variable provides information on the likely value of the other, for example high values of one variable being commonly observed along with high values of the other variable. It can be positive or negative (it is negative when high values of one variable are observed with low values of the other). It does not mean that there is a causal relationship between the variables. A measure of how closely related two variables are. Two variables are correlated if knowing the value of one variable provides information on the likely value of the other, for example high values of one variable being commonly observed along with high values of the other variable. Correlation can be positive or negative. It is negative when high values of one variable are observed with low values of the other. Correlation does not mean that there is a causal relationship between the variables. Example: When the weather is hotter, purchases of ice cream are higher. Temperature and ice cream sales are positively correlated. On the other hand, if purchases of hot beverages decrease when the weather is hotter, we say that temperature and hot beverage sales are negatively correlated (Micro, Macro, ESPP, DE, TE1, TESA). Introduced in Part 1.3 Carbon emissions and the environment, Part 1.2 Variation in temperature over time, Part 1.3 Carbon emissions and the environment, Part 1.3 Carbon emissions and the environment, Micro, Macro, Macro, TE1, TESA. See also: causality, correlation coefficient.

correlation coefficient

A measure of how closely associated two variables are and whether they tend to take similar or dissimilar values, ranging from a value of 1 indicating that the variables take similar values (‘are positively correlated’) to –1 indicating that the variables take dissimilar variables (‘negative’ or ‘inverse’ correlation). A value of 1 or –1 indicates that knowing the value of one of the variables would allow you to perfectly predict the value of the other. A value of 0 indicates that knowing one of the variables provides no information about the value of the other. A numerical measure, ranging between 1 and −1, of how closely associated two variables are—whether they tend to rise and fall together, or move in opposite directions. A positive coefficient indicates that when one variable takes a high (low) value, the other tends to be high (low) too, and a negative coefficient indicates that when one variable is high the other is likely to be low. A value of 1 or −1 indicates that knowing the value of one of the variables would allow you to perfectly predict the value of the other. A value of indicates that knowing one of the variables provides no information about the value of the other (ESPP, DE, TE1, TESA). Introduced in Part 1.3 Carbon emissions and the environment, Part 1.2 Variation in temperature over time, Part 1.3 Carbon emissions and the environment, Part 1.3 Carbon emissions and the environment, Part 8.2 Visualizing the data, Part 8.2 Visualizing the data, Part 8.2 Visualizing the data, Getting started in Python. See also: correlation, causality.

credit-constrained, credit constrained

A description of individuals who are able to borrow only on unfavourable terms (ESPP, DE, TE1, TESA). Introduced in TE1, TE1, TESA, TESA, ESPP. See also: credit-excluded.

credit-excluded, credit excluded

A description of individuals who are unable to borrow on any terms (ESPP, DE, TE1, TESA). Introduced in TE1, TE1, TESA, TESA, ESPP. See also: credit-constrained.

Cronbach's alpha

A measure used to assess the extent to which a set of items is a reliable or consistent measure of a concept. This measure ranges from –1, with meaning that all of the items are independent of one another, and 1 meaning that all of the items are perfectly correlated with each other (DE). Introduced in Part 11.1 Summarizing the data, Getting started in R, Learning objectives for this part, Getting started in Python.

cross-sectional data

Data that is collected from participants at one point in time or within a relatively short time frame. In contrast, time series data refers to data collected by following an individual (or firm, country, etc.) over a course of time. Example: Data on degree courses taken by all the students in a particular university in 216 is considered cross-sectional data. In contrast, data on degree courses taken by all students in a particular university from 199 to 216 is considered time series data (DE). Introduced in Part 4.1 GDP and its components as a measure of material wellbeing, Part 4.1 GDP and its components as a measure of material wellbeing, Part 4.1 GDP and its components as a measure of material wellbeing, Part 4.1 GDP and its components as a measure of material wellbeing.

cyclical component

The short-run (business cycle) fluctuations around the long-run trend component of a macroeconomic time series. Commonly obtained using the Hodrick–Prescott (HP) filter (DE). Introduced in Part 1: Collecting and preparing the data, Part 2: Links between female labour supply and the macroeconomy, Part 1: Collecting and preparing the data.

