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INTRODUCTION TO ECONOMIC STATISTICS
FORMULAS
Lesson 2.
Lesson 4.
UP : Percentage of an average: UP= 0.4*Me q: nº poor people: Thales theorem Poverty rate : H=q/N Gini coefficient:
Lesson 6.
Lesson 7 Lesson 8.
Lesson 9.
Change at current prices: nominal change Change at constant prices : real change
Lessons 10 and 11
b1< 0.1: Additive decomposition model: Yt =Tt +Ct +et +ut =Et +et +ut b1>0.1: Multiplicative decomposition model: Yt =Tt ×Ict ×Iet +ut =Et ×Iet +ut Forecasting: Result * IVEx.
INTRODUCTION TO ECONOMIC STATISTICS
FORMULA’S INTERPRETATION
Lesson 2. Mean: Average that summarizes the distribution of a variable. If all the data had to be the same, it will be the mean. Median: Average that summarizes the distribution of a variable. If all data is put in increasing order, the Median would divide all the data in two equal subgroups. Mode: Average that summarizes the distribution of a variable. The most frequent data. Quartile: (Ej. 3 rd quartile) The behave of the 75% of the sample. Lesson 3. Variance: Variance is a measurement of the spread between numbers in a data set. The variance measures how far each number in the set is from the mean. Standard deviation: Measure of the dispersion of a set of data from its mean. Coefficient of variation (or Pearson’s): The higher the less representative the average is. Standardized variables: The larger, the bigger it is in relative terms. Lesson 4. Poverty line (UP): People that have a lower income than the UP is called poor. Gini coefficient: Measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents, and is the most commonly used measure of inequality. Lesson 5. Chi-squared coefficient: χ2 ≥0. 0=Independent. The bigger it is from 0, higher association. Pearson’s Contingency coefficient: 0 ≤ C<1, 0= independent. The closer to 1, the higher the association. Covariance: Linear relation: Sxy <0 Inverse, Sxy =0 not linear relation, Sxy >0 Direct. Linear correlation coefficien t : - 1 < rxy >1. 0=Independent +Direct relation, - Inverse relation. Lesson 6. Regression line : approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables. Coefficient of determination: Goodness of fit. The coefficient of determination R2 is defined as the proportion of the variance of Y explained by the regression line. Lesson 7,8 and 9. Contribution: The contribution (in%) of a component to the relative change of the synthetic index between t and t' is defined as the individual effect of this component. Time index numbers. Changes between two time periods. Spatial index numbers****. Differences between two geographical areas. Lessons 10 and 11. Trend : Tt, long term evolution of the series. Cyclical component : Ct, non-periodic fluctuations around the trend over 3, 5, 8 or even more years, caused by business cycle. Non-seasonal component : Et, combination of trend and cyclical component. Seasonal component, et (or eij), periodic fluctuations around the trend in the short term (always shorter than one year). Irregular or residual component: Ut, random, irregular and unpredictable fluctuations over time.
