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Relative change

Relative change is a fundamental concept in mathematics and quantitative reasoning that quantifies the proportional difference between two values, typically expressed as the ratio of the absolute change to the reference (initial) value, often in percentage form to facilitate comparison across different scales.^[1] It is defined formally as \frac{P_1 - P_0}{P_0}, where P_0 is the reference value and P_1 is the new value, providing a dimensionless measure that indicates growth or decline relative to the starting point.^[2] This approach contrasts with absolute change, which simply subtracts the reference value from the new value (P_1 - P_0) and retains the units of the original quantity, making it less suitable for comparing changes in datasets with varying magnitudes.^[3] The utility of relative change lies in its scale-invariance, allowing meaningful comparisons of variations in disparate contexts, such as population shifts or financial metrics.^[4] For instance, a relative change of 0.05 (or 5%) signifies a consistent proportional increase regardless of the baseline size, which is particularly valuable when the reference value is positive and non-zero to avoid undefined results.^[5] In practice, it is often multiplied by 100 to express percentage change, with positive values indicating increases and negative values decreases.^[6] Relative change finds broad applications across disciplines, including economics where it underpins growth rate calculations, such as the percentage increase in gross domestic product (GDP) per capita to assess economic expansion.^[7] In the sciences, it is integral to error analysis, manifesting as relative error to evaluate the precision of measurements by comparing the deviation from the true value to the true value itself, thus highlighting proportional accuracy in experimental data.^[8] Similarly, in health research, relative change measures are used to report effect sizes in inequalities studies, enabling comparisons of disparities like disease prevalence across populations of different sizes.^[9] These applications underscore its role in providing contextually relevant insights beyond raw differences.

Basic Concepts

Definition

Relative change is a mathematical measure that quantifies the variation between two values as a proportion of the original or baseline value, providing a normalized way to compare differences across scales.^[4] It is formally defined by the formula \Delta = \frac{a - b}{b}, where a represents the new or final value and b is the baseline value, with the condition that b \neq 0 to avoid division by zero.^[3] This expression yields a dimensionless quantity, as the units in the numerator and denominator cancel out, allowing relative change to be independent of the measurement scale and thus suitable for proportional comparisons in diverse fields such as economics and science.^[4] Unlike absolute change, which simply subtracts the baseline from the new value (a - b) and retains dimensional units, relative change emphasizes proportionality by dividing the difference by the baseline, enabling meaningful assessments of growth or decline relative to the starting point.^[3] The sign of \Delta follows a standard convention: a positive value indicates an increase (when a > b), while a negative value signifies a decrease (when a < b).^[10] Special considerations arise when the baseline b is zero, rendering the relative change undefined due to division by zero, or when b is negative, which can lead to counterintuitive signs—for instance, a positive increase from a negative baseline may yield a negative \Delta.^[11] In such cases, alternative approaches or contextual interpretations may be necessary to ensure meaningful analysis.^[12]

Domains of Application

Relative change is widely applied across multiple disciplines for facilitating comparative analysis of quantities that vary in scale or units. In economics, it is commonly used to compute growth rates, enabling the assessment of proportional expansions or contractions in indicators such as GDP over time.^[13] In physics, relative change serves as a fundamental tool in error analysis, where it quantifies uncertainties in measurements by expressing deviations as fractions of the measured value, aiding in the propagation of errors during calculations.^[14] Biology employs relative change to model population growth dynamics, particularly through relative growth rates that describe how population size increases proportionally to its current state in exponential models.^[15] In finance, it underpins the calculation of return on investment (ROI), which evaluates the efficiency of capital allocation by comparing gains or losses to the initial outlay.^[16] Statistics utilizes relative change in measures of variability, such as the coefficient of variation, to standardize dispersion across datasets with different means and units.^[17] The application of relative change traces its roots to 19th-century developments in mathematical economics, where proportional comparisons became essential for analyzing economic structures and transformations.^[18] A primary advantage of relative change in these domains is its scalability, which permits meaningful comparisons between entities with disparate absolute scales or units—for instance, evaluating percentage growth in GDP alongside population increases to gauge economic productivity per capita.^[19] This unitless nature enhances cross-disciplinary utility, as seen in econophysics applications where statistical physics methods adapt relative price changes for financial modeling.^[20] However, limitations arise in domain-specific contexts; notably, in finance, relative change exhibits sensitivity to the baseline value, where volatile or low initial figures can distort interpretations by magnifying minor absolute shifts into extreme percentages, potentially misleading risk assessments.^[21]