decile

A subset of observations, formed by ordering the full set of observations according to the values of a particular variable, and then splitting the set into ten equally-sized groups. For example, the 1st decile refers to the smallest 1% of values in a set of observations. A subset of observations, formed by ordering the full set of observations according to the values of a particular variable and then splitting the set into ten equally-sized groups. For example, the 1st decile refers to the smallest 10% of values in a set of observations (Macro, ESPP, DE). Introduced in Macro, Macro, ESPP, ESPP. See also: percentile.

deflation

A decrease in the general price level (Macro, ESPP, DE, TE1, TESA). Introduced in Macro, TE1, TE1, TE1, TESA, TESA. See also: inflation.

differences-in-differences

A method that applies an experimental research design to outcomes observed in a natural experiment. It involves comparing the difference in the average outcomes of two groups, a treatment and control group, both before and after the treatment took place (DE). Introduced in Introduction.

disinflation

A decrease in the rate of inflation (Macro, ESPP, DE, TE1, TESA). Introduced in Macro, TE1, TESA. See also: inflation, deflation.

dummy variable (indicator variable)

A variable that takes the value 1 if a certain condition is met, and otherwise (DE). Introduced in Part 1.2 Variation in temperature over time, Part 7.1 Drawing supply and demand diagrams, Part 7.2 Interpreting supply and demand curves, Part 7.1 Drawing supply and demand diagrams, Part 7.1 Drawing supply and demand diagrams, Part 9.1 Households that did not get a loan, Part 9.1 Households that did not get a loan, Part 1: Collecting and preparing the data, Part 1: Collecting and preparing the data, Part 1: Collecting and preparing the data.

endogenous

Endogenous means ‘generated by the model’. In an economic model, a variable is endogenous if its value is determined by the workings of the model (rather than being set by the modeller). Produced by the workings of a model rather than coming from outside the model (Micro, Macro, ESPP, DE, TE1, TESA). Introduced in Part 7.1 Drawing supply and demand diagrams, Part 7.2 Interpreting supply and demand curves, Part 7.1 Drawing supply and demand diagrams, Part 7.1 Drawing supply and demand diagrams, Micro, Micro, TE1, TESA. See also: exogenous.

exogenous

Exogenous means ‘generated outside the model’. In an economic model, a variable is exogenous if its value is set by the modeller, rather than being determined by the workings of the model itself. Coming from outside the model rather than being produced by the workings of the model itself (Micro, Macro, ESPP, DE, TE1, TESA). Introduced in Part 7.1 Drawing supply and demand diagrams, Part 7.1 Drawing supply and demand diagrams, Part 7.1 Drawing supply and demand diagrams, Part 7.1 Drawing supply and demand diagrams, Micro, Micro, Micro, Macro, Macro, TE1, TE1, TE1, TESA, TESA, TESA, ESPP. See also: endogenous.

frequency table

A record of how many observations in a dataset have a particular value, range of values, or belong to a particular category (DE). Introduced in Part 1.2 Variation in temperature over time, Part 1.2 Variation in temperature over time, Part 1.2 Variation in temperature over time, Part 1.2 Variation in temperature over time.

geometric mean

A summary measure calculated by multiplying N numbers together and then taking the Nth root of this product. The geometric mean is useful when the items being averaged have different scoring indices or scales, because it is not sensitive to these differences, unlike the arithmetic mean. For example, if education ranged from to 2 years and life expectancy ranged from to 85 years, life expectancy would have a bigger influence on the HDI than education if we used the arithmetic mean rather than the geometric mean. Conversely, the geometric mean treats each criteria equally. Example: Suppose we use life expectancy and mean years of schooling to construct an index of wellbeing. Country A has life expectancy of 4 years and a mean of 6 years of schooling. If we used the arithmetic mean to make an index, we would get (4 + 6)/2 = 23. If we used the geometric mean, we would get (4 × 6)1/2 = 15.5. Now suppose life expectancy doubled to 8 years. The arithmetic mean would be (8 + 6)/2 = 43, and the geometric mean would be (8 × 6)1/2 = 21.9. If, instead, mean years of schooling doubled to 12 years, the arithmetic mean would be (4 + 12)/2 = 26, and the geometric mean would be (4 × 12)1/2 = 21.9. This example shows that the arithmetic mean can be ‘unfair’ because proportional changes in one variable (life expectancy) have a larger influence over the index than changes in the other variable (years of schooling). The geometric mean gives each variable the same influence over the value of the index, so doubling the value of one variable would have the same effect on the index as doubling the value of another variable (DE). Introduced in Part 4.1 GDP and its components as a measure of material wellbeing, Part 4.1 GDP and its components as a measure of material wellbeing, Part 4.2 The HDI as a measure of wellbeing, Part 4.2 The HDI as a measure of wellbeing.

