Bootstrap is a computer-intensive technique of re-sampling the data to obtain the sampling distribution of a statistic, treating the initial sample as the population from which samples are drawn repeatedly and randomly, with replacement.
Bootstrap methods are resampling techniques that provide measures of accuracy like confidence intervals and standard errors without relying on parametric assumptions. These techniques are essential in statistical inference when the underlying distribution is unknown or complex.
An overview of the Box-Cox Transformation, a statistical method for normalizing data and improving the validity of inferences in time-series and other types of data analysis.
The Box–Jenkins Approach is a systematic method for identifying, estimating, and checking autoregressive integrated moving average (ARIMA) models. It involves using sample autocorrelation and partial autocorrelation coefficients to specify a model, estimating parameters, and performing diagnostic checks.
An examination of the Breitung Test, used for testing unit roots or stationarity in panel data sets. The Breitung Test assumes a balanced panel with the null hypothesis of a unit root.
An in-depth look at the Breusch-Pagan Test, its importance in linear regression models, and how to perform and interpret it.
The British Household Panel Survey (BHPS) is a crucial source of longitudinal data about UK households, conducted by the Institute for Social and Economic Research (ISER) at the University of Essex.
A comprehensive exploration of the concept of 'Bundle of Goods,' its historical context, types, importance, applications, examples, and key considerations in economics.
Calculation is the mathematical process of determining values through arithmetic or algorithmic operations. It often involves percentages and other forms of quantitative analysis.
An exploration of calendar effects, which refer to time-period impacts on stock returns in finance and calendar variations in statistical time series.
Calibration is the process of identifying numerical values for parameters in an economic model by combining empirical data, informed judgement, simulation, and fine-tuning to match model predictions with empirical observations. This procedure is crucial in assessing business cycle models.
Capability Analysis is a statistical method used to determine if a process can consistently produce output within specified limits. It involves assessing process performance using statistical tools and techniques to ensure quality control.
The capital--output ratio is a critical metric in economics that measures the efficiency with which capital is used to generate output over a given period. This article delves into its historical context, types, key events, detailed explanations, and more.
A comprehensive exploration of categorical data, encompassing both nominal and ordinal types, including historical context, key concepts, applications, and more.
A comprehensive guide to understanding categorical variables, their types, usage in statistics, and significance in data analysis and modeling.
Causal inference is the process of determining cause-effect relationships between variables to account for variability, utilizing statistical methods and scientific principles.
An in-depth exploration of causality, focusing on Granger causality. We will cover historical context, types, key events, detailed explanations, mathematical models, examples, related terms, comparisons, interesting facts, and more.
Causation is a concept in statistics and science that explains the direct effect of one variable on another. This entry explores the definition, types, examples, historical context, and special considerations of causation.
Causation vs. Correlation: A comprehensive guide on distinguishing between related events and those where one event causes the other, including historical context, mathematical formulas, charts, examples, and FAQs.
A censored sample involves observations on the dependent variable that are missing or reported as a single value, often due to some known set of values of independent variables. This situation commonly arises in scenarios such as sold-out concert ticket sales, where the true demand is not observed. The Tobit model is frequently employed to address such challenges.
An in-depth look at the practice of conducting censuses, historical context, key methodologies, significance, and applications in various fields.
A systematic survey conducted by an official body to collect detailed information about productive enterprises, including the nature of their products, the types and quantities of inputs used, and the number of employees. These results help draw up input-output tables for the economy.
The Central Limit Theorem (CLT) states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the data's original distribution.
A deep dive into the Central Limit Theorems, which form the cornerstone of statistical theory by explaining the limiting distribution of sample averages.
Central Moment refers to statistical moments calculated about the mean of a distribution, essential for understanding the distribution's shape and characteristics.
The UK government department responsible up to 1996 for publishing major UK statistical sources, including the National Income Accounts, Economic Trends, and the Balance of Payments Accounts.
A detailed look into Chain Price Index, its historical context, methodology, importance, and applications.
A comprehensive overview of charts, their types, uses, and historical context.
Explore the Chi-Square Distribution, a fundamental statistical tool used to analyze the goodness of fit and independence in categorical data.
An in-depth look at the Chi-Square Statistic, its applications, calculations, and significance in evaluating categorical data, such as goodness-of-fit tests.
