4.6
Variância
Os desvios mostram quão dispersos os dados estão em relação à média. Um desvio positivo ocorre quando o valor dos dados excede a média, enquanto um de…
A variância é uma estatística que estima a variabilidade dos valores do conjunto de dados a partir da média. É numericamente igual ao quadrado do desvio padrão de um conjunto de dados.
A variância é uma ferramenta estatística valiosa usada na análise de variância, estimativa de risco ou volatilidade nos mercados financeiros.
A variância da amostra é denotada como o quadrado do desvio padrão da amostra s, enquanto a variância da população é expressa como o quadrado do sigma do desvio padrão da população.
Imagine se alguém estimasse a variação amostral do peso dos ursos polares em diferentes regiões do Ártico. Ao dividir a população em amostras aleatórias e calcular as variâncias amostrais, observa-se que os valores giram em torno do valor constante da variância da população. Assim, a variância amostral é um estimador imparcial da variância da população.
A principal desvantagem da variância é que suas unidades diferem muito das unidades do conjunto de dados. Por exemplo, as unidades de variância da precipitação em um ano serão milímetros ao quadrado, o que é inútil. Portanto, na maioria das análises, o desvio padrão é preferível à variância.
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Q1: What is the relationship between variance and standard deviation?
Variance is numerically equal to the square of the standard deviation. While standard deviation measures spread in the same units as the original data, variance's units differ from the dataset units. For example, rainfall variance is expressed in millimeters squared, making it less intuitive. This unit difference is why standard deviation is often preferred in practical analyses.
Q2: Why do deviations need to be squared when calculating variance?
Deviations show how spread out data are about the mean. When deviations are added together, the sum is always zero because positive and negative deviations cancel out. By squaring the deviations, all numbers become positive, so their sum is also positive. This allows variance to accurately represent data spread.
Q3: How does sample variance differ from population variance?
Sample variance is denoted as the square of sample standard deviation s, while population variance is the square of population standard deviation sigma. Sample variance is an unbiased estimator of population variance. When calculating sample variance, dividing by n – 1 instead of n provides a better estimate of the population variance based on theoretical mathematics.
Q4: What are the practical applications of variance in statistics?
Variance is a valuable statistical tool used in analysis of variance, estimation of risk, and measurement of volatility in financial markets. It quantifies how dataset values vary from the mean, providing insight into data dispersion. However, its unintuitive units often make standard deviation the preferred choice for most practical analyses and interpretations.
Q5: Why is dividing by n – 1 important when calculating sample variance?
Dividing by n – 1 instead of n when calculating sample variance produces a better estimate of the population variance. This adjustment accounts for the fact that sample data is used to estimate population parameters. The theoretical mathematics behind this calculation shows that n – 1 provides an unbiased estimator, making sample variance a reliable tool for inference.
Q6: What is the main disadvantage of using variance in data analysis?
The major disadvantage of variance is that its units vastly differ from the dataset units, making interpretation difficult. For instance, variance of rainfall measured in millimeters squared is unhelpful for practical understanding. This unit incompatibility is why standard deviation, which maintains the original data units, is preferred in most statistical analyses and real-world applications.
Q7: How can you verify that sample variance is an unbiased estimator?
When a population is divided into random samples and sample variances are calculated, the values center around the constant population variance value. This clustering demonstrates that sample variance is an impartial estimator of population variance. This property makes sample variance reliable for drawing inferences about population characteristics from sample data.