Gini coefficient

A measure of inequality of a quantity such as income or wealth, varying from a value of zero (if there is no inequality) to one (if a single individual receives all of it). It is the average difference in, say, income between every pair of individuals in the population relative to the mean income, multiplied by one-half. Other than for small populations, a close approximation to the Gini coefficient can be calculated from a Lorenz curve diagram. A measure of inequality of any quantity such as income or wealth, varying from a value of zero (if there is no inequality) to one (if a single individual receives all of it) (Micro, Macro, ESPP, DE, TE1, TESA). Introduced in Introduction, Micro, Micro, Macro, Macro, TE1, TESA, ESPP, ESPP. See also: Lorenz curve.

great moderation

A period of low volatility in aggregate output in advanced economies between the 198s and the 28 financial crisis. The name was suggested by James Stock and Mark Watson, the economists, and popularized by Ben Bernanke, then chairman of the Federal Reserve. Period of low volatility in aggregate output in advanced economies between the 198s and the 28 financial crisis. The name was suggested by James Stock and Mark Watson, the economists, and popularized by Ben Bernanke, then chairman of the Federal Reserve (DE, TE1, TESA). Introduced in Part 1: Collecting and preparing the data, Part 2: Links between female labour supply and the macroeconomy, Part 1: Collecting and preparing the data, TE1, TE1, TE1, TESA, TESA.

Hodrick-Prescott (HP) filter

A mathematical tool used by macroeconomists to estimate the cyclical and trend components of time series data. Its main purpose is to fit a smooth curve (the trend) through the time series, where the trend reacts more to long-term fluctuations than to short-term fluctuations (the latter will mostly affect the cyclical component). The HP filter uses a parameter λ (‘lambda’) to dictate how sensitive this trend is to short-term fluctuations. This lambda needs to be chosen depending on the frequency of the data; popular values are λ = 6.25 for annual and λ = 16, for quarterly data (DE). Introduced in Part 1: Collecting and preparing the data, Part 2: Links between female labour supply and the macroeconomy, Part 1: Collecting and preparing the data.

hypothesis test

A test in which a null (default) and an alternative hypothesis are posed about some characteristic of the population. Sample data is then used to test how likely it is that these sample data would be seen if the null hypothesis was true (DE). Introduced in Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?.

incomplete contract

A contract that does not specify, in a way that can be enforced by a court, every aspect of the exchange that affects the interests of parties to the exchange (or of others). A contract that does not specify, in an enforceable way, every aspect of the exchange that affects the interests of parties to the exchange (or of any others affected by the exchange). A contract that does not specify, in an enforceable way, every aspect of the exchange that affects the interests of parties to the exchange (or of others) (Micro, Macro, ESPP, DE, TE1, TESA). Introduced in Micro, Micro, Micro, Micro, TE1, TE1, TE1, TESA, TESA, TESA, ESPP, ESPP, ESPP.

index

An index is formed by aggregating the values of multiple items into a single value, and is used as a summary measure of an item of interest. Example: The HDI is a summary measure of wellbeing, and is calculated by aggregating the values for life expectancy, expected years of schooling, mean years of schooling, and gross national income per capita. A measure of the amount of something in one period of time, compared to the amount of the same thing in a different period of time, called the reference period or base period. It is common to set its value at 1 in the reference period (DE, TE1, TESA). Introduced in Introduction, TESA.