Percentage-Based Measures

Percentage Change

Percentage change represents the relative change scaled by a factor of 100 to express proportional variations in intuitive percentage terms. It derives directly from the basic relative change ratio, (a - b)/b, where b is the initial value and a is the final value, by multiplying this ratio by 100 to convert it to a percentage.^[4] The standard formula is:

\text{Percentage change} = 100 \times \frac{a - b}{b} \%

This formula is widely used in economics and statistics to quantify growth or decline relative to the baseline.^[22] For changes spanning multiple periods, such as successive rates of return or growth, the cumulative percentage change is computed by multiplying the individual growth factors—each $1 + r_i, where r_i is the decimal form of the percentage change in period i—and then subtracting 1 before scaling by 100. For two periods with rates r_1 and r_2, this yields:

\text{Cumulative percentage change} = 100 \times \left[ (1 + r_1)(1 + r_2) - 1 \right] \%

This multiplicative approach accounts for compounding effects, ensuring the overall change reflects the sequential application of rates rather than simple addition.^[23] In interpretation, a positive percentage change signifies growth or increase from the initial value, while a negative value indicates decline or decrease. However, percentage changes exhibit asymmetry: an increase followed by an equal percentage decrease (or vice versa) does not return the value to its original level. For instance, starting from 100, a 50% increase reaches 150, but a subsequent 50% decrease from 150 yields 75, which is only 75% of the original. This occurs because the base for the second change is the elevated value, amplifying the relative impact of decreases on smaller bases.^[24] Common pitfalls arise when the initial value b is zero, rendering the formula undefined due to division by zero, as no proportional change can be meaningfully computed from a null baseline.^[10] Similarly, when b is negative, the standard formula can produce counterintuitive or misleading results; in such cases, some applications resolve this by using the absolute value of b in the denominator to focus on magnitude, though this alters the directional interpretation.^[25]

Percent Error

Percent error quantifies the discrepancy between an observed or measured value and a true or accepted value, expressing this difference as a percentage of the true value to assess measurement accuracy in scientific contexts. It serves as a specialized application of relative change, focusing on errors rather than general value shifts.^[26] The standard formula for percent error is

\text{Percent error} = 100 \times \frac{|\text{observed} - \text{true}|}{\text{true}}\%

This expression incorporates the absolute value to emphasize the magnitude of the inaccuracy, disregarding whether the observed value over- or underestimates the true value.^[27] Percent error differs from relative error, which is the unscaled ratio \frac{|\text{observed} - \text{true}|}{\text{true}}, by multiplying the result by 100 to yield a percentage; this scaled form is prevalent in experimental sciences like physics and chemistry for reporting precision. The concept arose within 19th-century advancements in error analysis and metrology, where scientists such as Carl Friedrich Gauss and Pierre-Simon Laplace developed foundational theories for evaluating measurement reliability in physical and chemical experiments.^[28]^[29] For datasets involving multiple measurements, variants such as the mean absolute percentage error (MAPE) extend percent error by aggregating individual errors. MAPE is computed as the average of the absolute percentage errors across the dataset, providing a summary metric for overall accuracy in forecasting or repeated experiments without being skewed by directional biases.^[30]^[31]

