Standard error of regression formula - Исправление ошибок и поиск оптимальных решений проблем

Когда мы подгоняем регрессионную модель к набору данных, нас часто интересует, насколько хорошо регрессионная модель «подходит» к набору данных. Две метрики, обычно используемые для измерения согласия, включают R -квадрат (R2) и стандартную ошибку регрессии , часто обозначаемую как S.

В этом руководстве объясняется, как интерпретировать стандартную ошибку регрессии (S), а также почему она может предоставить более полезную информацию, чем R 2 .

Стандартная ошибка по сравнению с R-квадратом в регрессии

Предположим, у нас есть простой набор данных, который показывает, сколько часов 12 студентов занимались в день в течение месяца, предшествующего важному экзамену, а также их баллы за экзамен:

Если мы подгоним простую модель линейной регрессии к этому набору данных в Excel, мы получим следующий результат:

R-квадрат — это доля дисперсии переменной отклика, которая может быть объяснена предикторной переменной. При этом 65,76% дисперсии экзаменационных баллов можно объяснить количеством часов, потраченных на учебу.

Стандартная ошибка регрессии — это среднее расстояние, на которое наблюдаемые значения отклоняются от линии регрессии. В этом случае наблюдаемые значения отклоняются от линии регрессии в среднем на 4,89 единицы.

Если мы нанесем фактические точки данных вместе с линией регрессии, мы сможем увидеть это более четко:

Обратите внимание, что некоторые наблюдения попадают очень близко к линии регрессии, в то время как другие не так близки. Но в среднем наблюдаемые значения отклоняются от линии регрессии на 4,19 единицы .

Стандартная ошибка регрессии особенно полезна, поскольку ее можно использовать для оценки точности прогнозов. Примерно 95% наблюдений должны находиться в пределах +/- двух стандартных ошибок регрессии, что является быстрым приближением к 95% интервалу прогнозирования.

Если мы заинтересованы в прогнозировании с использованием модели регрессии, стандартная ошибка регрессии может быть более полезной метрикой, чем R-квадрат, потому что она дает нам представление о том, насколько точными будут наши прогнозы в единицах измерения.

Чтобы проиллюстрировать, почему стандартная ошибка регрессии может быть более полезной метрикой для оценки «соответствия» модели, рассмотрим другой пример набора данных, который показывает, сколько часов 12 студентов занимались в день в течение месяца, предшествующего важному экзамену, а также их экзаменационная оценка:

Обратите внимание, что это точно такой же набор данных, как и раньше, за исключением того, что все значения s сокращены вдвое.Таким образом, студенты из этого набора данных учились ровно в два раза дольше, чем студенты из предыдущего набора данных, и получили ровно половину экзаменационного балла.

Обратите внимание, что R-квадрат 65,76% точно такой же, как и в предыдущем примере.

Однако стандартная ошибка регрессии составляет 2,095 , что ровно вдвое меньше стандартной ошибки регрессии в предыдущем примере.

Если мы нанесем фактические точки данных вместе с линией регрессии, мы сможем увидеть это более четко:

Обратите внимание на то, что наблюдения располагаются гораздо плотнее вокруг линии регрессии. В среднем наблюдаемые значения отклоняются от линии регрессии на 2,095 единицы .

Таким образом, несмотря на то, что обе модели регрессии имеют R-квадрат 65,76% , мы знаем, что вторая модель будет давать более точные прогнозы, поскольку она имеет более низкую стандартную ошибку регрессии.

Преимущества использования стандартной ошибки

Стандартную ошибку регрессии (S) часто бывает полезнее знать, чем R-квадрат модели, потому что она дает нам фактические единицы измерения. Если мы заинтересованы в использовании регрессионной модели для получения прогнозов, S может очень легко сказать нам, достаточно ли точна модель для прогнозирования.

Например, предположим, что мы хотим создать 95-процентный интервал прогнозирования, в котором мы можем прогнозировать результаты экзаменов с точностью до 6 баллов от фактической оценки.

Наша первая модель имеет R-квадрат 65,76%, но это ничего не говорит нам о том, насколько точным будет наш интервал прогнозирования. К счастью, мы также знаем, что у первой модели показатель S равен 4,19. Это означает, что 95-процентный интервал прогнозирования будет иметь ширину примерно 2*4,19 = +/- 8,38 единиц, что слишком велико для нашего интервала прогнозирования.