inflation

An increase in the general price level in the economy, usually measured as the percentage increase in prices over the last year. An increase in the general price level in the economy. Usually measured over a year (Macro, ESPP, DE, TE1, TESA). Introduced in Part 12.1 Inequality, Getting started in R, Part 12.1 Inequality, Getting started in Python, Macro, TE1, TE1, TESA, TESA, ESPP. See also: deflation, disinflation.

jobless recovery

A macroeconomic phenomenon in which employment grows slowly (or not at all) during a post-recession economic recovery, thereby making the recovery ‘jobless’. One example is from the US, where recoveries have been jobless from the 199s onwards (DE). Introduced in Part 1: Collecting and preparing the data, Part 2: Links between female labour supply and the macroeconomy, Part 1: Collecting and preparing the data.

leverage ratio (for banks or households)

The value of assets divided by the equity stake in those assets. The value of assets divided by the equity stake (capital contributed by owners and shareholders) in those assets (ESPP, DE, TE1, TESA). Introduced in 10. Characteristics of banking systems around the world Working in Excel, Part 10.1 Summarizing the data, Part 10.1 Summarizing the data, Part 10.1 Summarizing the data, TE1, TE1, TESA, ESPP.

leverage ratio (for non-bank companies)

The value of total liabilities divided by total assets (DE, TE1, TESA).

Likert scale

A numerical scale (usually ranging from 1–5 or 1–7) used to measure attitudes or opinions, with each number representing the individual’s level of agreement or disagreement with a particular statement (DE). Introduced in Part 11.1 Summarizing the data, Getting started in R, Learning objectives for this part, Getting started in Python.

logarithmic scale

A way of measuring a quantity based on the logarithm function, f(x) = log(x). The logarithm function converts a ratio to a difference: log (a/b) = log a – log b. This is very useful for working with growth rates. For instance, if national income doubles from 5 to 1 in a poor country and from 1, to 2, in a rich country, the absolute difference in the first case is 5 and in the second 1,, but log(1) – log(5) = .693, and log(2,) – log(1,) = .693. The ratio in each case is 2 and log(2) = .693 (DE, TE1, TESA). Introduced in TE1, TESA.

Lorenz curve

A graphical representation of the inequality of some quantity such as income or wealth. Taking income as an example, individuals in the population are arranged in ascending order of income. First we calculate the total income of the population. Then for each level of income, we plot the percentage of total income held by people at this income level or lower, against the percentage of people at this income level or lower. The area between the Lorenz curve and the 45-degree line, expressed as a fraction of the total area below the 45-degree line, is a measure of inequality. Other than for small populations, it is a close approximation to the Gini coefficient. A graphical representation of inequality of some quantity such as wealth or income. Individuals are arranged in ascending order by how much of this quantity they have, and the cumulative share of the total is then plotted against the cumulative share of the population. For complete equality of income, for example, it would be a straight line with a slope of one. The extent to which the curve falls below this perfect equality line is a measure of inequality (Micro, Macro, ESPP, DE, TE1, TESA). Introduced in Introduction, Macro, TE1, TESA, ESPP, ESPP. See also: Gini coefficient.

mean

A summary statistic for a set of observations, calculated by adding all values in the set and dividing by the number of observations (ESPP, DE). Introduced in Part 2.2 Describing the data, Part 2.1 Collecting data by playing a public goods game, 2. Collecting and analysing data from experiments Working in Google Sheets, Part 2.1 Collecting data by playing a public goods game, ESPP.

median

When a set of observations is arranged in order, the median is in the middle: half of the observations are above it, and half below. (More precisely, if the number of observations is odd, the median is the value of middle observation; if the number of obeservations is even, the median is the value halfway between the two middle observations.) The middle number in a set of values, such that half of the numbers are larger than the median and half are smaller. Also known as: 5th percentile (Macro, ESPP, DE). Introduced in Macro, Macro, Macro, ESPP.