Examples and Illustrations

Changes in Valuable Assets

In finance and economics, relative change provides a standardized way to assess variations in asset values, emphasizing the proportional shift relative to the initial baseline rather than the raw dollar amount. For instance, consider two stock investments: one where the price rises from $100 to $150, representing a relative change of \frac{150 - 100}{100} = 0.5 or 50%, and another from $1000 to $1050, yielding \frac{1050 - 1000}{1000} = 0.05 or 5%.^[32] This illustrates how relative change highlights the scale-independent nature of growth, making it easier to compare performance across assets of different magnitudes.^[33] The general formula for relative change in asset values is \frac{\text{new value} - \text{old value}}{\text{old value}}, which can be applied to appreciating or depreciating holdings such as stocks, bonds, or real estate.^[32] In real estate, for example, this measures depreciation when property values decline due to market conditions or obsolescence; a home valued at $300,000 dropping to $240,000 results in a relative change of \frac{240,000 - 300,000}{300,000} = -0.2 or -20%.^[34] Similarly, for inflation-adjusted values, the formula adjusts the old value for purchasing power erosion before computing the change, ensuring the metric reflects real economic impact rather than nominal shifts.^[35] Economists and investors prefer relative change over absolute differences for decision-making because it normalizes for initial investment size, enabling fair comparisons of returns across portfolios—for example, a 10% gain on a small stake may compound similarly to one on a larger one in percentage terms, guiding allocation strategies.^[36] Relative change, often expressed as percentage returns in finance, facilitates benchmarking against market indices or inflation.^[32] A stark real-world example occurred during the 2008 financial crisis, where the S&P 500 index declined approximately 57% from its October 2007 peak to its March 2009 trough, while U.S. home prices fell by over 20% on average nationwide from 2007 to 2011.^[37]^[34] These percentage drops masked vastly different absolute impacts: a 30% decline in a $100,000 property equated to a $30,000 loss, but the same relative change in a $1 million commercial asset resulted in a $300,000 hit, amplifying distress for high-value holders and underscoring why relative metrics reveal disproportionate economic ripple effects.^[38]

Percentages of Percentages

When relative changes are applied sequentially to a quantity, the resulting overall change is not simply the sum of the individual percentages, but rather the product of the corresponding multiplicative factors minus one. This sequential application, often termed percentages of percentages, arises in scenarios where each change is calculated relative to the updated value from the previous step. A classic illustration is a 10% increase followed by a 10% decrease: starting from an initial value of 100, the increase yields 110, and the subsequent 10% decrease (applied to 110) reduces it to 99, resulting in a net loss of 1%, computed as $100 \times (1.10 \times 0.90 - 1) = -1\%.^[39] The general formula for the total relative change after a sequence of n individual relative changes r_i (expressed as decimals) is \prod_{i=1}^{n} (1 + r_i) - 1, where the product reflects the compounding effect on successive bases.^[40] This multiplicative approach extends the single-period percentage change by chaining the factors, ensuring accuracy across multiple steps.^[41] A common misconception is that percentages applied sequentially can be added directly, leading to errors in estimating the net effect. For instance, two successive 50% increases on an initial value of 100 might be erroneously thought to double the value to 200 (50% + 50% = 100% total increase); however, the first increase reaches 150, and the second (50% of 150) adds 75, yielding 225—a 125% net increase, not 100%.^[42] This concept applies in financial contexts like annual compounding in savings accounts, where interest is added each period based on the growing balance, using the formula A = P \prod_{i=1}^{n} (1 + r_i) for the final amount A from principal P. Similarly, in sales discounts, sequential reductions compound multiplicatively: a $100 item with two 20% discounts first drops to $80, then to $64 (a 36% total reduction, not 40%), as each discount applies to the reduced price.^[43]^[44]

Logarithmic Measures

Logarithmic Change

Logarithmic change provides an alternative to linear measures of relative change by transforming multiplicative effects into additive differences on a logarithmic scale. Defined as the difference between the logarithms of two values, a and b, it is expressed as \log(a) - \log(b) = \log\left(\frac{a}{b}\right), where the logarithm can be taken with respect to any base greater than 1.^[45] This formulation directly captures the relative ratio \frac{a}{b}, making it particularly suitable for scenarios involving proportional or multiplicative adjustments. For a change from b to a = b(1 + \Delta), where \Delta is the relative change, the logarithmic change simplifies to \log(1 + \Delta).^[45] The use of logarithmic change is especially natural in contexts characterized by exponential growth processes, such as population dynamics in biology and economic modeling. In biology, exponential population growth models, where the population size N(t) follows N(t) = N_0 e^{rt} with growth rate r, yield a constant rate of change in \log N(t) = \log N_0 + rt, allowing straightforward analysis of intrinsic growth rates.^[46] Similarly, in economics, logarithmic change aligns with continuous compounding of interest, where the future value A of a principal P at continuous rate r over time t is A = P e^{rt}, and the logarithmic difference \log(A/P) = rt directly measures the compounded growth.^[47] For small relative changes, logarithmic change approximates the basic relative change itself. Specifically, \log(1 + \Delta) \approx \Delta when |\Delta| \ll 1, a first-order Taylor expansion that links the two measures and explains why small percentage changes are nearly equivalent to logarithmic differences.^[48] This approximation holds well for typical economic or biological increments, such as 5% growth where \log(1.05) \approx 0.0488, close to 0.05.^[48] The choice of logarithmic base affects the scale but not the underlying relative interpretation. The natural logarithm (base e) is preferred for continuous rates in growth models and compounding, as its derivative is unity, simplifying differential equations in biology and economics.^[47] In contrast, the common logarithm (base 10) is standard in engineering applications like decibel scales for sound intensity, where the decibel level is $10 \log_{10}(I/I_0), facilitating intuitive orders-of-magnitude comparisons.^[49]