Наша вторая модель также имеет R-квадрат 65,76%, но опять же это ничего не говорит нам о том, насколько точным будет наш интервал прогнозирования. Однако мы знаем, что вторая модель имеет S 2,095. Это означает, что 95-процентный интервал прогнозирования будет иметь ширину примерно 2*2,095= +/- 4,19 единиц, что меньше 6 и, следовательно, будет достаточно точным для использования для создания интервалов прогнозирования.

Дальнейшее чтение

Введение в простую линейную регрессию
Что такое хорошее значение R-квадрата?

Источник

When we fit a regression model to a dataset, we’re often interested in how well the regression model “fits” the dataset. Two metrics commonly used to measure goodness-of-fit include R-squared (R²) and the standard error of the regression, often denoted S.

This tutorial explains how to interpret the standard error of the regression (S) as well as why it may provide more useful information than R².

Standard Error vs. R-Squared in Regression

Suppose we have a simple dataset that shows how many hours 12 students studied per day for a month leading up to an important exam along with their exam score:

If we fit a simple linear regression model to this dataset in Excel, we receive the following output:

R-squared is the proportion of the variance in the response variable that can be explained by the predictor variable. In this case, 65.76% of the variance in the exam scores can be explained by the number of hours spent studying.

The standard error of the regression is the average distance that the observed values fall from the regression line. In this case, the observed values fall an average of 4.89 units from the regression line.

If we plot the actual data points along with the regression line, we can see this more clearly:

Notice that some observations fall very close to the regression line, while others are not quite as close. But on average, the observed values fall 4.19 units from the regression line.

The standard error of the regression is particularly useful because it can be used to assess the precision of predictions. Roughly 95% of the observation should fall within +/- two standard error of the regression, which is a quick approximation of a 95% prediction interval.

If we’re interested in making predictions using the regression model, the standard error of the regression can be a more useful metric to know than R-squared because it gives us an idea of how precise our predictions will be in terms of units.

To illustrate why the standard error of the regression can be a more useful metric in assessing the “fit” of a model, consider another example dataset that shows how many hours 12 students studied per day for a month leading up to an important exam along with their exam score:

Notice that this is the exact same dataset as before, except all of the values are cut in half. Thus, the students in this dataset studied for exactly half as long as the students in the previous dataset and received exactly half the exam score.

If we fit a simple linear regression model to this dataset in Excel, we receive the following output:

Notice that the R-squared of 65.76% is the exact same as the previous example.

However, the standard error of the regression is 2.095, which is exactly half as large as the standard error of the regression in the previous example.

If we plot the actual data points along with the regression line, we can see this more clearly:

Notice how the observations are packed much more closely around the regression line. On average, the observed values fall 2.095 units from the regression line.

So, even though both regression models have an R-squared of 65.76%, we know that the second model would provide more precise predictions because it has a lower standard error of the regression.

The Advantages of Using the Standard Error

The standard error of the regression (S) is often more useful to know than the R-squared of the model because it provides us with actual units. If we’re interested in using a regression model to produce predictions, S can tell us very easily if a model is precise enough to use for prediction.

For example, suppose we want to produce a 95% prediction interval in which we can predict exam scores within 6 points of the actual score.

Our first model has an R-squared of 65.76%, but this doesn’t tell us anything about how precise our prediction interval will be. Luckily we also know that the first model has an S of 4.19. This means a 95% prediction interval would be roughly 2*4.19 = +/- 8.38 units wide, which is too wide for our prediction interval.

Our second model also has an R-squared of 65.76%, but again this doesn’t tell us anything about how precise our prediction interval will be. However, we know that the second model has an S of 2.095. This means a 95% prediction interval would be roughly 2*2.095= +/- 4.19 units wide, which is less than 6 and thus sufficiently precise to use for producing prediction intervals.

Further Reading

Introduction to Simple Linear Regression
What is a Good R-squared Value?

Источник

From Wikipedia, the free encyclopedia

For a value that is sampled with an unbiased normally distributed error, the above depicts the proportion of samples that would fall between 0, 1, 2, and 3 standard deviations above and below the actual value.

The standard error (SE)^[1] of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution^[2] or an estimate of that standard deviation. If the statistic is the sample mean, it is called the standard error of the mean (SEM).^[1]

The sampling distribution of a mean is generated by repeated sampling from the same population and recording of the sample means obtained. This forms a distribution of different means, and this distribution has its own mean and variance. Mathematically, the variance of the sampling mean distribution obtained is equal to the variance of the population divided by the sample size. This is because as the sample size increases, sample means cluster more closely around the population mean.