natural experiment

An empirical study that exploits a difference in the conditions affecting two populations (or two economies), that has occurred for external reasons: for example, differences in laws, policies, or weather. Comparing outcomes for the two populations gives us useful information about the effect of the conditions, provided that the difference in conditions was caused by a random event. But it would not help, for example, in the case of a difference in policy that occurred as a response to something else that might affect the outcome. An empirical study exploiting naturally occurring statistical controls in which researchers do not have the ability to assign participants to treatment and control groups, as is the case in conventional experiments. Instead, differences in law, policy, weather, or other events can offer the opportunity to analyse populations as if they had been part of an experiment. The validity of such studies depends on the premise that the assignment of subjects to the naturally occurring treatment and control groups can be plausibly argued to be random (Micro, Macro, ESPP, DE, TE1, TESA). Introduced in Part 1.3 Carbon emissions and the environment, Part 1.2 Variation in temperature over time, Part 1.3 Carbon emissions and the environment, Part 1.3 Carbon emissions and the environment, Introduction, Micro, Micro, Macro, Macro, Macro, TE1, TE1, TE1, TESA, TESA, TESA, ESPP, ESPP, ESPP, ESPP.

natural logarithm

See: logarithmic scale (DE, TE1, TESA). See also: logarithmic scale.

nominal wage

The actual amount received in payment for work, per unit of time, expressed in a particular currency. Also known as: money wage. The actual amount received in payment for work, in a particular currency. Also known as: money wage. The actual amount received in payment for work, in a particular currency. Also known as: money wage (Macro, ESPP, DE, TE1, TESA). Introduced in Macro, Macro, TE1, TESA, ESPP. See also: real wage.

p-value

The probability of observing data at least as extreme as the data collected if a particular hypothesis about the population is true. The p-value ranges from to 1: the lower the probability (the lower the p-value), the less likely it is to observe the given data, and therefore the less compatible the data are with the hypothesis (DE). Introduced in Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?.

percentile

A subset of observations, formed by ordering the full set of observations according to the values of a particular variable and then splitting the set into one hundred equally-sized groups. For example, the 1st percentile refers to the smallest 1% of values in a set of observations. A subset of observations, formed by ordering the full set of observations according to the values of a particular variable and then splitting the set into ten equally-sized groups. For example, the 1st percentile refers to the smallest 1% of values in a set of observations (ESPP, DE). See also: decile.

principal-agent relationship

This is an asymmetrical relationship in which one party (the principal) benefits from some action or attribute of the other party (the agent) about which the principal’s information is not sufficient to enforce in a complete contract. This relationship exists when one party (the principal) would like another party (the agent) to act in some way, or have some attribute that is in the interest of the principal, and that cannot be enforced or guaranteed in a binding contract. This relationship exists when one party (the principal) would like another party (the agent) to act in some way, or have some attribute that is in the interest of the principal, and that cannot be enforced or guaranteed in a binding contract. Also known as: principal–agent problem (ESPP, DE, TE1, TESA). Introduced in Introduction, TE1, TE1, TE1, TE1, TE1, TE1, TESA, TESA, TESA, TESA, ESPP, ESPP, ESPP, ESPP, ESPP. See also: incomplete contract.

range

The interval formed by the smallest (minimum) and the largest (maximum) value of a particular variable. The range shows the two most extreme values in the distribution, and can be used to check whether there are any outliers in the data. (Outliers are a few observations in the data that are very different from the rest of the observations.) (DE). Introduced in Part 2.2 Describing the data, Part 2.1 Collecting data by playing a public goods game, 2. Collecting and analysing data from experiments Working in Google Sheets, Part 2.1 Collecting data by playing a public goods game.

real wage

The wage expressed in terms of the amount of goods and services the worker can buy with it. It is calculated by dividing the nominal wage by the current price level in the same currency. The nominal wage, adjusted to take account of changes in prices between different time periods. It measures the amount of goods and services the worker can buy (Macro, ESPP, DE, TE1, TESA). Introduced in Macro, Macro, TE1, TE1, TESA, TESA, ESPP. See also: nominal wage.

selection bias

An issue that occurs when the sample or data observed is not representative of the population of interest. For example, individuals with certain characteristics may be more likely to be part of the sample observed (such as students being more likely than CEOs to participate in computer lab experiments) (DE). Introduced in Part 9.1 Households that did not get a loan, Part 9.1 Households that did not get a loan, Part 9.1 Households that did not get a loan, Part 9.1 Households that did not get a loan.