The Chow Test is a statistical test used to determine whether the coefficients in two linear regressions on two different data samples are equal. This test is particularly important in assessing the stability of coefficients over time in time series analysis.
Comprehensive guide on Cluster Analysis, a method used to group objects with similar characteristics into clusters, explore data, and discover structures without providing an explanation for those structures.
An in-depth exploration of Cluster Sampling, a statistical method for selecting random samples from a divided population.
The Cochrane-Orcutt procedure is a two-step estimation technique designed to handle first-order serial correlation in the errors of a linear regression model. This method uses the ordinary least squares residuals to estimate the first-order autocorrelation coefficient and then rescale the variables to eliminate serial correlation in the errors.
The coefficient of determination, denoted by R², quantifies the proportion of variance in the dependent variable that is predictable from the independent variables in a regression model.
A statistical measure representing the proportion of the variance for a dependent variable that is explained by an independent variable(s) in a regression model. Indicates the proportion of the variance in the dependent variable predictable from the independent variable(s).
A comprehensive look at the Coefficient of Variation (CV), a statistic used to compare the degree of variation relative to the mean of different data sets.
A 'cohort' refers to a group of people banded together or treated as a group, often used in statistics, social sciences, and public health to analyze trends over time.
A detailed exploration of cohort studies, their historical context, types, key events, explanations, formulas, diagrams, importance, examples, related terms, and more.
Cointegration refers to a statistical property indicating a stable, long-run relationship between two or more time series variables, despite short-term deviations.
A comprehensive overview of cointegration, its historical context, types, key events, mathematical models, and importance in various fields such as economics and finance.
Understanding Collinearity and Its Implications in Statistical Models
An in-depth look at combinations, a mathematical concept used to determine how items can be selected where order does not matter.
A comprehensive overview of comparability, focusing on its application in economics, accounting, and statistics, along with historical context, key events, models, examples, and related concepts.
A detailed examination of the similarities and differences between entities through the method of comparing two or more datasets to identify trends or differences.
Comparative Statics involves the analysis of how equilibrium positions in economic models change with variations in exogenous parameters, helping economists understand the effects of policy changes, shifts in preferences, and external shocks.
Comparison provides an evaluative framework to analyze differences and similarities between items, concepts, or entities in various contexts.
Understanding complementary events, their definition, examples, and significance in probability theory.
A comprehensive index that blends multiple economic variables to represent the overall economic condition, often used in statistical analysis and economic forecasting.
An in-depth exploration of the condition number, a measure of how the output value of a function changes for a small change in the input argument. Understanding its importance in numerical analysis and various applications.
An in-depth exploration of the Conditional Cost of Living Index, its historical context, significance, calculation methods, and applications in economics and policy making.
Explore the concept of conditional distribution, its importance, applications, key events, and examples in the field of statistics and probability.
Understanding Conditional Distribution in Probability and Statistics
A detailed exploration of Conditional Entropy (H(Y|X)), its mathematical formulation, importance in information theory, applications in various fields, and related terms.
A Confidence Interval (CI) is a range of values derived from sample data that is likely to contain a population parameter with a certain level of confidence.
Confidence Interval is an estimation rule that, with a given probability, provides intervals containing the true value of an unknown parameter when applied to repeated samples.
A comprehensive guide to understanding the confidence level, its historical context, types, key events, mathematical models, and practical applications in statistics.
A comprehensive description of the concept of confounding variables, their implications in research, examples, identification methods, and ways to control for them.
An in-depth examination of consistent estimators, their mathematical properties, types, applications, and significance in statistical inference.
The Consumer Expenditure Survey provides detailed information on the expenditures, incomes, and characteristics of U.S. consumers. It includes two key components: the Quarterly Interview Survey and the Diary Survey, collected by the Bureau of Labor Statistics through the U.S. Census Bureau.
The Consumer Price Index (CPI) is a critical economic indicator that measures the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
An in-depth exploration of the Consumer Price Index (CPI), a crucial economic indicator used to measure inflation and inform economic policy decisions.
An in-depth look at the Consumer Price Index (CPI), its historical context, importance, applications, and more.
A detailed exploration of contemporaneous correlation, which measures the correlation between the realizations of two time series variables within the same period.
An in-depth look at continuous distributions, key concepts, applications, and examples.
A comprehensive guide to understanding continuous random variables, their historical context, types, key events, mathematical models, applicability, examples, and more.