Therefore, the relationship between the standard error of the mean and the standard deviation is such that, for a given sample size, the standard error of the mean equals the standard deviation divided by the square root of the sample size.^[1] In other words, the standard error of the mean is a measure of the dispersion of sample means around the population mean.

In regression analysis, the term «standard error» refers either to the square root of the reduced chi-squared statistic or the standard error for a particular regression coefficient (as used in, say, confidence intervals).

Standard error of the sample mean[edit]

Exact value[edit]

Suppose a statistically independent sample of observations ${displaystyle x_{1},x_{2},ldots ,x_{n}}$ is taken from a statistical population with a standard deviation of sigma . The mean value calculated from the sample, , will have an associated standard error on the mean, ${displaystyle {sigma }_{bar {x}}}$ , given by:^[1]

${displaystyle {sigma }_{bar {x}} ={frac {sigma }{sqrt {n}}}}$

Practically this tells us that when trying to estimate the value of a population mean, due to the factor , reducing the error on the estimate by a factor of two requires acquiring four times as many observations in the sample; reducing it by a factor of ten requires a hundred times as many observations.

Estimate[edit]

The standard deviation sigma of the population being sampled is seldom known. Therefore, the standard error of the mean is usually estimated by replacing sigma with the sample standard deviation $sigma _{x}$ instead:

${displaystyle {sigma }_{bar {x}} approx {frac {sigma _{x}}{sqrt {n}}}}$

As this is only an estimator for the true «standard error», it is common to see other notations here such as:

${displaystyle {widehat {sigma }}_{bar {x}}approx {frac {sigma _{x}}{sqrt {n}}}}$

or alternately ${displaystyle {s}_{bar {x}} approx {frac {s}{sqrt {n}}}}$

A common source of confusion occurs when failing to distinguish clearly between:

Accuracy of the estimator[edit]

When the sample size is small, using the standard deviation of the sample instead of the true standard deviation of the population will tend to systematically underestimate the population standard deviation, and therefore also the standard error. With n = 2, the underestimate is about 25%, but for n = 6, the underestimate is only 5%. Gurland and Tripathi (1971) provide a correction and equation for this effect.^[3] Sokal and Rohlf (1981) give an equation of the correction factor for small samples of n < 20.^[4] See unbiased estimation of standard deviation for further discussion.

Derivation[edit]

The standard error on the mean may be derived from the variance of a sum of independent random variables,^[5] given the definition of variance and some simple properties thereof. If ${displaystyle x_{1},x_{2},ldots ,x_{n}}$ are independent samples from a population with mean and standard deviation sigma , then we can define the total

${displaystyle T=(x_{1}+x_{2}+cdots +x_{n})}$

which due to the Bienaymé formula, will have variance

${displaystyle operatorname {Var} (T)approx {big (}operatorname {Var} (x_{1})+operatorname {Var} (x_{2})+cdots +operatorname {Var} (x_{n}){big )}=nsigma ^{2}.}$

where we’ve approximated the standard deviations, i.e., the uncertainties, of the measurements themselves with the best value for the standard deviation of the population. The mean of these measurements is simply given by

The variance of the mean is then

${displaystyle operatorname {Var} ({bar {x}})=operatorname {Var} left({frac {T}{n}}right)={frac {1}{n^{2}}}operatorname {Var} (T)={frac {1}{n^{2}}}nsigma ^{2}={frac {sigma ^{2}}{n}}.}$

The standard error is, by definition, the standard deviation of which is simply the square root of the variance:

${displaystyle sigma _{bar {x}}={sqrt {frac {sigma ^{2}}{n}}}={frac {sigma }{sqrt {n}}}}$

For correlated random variables the sample variance needs to be computed according to the Markov chain central limit theorem.