Additivity Properties

One key advantage of logarithmic changes over arithmetic relative changes is their additivity property, which allows the total change across multiple intervals to be expressed as the simple sum of individual changes. Specifically, for a sequence of values where the overall transition is from initial value a to final value c through intermediate value b, the logarithmic change satisfies \log\left(\frac{a}{c}\right) = \log\left(\frac{a}{b}\right) + \log\left(\frac{b}{c}\right). This property holds because the total relative change is the product of the individual relative changes, and the logarithm converts multiplication into addition. The derivation stems directly from the fundamental product rule of logarithms, \log(xy) = \log x + \log y, applied to ratios. For successive relative changes with ratios r_1 = \frac{b}{a} and r_2 = \frac{c}{b}, the overall ratio is r = r_1 \cdot r_2 = \frac{c}{a}, so \log r = \log(r_1 r_2) = \log r_1 + \log r_2. This contrasts with arithmetic relative changes, where summing percentages (e.g., +10% followed by +20%) does not yield the correct total (which is actually +32%, not +30%). This additivity simplifies aggregation in time series analysis, such as computing cumulative growth or average rates. For example, average growth rates over multiple periods can be obtained as the arithmetic mean of the logarithmic changes, then exponentiated to recover the geometric mean, avoiding complex compounding calculations.^[48] In finance, this property makes logarithmic returns particularly useful for modeling multi-period performance, as the total log return is the sum of daily or periodic log returns. To illustrate, consider two consecutive periods with relative changes of 10% (ratio 1.1) and 20% (ratio 1.2). The individual log changes are \log(1.1) \approx 0.0953 and \log(1.2) \approx 0.1823, summing to approximately 0.2776. The total relative change is then e^{0.2776} \approx 1.32, matching the product $1.1 \times 1.2 = 1.32 and confirming the 32% overall increase.^[50] This additive structure facilitates statistical analysis, such as variance calculations over time, where non-additive arithmetic returns would require adjustments.

Uniqueness and Extensions

The logarithmic transformation is unique among continuous functions that convert multiplicative relative changes into additive ones, as it is the only solution to the functional equation f(xy) = f(x) + f(y) for x, y > 0, up to a constant multiple, under mild regularity conditions such as monotonicity or continuity.^[51]^[52] This uniqueness extends to more general transformations, such as the Box-Cox family, which generalizes the logarithm for power-law adjustments when data do not strictly require logarithmic scaling; for \lambda \neq 0, it applies (x^\lambda - 1)/\lambda, reducing to the natural logarithm as \lambda \to 0.^[53] In economic applications, logarithmic changes underpin elasticity measures, where price elasticity of demand is approximated as \frac{\% \Delta Q}{\% \Delta P} \approx \frac{\Delta \log Q}{\Delta \log P}, enabling direct estimation via log-log regression coefficients.^[54] Mathematically, logarithmic change connects to Lie group theory, where the multiplicative group of positive real numbers is isomorphic to the additive group of reals via the logarithm, with infinitesimal generators describing continuous relative variations as elements of the Lie algebra.^[55] However, logarithmic measures assume positive values, limiting applicability to datasets with zeros or negatives; extensions like \log(1 + x) approximate relative changes for small negative deviations while preserving additivity near unity.^[53]

