Linked Questions
16 questions linked to/from Calculating required sample size, precision of variance estimate?
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Confidence of a variance estimate [duplicate]
Possible Duplicate:
Calculating required sample size, precision of variance estimate?
I would like to present some measure of how variable a particular phenomenon is. This phenomenon appears to ...
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4
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Why is sample standard deviation a biased estimator of $\sigma$?
According to the Wikipedia article on unbiased estimation of standard deviation the sample SD
$$s = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \overline{x})^2}$$
is a biased estimator of the SD of the ...
- 769
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Reference for $\mathrm{Var}[s^2]=\sigma^4 \left(\frac{2}{n-1} + \frac{\kappa}{n}\right)$?
In his answer to my previous question, @Erik P. gives the expression
$$
\mathrm{Var}[s^2]=\sigma^4 \left(\frac{2}{n-1} + \frac{\kappa}{n}\right) \>,
$$
where $\kappa$ is the excess kurtosis of the ...
- 3,801
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What is the distribution of the variance of a sample from an unknown distribution?
I am sampling from a parameter with unknown distribution. I would like to calculate a 95% CI for the standard deviation of the sample.
@cardinal provides a nice general solution for calculating a CI ...
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2
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is it a good practice to use K-Fold cross validation instead of training, validation and test set?
i have a Dataset of 5000 samples for a regression problem, now with this number of samples can i and is it better to use K-Fold cross validation instead of the validation set as an alternative?
if ...
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1
answer
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Estimate for the error of an error?
Searching through Wikipedia and StackExchange I managed to understand that, for a set of $N$ normally distributed values, the unbiased variance $\textrm{Var}[s^2]$ of the unbiased variance $s^2$ of ...
1
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1
answer
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Number of observations to study the reproducibility
I'm running an experiment which is about to investigate the influence of gases on the resistance of sensors. Since this is chemistry and gases are experimentally rather hard to handle, I would like to ...
- 3,163
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How to best find standard error *across* linear regression fits?
So I have a scenario where there are $n = 8$ subjects, which are observed at 20 time points and having heteroscedasticity in their response. For example, consider the following:
...
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0
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1
answer
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The Unknown Variances in Sample Size Calculation for Two Proportions vs Means
I recognize that the sample size calculation for the two-sample test at $\alpha = 0.05$ and $\beta = 0.80$ under a normal distribution becomes:
$$
n_{i} = \frac{16 * \sigma^2}{\Delta^2}
$$
But, what ...
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2
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Confidence Interval on the standard deviation [duplicate]
Supposed we have $n = 15$ independent samples $X_1, X_2, ..., X_n$ from distribution $N(\mu, \sigma)$. Sample mean $\bar{X} = 2.4$ and sample variance $\hat{\sigma^2} = 0.55$
What's the 95% ...
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0
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Marginal interpretation of fixed effects in GLMM
I understand that when applying GLMMs (e.g. in logistic mixed effects regression), the interpretation of the coefficients for the fixed effects is that they are also conditional on the random effects (...
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1
vote
0
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Standard deviation of errors
I have a multiple regression model (information derived from gretl):
$$Y=‐3,859921 \ln(P)
+
1,707514\,
(A)
+
3,578656$$
$$\quad\quad\space(1,216387)\quad\quad\quad(1,259650)\quad\quad(0,323867)$$
...
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1
answer
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minimum number of points to calculate variance
It seems to be that the minimum number of observations needed to calculate variance is 2. I can see the logic behind this because by logic, there can not be variance for a single point.
But on the ...
2
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0
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Error of the variance
I have a collection of $(x,y,z)$ data points. I want to compute the mean, $\mu$, and variance, $\sigma^2$, along each axis, as well as the errors on each. I know that the standard error of the mean ...
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0
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1
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Need equality of variance test between control and experimental conditions on a within subjects with replication dataset
I have some proteomics data of 16 biological replicate samples split between 8 control and 8 knockdown. One of the tests I wanted to do was to see if the knockdown impacts the variance of the protein ...
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