Independent and identically distributed random variables with random sample size[edit]

There are cases when a sample is taken without knowing, in advance, how many observations will be acceptable according to some criterion. In such cases, the sample size is a random variable whose variation adds to the variation of such that,

${displaystyle operatorname {Var} (T)=operatorname {E} (N)operatorname {Var} (X)+operatorname {Var} (N){big (}operatorname {E} (X){big )}^{2}}$

^[6]

If has a Poisson distribution, then with estimator . Hence the estimator of becomes ${displaystyle nS_{X}^{2}+n{bar {X}}^{2}}$ , leading the following formula for standard error:

${displaystyle operatorname {Standard~Error} ({bar {X}})={sqrt {frac {S_{X}^{2}+{bar {X}}^{2}}{n}}}}$

(since the standard deviation is the square root of the variance)

Student approximation when σ value is unknown[edit]

In many practical applications, the true value of σ is unknown. As a result, we need to use a distribution that takes into account that spread of possible σ’s.
When the true underlying distribution is known to be Gaussian, although with unknown σ, then the resulting estimated distribution follows the Student t-distribution. The standard error is the standard deviation of the Student t-distribution. T-distributions are slightly different from Gaussian, and vary depending on the size of the sample. Small samples are somewhat more likely to underestimate the population standard deviation and have a mean that differs from the true population mean, and the Student t-distribution accounts for the probability of these events with somewhat heavier tails compared to a Gaussian. To estimate the standard error of a Student t-distribution it is sufficient to use the sample standard deviation «s» instead of σ, and we could use this value to calculate confidence intervals.

Note: The Student’s probability distribution is approximated well by the Gaussian distribution when the sample size is over 100. For such samples one can use the latter distribution, which is much simpler.

Assumptions and usage[edit]

An example of how is used is to make confidence intervals of the unknown population mean. If the sampling distribution is normally distributed, the sample mean, the standard error, and the quantiles of the normal distribution can be used to calculate confidence intervals for the true population mean. The following expressions can be used to calculate the upper and lower 95% confidence limits, where is equal to the sample mean, is equal to the standard error for the sample mean, and 1.96 is the approximate value of the 97.5 percentile point of the normal distribution:

Upper 95% limit

{displaystyle ={bar {x}}+(operatorname {SE} times 1.96),}

and

Lower 95% limit

In particular, the standard error of a sample statistic (such as sample mean) is the actual or estimated standard deviation of the sample mean in the process by which it was generated. In other words, it is the actual or estimated standard deviation of the sampling distribution of the sample statistic. The notation for standard error can be any one of SE, SEM (for standard error of measurement or mean), or S_E.

Standard errors provide simple measures of uncertainty in a value and are often used because:

in many cases, if the standard error of several individual quantities is known then the standard error of some function of the quantities can be easily calculated;
when the probability distribution of the value is known, it can be used to calculate an exact confidence interval;
when the probability distribution is unknown, Chebyshev’s or the Vysochanskiï–Petunin inequalities can be used to calculate a conservative confidence interval; and
as the sample size tends to infinity the central limit theorem guarantees that the sampling distribution of the mean is asymptotically normal.

Standard error of mean versus standard deviation[edit]

In scientific and technical literature, experimental data are often summarized either using the mean and standard deviation of the sample data or the mean with the standard error. This often leads to confusion about their interchangeability. However, the mean and standard deviation are descriptive statistics, whereas the standard error of the mean is descriptive of the random sampling process. The standard deviation of the sample data is a description of the variation in measurements, while the standard error of the mean is a probabilistic statement about how the sample size will provide a better bound on estimates of the population mean, in light of the central limit theorem.^[7]

Put simply, the standard error of the sample mean is an estimate of how far the sample mean is likely to be from the population mean, whereas the standard deviation of the sample is the degree to which individuals within the sample differ from the sample mean.^[8] If the population standard deviation is finite, the standard error of the mean of the sample will tend to zero with increasing sample size, because the estimate of the population mean will improve, while the standard deviation of the sample will tend to approximate the population standard deviation as the sample size increases.

Extensions[edit]

Finite population correction (FPC)[edit]

The formula given above for the standard error assumes that the population is infinite. Nonetheless, it is often used for finite populations when people are interested in measuring the process that created the existing finite population (this is called an analytic study). Though the above formula is not exactly correct when the population is finite, the difference between the finite- and infinite-population versions will be small when sampling fraction is small (e.g. a small proportion of a finite population is studied). In this case people often do not correct for the finite population, essentially treating it as an «approximately infinite» population.