Indicators and Advanced Uses

Indicators of Relative Change

Indicators of relative change quantify variations in quantities proportional to their initial or average values, providing scale-independent measures useful in diverse analytical contexts. These indicators often build on the core concept of relative change by incorporating time, weighting, or comparative frameworks to assess growth, inflation, or risk dynamics. Many such indicators express outcomes as percentages for intuitive interpretation, linking directly to percentage change calculations. A primary indicator is the relative growth rate, defined as the rate of change of a quantity relative to its current size, typically expressed as \frac{\Delta y / y}{\Delta t} for discrete intervals or \frac{1}{y} \frac{dy}{dt} in continuous form, where y is the quantity and t is time. This measure normalizes growth by the existing scale, allowing comparisons across entities of different sizes, such as populations or economic outputs. In biology, relative growth rate evaluates changes in organismal or population size relative to initial mass, facilitating analysis of relative abundance in ecological communities by highlighting proportional shifts in species biomass over time.^[15]^[56] Index numbers represent another key class of indicators that aggregate relative changes across multiple items, often using fixed weights to track overall shifts. The Laspeyres index, for instance, computes price changes by weighting current prices against a base-period basket, effectively incorporating relative price changes (price relatives) to produce a composite measure: L = \sum (p_t q_0) / \sum (p_0 q_0), where p_t and p_0 are prices at time t and base period 0, and q_0 is base-period quantity. In economics, this index underpins inflation measures like the Consumer Price Index (CPI), capturing relative changes in consumer goods prices to gauge purchasing power erosion over periods.^[57]^[58] The compound annual growth rate (CAGR) extends relative change over multiple periods by annualizing compounded growth, calculated as (a/b)^{1/n} - 1, where a is the ending value, b the beginning value, and n the number of periods. This formula derives from solving the compound interest equation a = b (1 + r)^n for the constant annual rate r, linking discrete relative changes into a smoothed, period-averaged indicator. CAGR is widely applied in economics to assess long-term relative growth in variables like GDP or investment returns, providing a consistent basis for comparing trajectories despite irregular interim fluctuations. Relative indicators like these contrast with absolute measures by emphasizing proportional impacts rather than raw differences, enhancing comparability in heterogeneous datasets. In epidemiology, relative risk (RR) exemplifies this by comparing outcome probabilities between exposed and unexposed groups, RR = (incidence in exposed) / (incidence in unexposed), which highlights multiplicative effects but can mislead without context from absolute risk—the actual event probability in each group. This distinction underscores the value of relative indicators in revealing scaled effects, such as treatment efficacy, while absolute metrics ground interpretations in baseline probabilities.^[59]^[60]

Relative Change in Statistics and Economics

In statistical modeling, relative change plays a key role in regression analyses, particularly through measures like relative risks and odds ratios, which quantify the proportional increase in event probability associated with exposures in cohort or case-control studies. For instance, in logistic regression, odds ratios approximate relative risks when outcomes are rare, enabling the assessment of multiplicative effects on probabilities.^[61]^[62] Relative change also aids in addressing heteroscedasticity, where error variances increase with the level of the response variable; by modeling errors as relative (multiplicative) rather than absolute, techniques like log-linear regression or weighted least squares with weights inversely proportional to squared fitted values stabilize variance and improve inference reliability.^[63]^[64] In economics, relative change underpins inequality metrics such as the Gini coefficient, which decomposes income disparities into pairwise absolute differences normalized by mean income, capturing how proportional shifts in incomes across the distribution affect overall inequality. The coefficient, ranging from 0 (perfect equality) to 1 (perfect inequality), highlights that small relative income changes at the tails can significantly alter the measure. Post-2020, analyses of pandemic impacts revealed mixed effects on Gini values; while some global estimates indicated a modest rise of about 0.7 points due to disproportionate income losses among lower quintiles, other studies found negligible shifts in within-country inequality thanks to fiscal interventions that mitigated relative declines.^[65]^[66]^[67]^[68] Modern extensions of relative change include its application in machine learning for feature normalization, where scaling variables to relative ranges (e.g., via min-max or z-score methods) ensures equitable contributions to model training, particularly in algorithms sensitive to magnitude like gradient descent-based optimizers. In climate economics, IPCC assessments employ relative change metrics to evaluate emission reductions, expressing progress as percentages below 2019 baselines or business-as-usual scenarios to benchmark mitigation pathways toward net-zero goals. These build on foundational indicators of relative change, such as growth rates, by integrating them into scenario modeling for policy evaluation.^[69]^[70]^[71]^[72] Addressing gaps in traditional approaches, the integration of big data has enabled real-time tracking of relative changes in algorithmic trading since the 2010s, where high-frequency algorithms process vast streams of market data to detect proportional price shifts and execute trades in milliseconds, enhancing liquidity but also amplifying volatility during rapid adjustments. This shift leverages distributed computing to monitor relative deviations from benchmarks instantaneously, far beyond static econometric models.^[73]^[74]^[75]