A comprehensive examination of continuous time processes, including historical context, key events, detailed explanations, mathematical models, examples, and applications.
A detailed exploration of continuous variables in mathematics and statistics, including their historical context, types, significance, and real-world applications.
A comprehensive guide on control charts, their historical context, types, key events, mathematical formulas, charts, and their importance in quality control and process management.
An in-depth examination of the Control Event Rate (CER) - its definition, significance in clinical trials, calculation methods, applications, and related terms.
An in-depth exploration of control groups, their importance, and application in various experiments, including key events, examples, and related terms.
Control limits are statistical boundaries used in process control to determine whether a process variable is operating within an acceptable range.
A comprehensive guide on Convergence in Distribution in probability theory, covering historical context, detailed explanations, mathematical models, importance, applicability, examples, and more.
An in-depth exploration of Convergence in Mean Squares, a concept where a sequence of random variables converges to another random variable in terms of the expected squared distance.
An in-depth examination of convergence in probability, a fundamental concept in probability theory where a sequence of random variables converges to a particular random variable.
Core Inflation measures the rate of inflation excluding volatile items like food and energy, providing a clearer picture of long-term inflation trends.
A comprehensive guide on the correlation coefficient (r), its historical context, types, key events, detailed explanations, mathematical formulas, importance, and applicability.
A comprehensive guide on correlation coefficient - its definition, types, calculations, importance, and applications in various fields.
A comprehensive overview of the correlation coefficient, its calculation, interpretation, significance in various fields, and associated concepts.
A comprehensive guide to understanding the difference between correlation and causation, including historical context, key events, detailed explanations, examples, and more.
A comprehensive examination of cost curves, illustrating the relationship between costs and production quantity. Includes short-run and long-run perspectives, different types of cost curves, and their practical implications in economics.
A comprehensive guide to Cost Prediction, the estimation of future cost levels based on historical cost behaviour using statistical techniques such as linear regression.
An in-depth exploration of counterfactual analysis in econometrics, including its historical context, methodologies, applications in macroeconomics and microeconomics, key events, and more.
Covariance measures the degree of linear relationship between two random variables. This article explores its historical context, types, formulas, importance, applications, and more.
An in-depth examination of the covariance matrix, a critical tool in statistics and data analysis that reveals the covariance between pairs of variables.
Understanding the covariance matrix, its significance in multivariate analysis, and its applications in fields like finance, machine learning, and economics.
A comprehensive overview of covariance stationary processes in time series analysis, including definitions, historical context, types, key events, mathematical models, charts, importance, applicability, examples, related terms, comparisons, interesting facts, famous quotes, and more.
An in-depth exploration of Coverage Error, its implications, causes, and methods for mitigation.
The Consumer Price Index (CPI) is a measure that examines the weighted average of prices of a basket of consumer goods and services, such as transportation, food, and medical care. This entry focuses on its relation to out-of-pocket expenses.
The Consumer Price Index (CPI) measures the average change in prices over time that consumers pay for a basket of goods and services.
CPIH is an inflation measure that includes owner-occupiers' housing costs, providing a comprehensive view of consumer price changes in the economy.
Critical Value: The threshold at which the test statistic is compared to decide on the rejection of the null hypothesis in statistical hypothesis testing.
An in-depth examination of Critical Value, its significance in hypothesis testing, mathematical models, examples, and related terms in statistics.
Cross-correlation measures the similarity between two different time series as a function of the lag of one relative to the other. It is used to compare different time series and has applications in various fields such as signal processing, finance, and economics.
A comprehensive explanation of Cross-Price Elasticity, including its historical context, types, key events, mathematical formulas, applicability, and real-world examples.
Comprehensive exploration of Cross-Section Data, including historical context, types, key events, mathematical models, importance, applicability, examples, and FAQs.
Cross-sectional data involves observations collected at a single point in time, commonly used in statistics and economics for analysis and comparison.
Cross-Validation is a critical resampling procedure utilized in evaluating machine learning models to ensure accuracy, reliability, and performance.
Explore the definition, historical context, types, key properties, importance, applications, and more about the Cumulative Distribution Function (CDF) in probability and statistics.
A Cumulative Distribution Function (CDF) describes the probability that a random variable will take a value less than or equal to a specified value. Widely used in statistics and probability theory to analyze data distributions.