If one is interested in measuring an existing finite population that will not change over time, then it is necessary to adjust for the population size (called an enumerative study). When the sampling fraction (often termed f) is large (approximately at 5% or more) in an enumerative study, the estimate of the standard error must be corrected by multiplying by a »finite population correction» (a.k.a.: FPC):^[9]
^[10]

${displaystyle operatorname {FPC} ={sqrt {frac {N-n}{N-1}}}}$

which, for large N:

${displaystyle operatorname {FPC} approx {sqrt {1-{frac {n}{N}}}}={sqrt {1-f}}}$

to account for the added precision gained by sampling close to a larger percentage of the population. The effect of the FPC is that the error becomes zero when the sample size n is equal to the population size N.

This happens in survey methodology when sampling without replacement. If sampling with replacement, then FPC does not come into play.

Correction for correlation in the sample[edit]

Expected error in the mean of A for a sample of n data points with sample bias coefficient ρ. The unbiased standard error plots as the ρ = 0 diagonal line with log-log slope −½.

If values of the measured quantity A are not statistically independent but have been obtained from known locations in parameter space x, an unbiased estimate of the true standard error of the mean (actually a correction on the standard deviation part) may be obtained by multiplying the calculated standard error of the sample by the factor f:

$f={sqrt {frac {1+rho }{1-rho }}},$

where the sample bias coefficient ρ is the widely used Prais–Winsten estimate of the autocorrelation-coefficient (a quantity between −1 and +1) for all sample point pairs. This approximate formula is for moderate to large sample sizes; the reference gives the exact formulas for any sample size, and can be applied to heavily autocorrelated time series like Wall Street stock quotes. Moreover, this formula works for positive and negative ρ alike.^[11] See also unbiased estimation of standard deviation for more discussion.

References[edit]

^ ^a ^b ^c ^d Altman, Douglas G; Bland, J Martin (2005-10-15). «Standard deviations and standard errors». BMJ: British Medical Journal. 331 (7521): 903. doi:10.1136/bmj.331.7521.903. ISSN 0959-8138. PMC 1255808. PMID 16223828.
^ Everitt, B. S. (2003). The Cambridge Dictionary of Statistics. CUP. ISBN 978-0-521-81099-9.
^ Gurland, J; Tripathi RC (1971). «A simple approximation for unbiased estimation of the standard deviation». American Statistician. 25 (4): 30–32. doi:10.2307/2682923. JSTOR 2682923.
^ Sokal; Rohlf (1981). Biometry: Principles and Practice of Statistics in Biological Research (2nd ed.). p. 53. ISBN 978-0-7167-1254-1.
^ Hutchinson, T. P. (1993). Essentials of Statistical Methods, in 41 pages. Adelaide: Rumsby. ISBN 978-0-646-12621-0.
^ Cornell, J R, and Benjamin, C A, Probability, Statistics, and Decisions for Civil Engineers, McGraw-Hill, NY, 1970, ISBN 0486796094, pp. 178–9.
^ Barde, M. (2012). «What to use to express the variability of data: Standard deviation or standard error of mean?». Perspect. Clin. Res. 3 (3): 113–116. doi:10.4103/2229-3485.100662. PMC 3487226. PMID 23125963.
^ Wassertheil-Smoller, Sylvia (1995). Biostatistics and Epidemiology : A Primer for Health Professionals (Second ed.). New York: Springer. pp. 40–43. ISBN 0-387-94388-9.
^ Isserlis, L. (1918). «On the value of a mean as calculated from a sample». Journal of the Royal Statistical Society. 81 (1): 75–81. doi:10.2307/2340569. JSTOR 2340569. (Equation 1)
^ Bondy, Warren; Zlot, William (1976). «The Standard Error of the Mean and the Difference Between Means for Finite Populations». The American Statistician. 30 (2): 96–97. doi:10.1080/00031305.1976.10479149. JSTOR 2683803. (Equation 2)
^ Bence, James R. (1995). «Analysis of Short Time Series: Correcting for Autocorrelation». Ecology. 76 (2): 628–639. doi:10.2307/1941218. JSTOR 1941218.

Источник

Errors are of various types and impact the research process in different ways. Here’s a deep exploration of the standard error, the types, implications, formula, and how to interpret the values

What is a Standard Error?

The standard error is a statistical measure that accounts for the extent to which a sample distribution represents the population of interest using standard deviation. You can also think of it as the standard deviation of your sample in relation to your target population.

The standard error allows you to compare two similar measures in your sample data and population. For example, the standard error of the mean measures how far the sample mean (average) of the data is likely to be from the true population mean—the same applies to other types of standard errors.

Explore: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

Why is Standard Error Important?

First, the standard error of a sample accounts for statistical fluctuation.