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This temporal additivity of logarithmic returns implies that if one-period returns are independent (as implied by the geometric random walk of (2.2)) the ...
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(PDF) Existence and uniqueness theorems for functional equations
Aug 6, 2025 · Abstract. In this paper we give simple extension and uniqueness theorems for restricted additive and logarithmic functional equations.Missing: transformation relative
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Uniqueness of the logarithm function - MathOverflow
Apr 27, 2010 · Saying that such an f is unique means that if f and g are both increasing functions that satisfy 1. and 2., then f=g.
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The Box-Cox Transformation: Review and Extensions - Project Euclid
The Box-Cox transformation is a power transformation used in linear regression models for non-negative responses, and is described in Section 2.
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[PDF] Models with Natural Logarithms - Colin Cameron
Economists are often interested in measuring proportionate changes ... Economics often uses logarithm to base e, the natural logarithm. ▻ where e ...
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logarithm in nLab
Dec 14, 2023 · A logarithm is a partially-defined smooth homomorphism from a multiplicative group of numbers to an additive group of numbers.
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Accounting for effects of growth rate when measuring ecological ...
Oct 17, 2024 · We present results from theoretical models and empirical data to illustrate how differences in growth rates among taxa influence four widely used metrics of ...Missing: inflation | Show results with:inflation
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Calculation : Handbook of Methods: U.S. Bureau of Labor Statistics
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Chapter 4 - Price index theory | Australian Bureau of Statistics
Typically the Laspeyres formula will produce a higher index number than the Paasche formula, with the Fisher Ideal and the Törnqvist index numbers falling ...
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Relative risk, relative and absolute risk reduction, number needed to ...
Relative risk reduction (RRR) tells you by how much the treatment reduced the risk of bad outcomes relative to the control group who did not have the treatment.
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Absolute and relative risk - Institute for Work & Health
Absolute risk is the number of people experiencing an event in relation to the population at large. Relative risk is a comparison between two groups of ...
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Relative Risk Regression
Such associations can instead be estimated and communicated as relative risks (sometimes called risk ratios or prevalence ratios) under certain circumstances.
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[PDF] Logistic Regression | UC Davis Health
Feb 10, 2021 · Relative risk (RR) = 1.056. Odds ratio (OR) = 1.067. RR and OR very close because the risk for both males and females is small. Sex. No Disease.
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Regression Calibration with Heteroscedastic Error Variance - PMC
An estimator is proposed to correct regression coefficients for covariate measurement error with heteroscedastic variance. Point and interval estimates are ...2. Methods · 3. Illustrative Examples · 4. Simulation Study
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Heteroscedasticity in Regression Analysis - Statistics By Jim
Heteroscedasticity refers to residuals for a regression model that do not have a constant variance. Learn how to identify and fix this problem.
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Measuring inequality: what is the Gini coefficient? - Our World in Data
Jun 30, 2023 · We see that modest relative changes in the Gini coefficient can be accompanied by much more substantial relative changes in the share of income ...
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5.12 Measuring economic inequality: The Gini coefficient
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 ...
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Publication: The Impact of COVID-19 on Global Inequality and Poverty
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Income inequality hardly changed during the COVID-19 pandemic
Feb 8, 2024 · Contrary to expectations, there was no widespread increase in either within-country or global income inequality during the pandemic.
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https://arxiv.org/pdf/2506.08274
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Importance of Feature Scaling — scikit-learn 1.7.2 documentation
Feature scaling through standardization, also called Z-score normalization, is an important preprocessing step for many machine learning algorithms.
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Summary for Policymakers
Projected rates of global emission reduction in mitigation scenarios are reported relative to modelled global emissions in 2019 rather than the global emissions ...
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[PDF] Climate Change 2022
h Rates of global emission reduction in mitigation pathways are reported on a pathway-by-pathway basis relative to harmonised modelled global emissions in 2019.
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3 ways big data is changing financial trading - Bloomberg.com
Jul 4, 2017 · The big data analytical revolution makes it possible to trade more accurately and informedly; impacting dramatically on how financial transactions are executed.
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Deep learning for algorithmic trading: A systematic review of ...
This systematic literature review explores recent advancements in the application of DL algorithms to algorithmic trading with a focus on optimizing financial ...
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[PDF] Staff Report on Algorithmic Trading in US Capital Markets - SEC.gov
Aug 5, 2020 · Trading and communication at national securities exchanges are now almost entirely automated. Order entry, message acknowledgement, matching ...Missing: big | Show results with:big