significance level

A cut-off probability that determines whether a p-value is considered statistically significant. If a p-value is smaller than the significance level, it is considered unlikely that the differences observed are due to chance, given the assumptions made about the variables (for example, having the same mean). Common significance levels are 1% (p-value of 0.01), 5% (p-value of 0.05), and 10% (p-value of 0.1) (DE). See also: statistically significant, p-value.

simultaneity

When the right-hand and left-hand variables in a model equation affect each other at the same time, so that the direction of causality runs both ways. For example, in supply and demand models, the market price affects the quantity supplied and demanded, but quantity supplied and demanded can in turn affect the market price (DE). Introduced in Part 7.1 Drawing supply and demand diagrams, Part 7.2 Interpreting supply and demand curves, Part 7.1 Drawing supply and demand diagrams, Part 7.1 Drawing supply and demand diagrams.

spurious correlation

A strong linear association between two variables that does not result from any direct relationship, but instead may be due to coincidence or to another unseen factor (DE). Introduced in Part 1.3 Carbon emissions and the environment, Part 1.2 Variation in temperature over time, Part 1.3 Carbon emissions and the environment, Part 1.3 Carbon emissions and the environment, Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?, Part 2.3 How did changing the rules of the game affect behaviour?.

standard deviation

A measure of dispersion in a frequency distribution, equal to the square root of the variance. The standard deviation has a similar interpretation to the variance. A larger standard deviation means that the data is more spread out. Example: The set of numbers 1, 1, 1 has a standard deviation of zero (no variation or spread), while the set of numbers 1, 1, 999 has a standard deviation of 46.7 (large spread) (DE). Introduced in Part 2.2 Describing the data, Part 2.1 Collecting data by playing a public goods game, 2. Collecting and analysing data from experiments Working in Google Sheets, Part 2.1 Collecting data by playing a public goods game.

standard error

A measure of the degree to which the sample mean deviates from the population mean. It is calculated by dividing the standard deviation of the sample by the square root of the number of observations (DE). Introduced in Getting started in R, Part 6.2 Do management practices differ between countries?.

statistically significant

When a relationship between two or more variables is unlikely to be due to chance, given the assumptions made about the variables (for example, having the same mean). Statistical significance does not tell us whether there is a causal link between the variables (DE). Introduced in Part 3.1 Before-and-after comparisons of retail prices, Getting started in R, Part 3.1 Before-and-after comparisons of retail prices, Getting started in Python.

time series data

A time series is a set of time-ordered observations of a variable taken at successive, in most cases regular, periods or points of time. Example: The population of a particular country in the years 199, 1991, 1992, … , 215 is time series data (DE). Introduced in Part 4.1 GDP and its components as a measure of material wellbeing, Part 4.1 GDP and its components as a measure of material wellbeing, Part 4.1 GDP and its components as a measure of material wellbeing, Part 4.1 GDP and its components as a measure of material wellbeing.

trend component

The long-run growth component of a macroeconomic time series. Commonly obtained using the Hodrick–Prescott (HP) filter (DE). Introduced in Part 1: Collecting and preparing the data, Part 2: Links between female labour supply and the macroeconomy, Part 1: Collecting and preparing the data.

variance

A measure of dispersion in a frequency distribution, equal to the mean of the squares of the deviations from the arithmetic mean of the distribution. The variance is used to indicate how ‘spread out’ the data is. A higher variance means that the data is more spread out. Example: The set of numbers 1, 1, 1 has zero variance (no variation), while the set of numbers 1, 1, 999 has a high variance of 221,334 (large spread) (DE). Introduced in Part 1.2 Variation in temperature over time, Part 1.2 Variation in temperature over time, Part 1.2 Variation in temperature over time, Part 1.2 Variation in temperature over time, Part 2.2 Describing the data, Part 2.1 Collecting data by playing a public goods game, 2. Collecting and analysing data from experiments Working in Google Sheets, Part 2.1 Collecting data by playing a public goods game.

weighted average

A type of average that assigns greater importance (weight) to some components than to others, in contrast with a simple average, which weights each component equally. Components with a larger weight can have a larger influence on the average (DE). Introduced in 10. Characteristics of banking systems around the world Working in Excel, Part 10.1 Summarizing the data, Part 10.1 Summarizing the data, Part 10.1 Summarizing the data.