Researchers depend on this statistical measure to know how much sampling fluctuation exists in their sample data. In other words, it shows the extent to which a statistical measure varies from sample to population.

In addition, standard error serves as a measure of accuracy. Using standard error, a researcher can estimate the efficiency and consistency of a sample to know precisely how a sampling distribution represents a population.

How Many Types of Standard Error Exist?

There are five types of standard error which are:

Standard error of the mean
Standard error of measurement
Standard error of the proportion
Standard error of estimate
Residual Standard Error

1. Standard Error of the Mean (SEM)

The standard error of the mean accounts for the difference between the sample mean and the population mean. In other words, it quantifies how much variation is expected to be present in the sample mean that would be computed from every possible sample, of a given size, taken from the population.

How to Find SEM (With Formula)

SEM = Standard Deviation ÷ √n

Where;

n = sample size

Suppose that the standard deviation of observation is 15 with a sample size of 100. Using this formula, we can deduce the standard error of the mean as follows:

SEM = 15 ÷ √100

Standard Error of Mean in 1.5

2. Standard Error of Measurement

The standard error of measurement accounts for the consistency of scores within individual subjects in a test or examination.

This means it measures the extent to which estimated test or examination scores are spread around a true score.

A more formal way to look at it is through the 1985 lens of Aera, APA, and NCME. Here, they define a standard error as “the standard deviation of errors of measurement that is associated with the test scores for a specified group of test-takers….”

Read: 7 Types of Data Measurement Scales in Research

How to Find Standard Error of Measurement

Where;

rxx is the reliability of the test and is calculated as:

Rxx = S2T / S2X

Where;

S2T = variance of the true scores.

S2X = variance of the observed scores.

Suppose an organization has a reliability score of 0.4 and a standard deviation of 2.56. This means

SEm = 2.56 × √1–0.4 = 1.98

3. Standard Error of the Estimate

The standard error of the estimate measures the accuracy of predictions in sampling, research, and data collection. Specifically, it measures the distance that the observed values fall from the regression line which is the single line with the smallest overall distance from the line to the points.

How to Find Standard Error of the Estimate

The formula for standard error of the estimate is as follows:

Where;

σest is the standard error of the estimate;

Y is an actual score;

Y’ is a predicted score, and;

N is the number of pairs of scores.

The numerator is the sum of squared differences between the actual scores and the predicted scores.

4. Standard Error of Proportion

The standard of error of proportion in an observation is the difference between the sample proportion and the population proportion of your target audience. In more technical terms, this variable is the spread of the sample proportion about the population proportion.

How to Find Standard Error of the Proportion

The formula for calculating the standard error of the proportion is as follows:

Where;

P (hat) is equal to x ÷ n (with number of success x and the total number of observations of n)

5. Residual Standard Error

Residual standard error accounts for how well a linear regression model fits the observation in a systematic investigation. A linear regression model is simply a linear equation representing the relationship between two variables, and it helps you to predict similar variables.

How to Calculate Residual Standard Error

The formula for residual standard error is as follows:

Residual standard error = √Σ(y – ŷ)2/df

where:

y: The observed value

ŷ: The predicted value

df: The degrees of freedom, calculated as the total number of observations – total number of model parameters.

As you interpret your data, you should note that the smaller the residual standard error, the better a regression model fits a dataset, and vice versa.

How Do You Calculate Standard Error?

The formula for calculating standard error is as follows:

Where

σ – Standard deviation

n – Sample size, i.e., the number of observations in the sample

Here’s how this works in real-time.

Suppose the standard deviation of a sample is 1.5 with 4 as the sample size. This means:

Standard Error = 1.5 ÷ √4

That is; 1.5 ÷2 = 0.75

Alternatively, you can use a standard error calculator to speed up the process for larger data sets.

How to Interpret Standard Error Values

As stated earlier, researchers use the standard error to measure the reliability of observation. This means it allows you to compare how far a particular variable in the sample data is from the population of interest.

Calculating standard error is just one piece of the puzzle; you need to know how to interpret your data correctly and draw useful insights for your research. Generally, a small standard error is an indication that the sample mean is a more accurate reflection of the actual population mean, while a large standard error means the opposite.

Standard Error Example

Suppose you need to find the standard error of the mean of a data set using the following information:

Standard Deviation: 1.5

n = 13

Standard Error of the Mean = Standard Deviation ÷ √n

1.5 ÷ √13 = 0.42

How Should You Report the Standard Error?

After calculating the standard error of your observation, the next thing you should do is present this data as part of the numerous variables affecting your observation. Typically, researchers report the standard error alongside the mean or in a confidence interval to communicate the uncertainty around the mean.

Applications of Standard Error

The most common application of standard error are in statistics and economics. In statistics, standard error allows researchers to determine the confidence interval of their data sets, and in some cases, the margin of error. Researchers also use standard error in hypothesis testing and regression analysis.

FAQs About Standard Error

What Is the Difference Between Standard Deviation and Standard Error of the Mean?

The major difference between standard deviation and standard error of the mean is how they account for the differences between the sample data and the population of interest.

Researchers use standard deviation to measure the variability or dispersion of a data set to its mean. On the other hand, the standard error of the mean accounts for the difference between the mean of the data sample and that of the target population.

Something else to note here is that the standard error of a sample is always smaller than the corresponding standard deviation.

What Is The Symbol for Standard Error?

During calculation, the standard error is represented as σx̅.

Is Standard Error the Same as Margin of Error?

No. Margin of Error and standard error are not the same. Researchers use the standard error to measure the preciseness of an estimate of a population, meanwhile margin of error accounts for the degree of error in results received from random sampling surveys.

The standard error is calculated as s / √n where;

s: Sample standard deviation

n: Sample size

On the other hand, margin of error = z*(s/√n) where:

z: Z value that corresponds to a given confidence level

s: Sample standard deviation

n: Sample size

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A mathematical tool used in statistics to measure variability

What is Standard Error?

Standard error is a mathematical tool used in statistics to measure variability. It enables one to arrive at an estimation of what the standard deviation of a given sample is. It is commonly known by its abbreviated form – SE.

Standard error is used to estimate the efficiency, accuracy, and consistency of a sample. In other words, it measures how precisely a sampling distribution represents a population.

It can be applied in statistics and economics. It is especially useful in the field of econometrics, where researchers use it in performing regression analyses and hypothesis testing. It is also used in inferential statistics, where it forms the basis for the construction of the confidence intervals.

Some commonly used measures in the field of statistics include:

Standard error of the mean (SEM)
Standard error of the variance
Standard error of the median
Standard error of a regression coefficient

Calculating Standard Error of the Mean (SEM)

The SEM is calculated using the following formula:

Where:

σ – Population standard deviation
n – Sample size, i.e., the number of observations in the sample

In a situation where statisticians are ignorant of the population standard deviation, they use the sample standard deviation as the closest replacement. SEM can then be calculated using the following formula. One of the primary assumptions here is that observations in the sample are statistically independent.

Where:

s – Sample standard deviation
n – Sample size, i.e., the number of observations in the sample

Importance of Standard Error

When a sample of observations is extracted from a population and the sample mean is calculated, it serves as an estimate of the population mean. Almost certainly, the sample mean will vary from the actual population mean. It will aid the statistician’s research to identify the extent of the variation. It is where the standard error of the mean comes into play.

When several random samples are extracted from a population, the standard error of the mean is essentially the standard deviation of different sample means from the population mean.

However, multiple samples may not always be available to the statistician. Fortunately, the standard error of the mean can be calculated from a single sample itself. It is calculated by dividing the standard deviation of the observations in the sample by the square root of the sample size.

Relationship between SEM and the Sample Size

Intuitively, as the sample size increases, the sample becomes more representative of the population.

For example, consider the marks of 50 students in a class in a mathematics test. Two samples A and B of 10 and 40 observations, respectively, are extracted from the population. It is logical to assert that the average marks in sample B will be closer to the average marks of the whole class than the average marks in sample A.

Thus, the standard error of the mean in sample B will be smaller than that in sample A. The standard error of the mean will approach zero with the increasing number of observations in the sample, as the sample becomes more and more representative of the population, and the sample mean approaches the actual population mean.

It is evident from the mathematical formula of the standard error of the mean that it is inversely proportional to the sample size. It can be verified using the SEM formula that if the sample size increases from 10 to 40 (becomes four times), the standard error will be half as big (reduces by a factor of 2).

Standard Deviation vs. Standard Error of the Mean

Standard deviation and standard error of the mean are both statistical measures of variability. While the standard deviation of a sample depicts the spread of observations within the given sample regardless of the population mean, the standard error of the mean measures the degree of dispersion of sample means around the population mean.