P Schnell
  • Member for 7 years, 11 months
  • Last seen more than 3 years ago
Paired t-test with subsamples in R?
1 votes

The paired t test takes advantage of natural pairings of observations between samples. In this case the pairs would be one measurement in a forest quadrate and the same measurement in the ...

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Binomial random variable conditional on another one
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10 votes

Let $X = \sum_{i=1}^{n} X_i$, with $X_i \overset{iid}{\sim} Bin(1, p)$, and $Z = \sum_{i=1}^{n} Z_i$, with $Z_i \overset{iid}{\sim} Bin(1, q)$. If all the $X_i$ and $Z_i$ are mutually independent, ...

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Count data on proportion or different kind of type?
4 votes

Note that although you're counting how many farmers give each response, the response itself isn't a count, it's a proportion. For a response that is a proportion, the usual family of distributions ...

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Logistic Regression - Binning - Interpretting p values
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2 votes

An insignificant p-value indicates that the estimated coefficient (first column) is not significantly different from zero. In interpretation, there's insufficient evidence to conclude that having ...

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Calculating Bias from bivariate data
1 votes

First of all, I think you have the positive and negative bias areas reversed. If Temp_Real is the true value and Temp_Symmetric is an estimate/calculation/test measurement, then you have positive bias ...

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Is $F(E[Y_n]) \approx E[F(Y_n)]$ a reasonable approximation?
3 votes

Jensen's Inequality states that for a random variable $Y$ and a convex function $\varphi$, we have $$ \varphi(E[Y]) \leq E[\varphi(Y)]. $$ If $\varphi$ is concave, then the direction of the inequality ...

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What are the Mild Regularity Conditions in the context of GEEs?
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11 votes

Regularity conditions in statistics usually refer to requirements that functions or groups of functions (usually probability density functions) "behave well" in various senses. These are assumptions ...

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Example of a subset of $\mathbb{R}$ which is not measurable?
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9 votes

The standard example is the Vitali Set. It is constructed from the equivalence relation $x\sim y \leftrightarrow x-y\in\mathbb{Q}$, where $\mathbb{Q}$ is the set of rationals. Consider a set $V$ ...

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Discrete uniform random variable(?) taking all rational values in a closed interval
13 votes

This "random variable" is similar to the idea of having a flat prior on the entire real line (your second example). To show that there can be no random variable $X$ such that $P(X=q)=c$ for all $q\in ...

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Meaning of 'number of parameters' in AIC
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18 votes

As mugen mentioned, $k$ represents the number of parameters estimated. In other words, it's the number of additional quantities you need to know in order to fully specify the model. In the simple ...

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Compare Log-Normal Distributions
1 votes

These data sets appear to have two fundamentally different types of data. Data Set 1 contains a distribution of fuel consumptions for vehicles in each year. This distribution has a mean and standard ...

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Wald test for gamma distributed data?
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4 votes

The Wald test assumes that the estimator is asymptotically normally distributed. This is the case for the mean from most distributions, as well as maximum likelihood estimators. Therefore you can use ...

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How to test if counts differ across temporal categories?
0 votes

There aren't much relevant statistics you can do with the highly aggregated data you have (especially since you don't have any measure of the variance within each time period), so given how you've ...

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Is it compulsory for a linear regression analysis that a dependent as well as independent variable have equal variance?
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7 votes

The classical linear regression model is $$Y_i = \beta_0 + \beta_1 X_{i,1} + \cdots + \beta_m X_{i,m} + \varepsilon_i$$ where the $\varepsilon_i$ are independent $\mathrm{normal}(0,\sigma^2)$ ...

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Generate binomial sample with (pretty) exact probability
1 votes

One particularly blunt way of doing it is to keep generating samples until you get a sample that you like (i.e. sample mean close to population mean). N <- 100 n <- 18 p <- 0.5 e <- 0.01 ...

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Analysis of the Residuals vs Fitted
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7 votes

Note that within each diagonal band, the residual decreases by one unit for every one unit the fitted value increases. This looks to me like the response for a given subject remains relatively ...

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Transformations of Variables
2 votes

Either model could work, and which to use depends on why you transformed. If you took the log because $Y$ is expected to be linearly related to $\log X_1$ (e.g. response to log dose of medication), ...

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General method for deriving the standard error
5 votes

The standard error is the standard deviation of the statistic (under the null hypothesis, if you're testing). A general method for finding standard error would be to first find the distribution or ...

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Distribution function of an indicated random variable
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2 votes

Think about how you can partition $\mathbb{R}$ such that placing $y$ in different intervals yields different behavior. Notice that $Y$ can either be zero or in the interval $\left(a, b\right)$. If $y ...

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Transition from pure mathematics to statistics via operational research
4 votes

I don't know what the requirements are in the UK, but many good American statistics and biostatistics graduate programs even accept and fund PhD students who only have an undergraduate degree in ...

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Are these statements about p-values correct?
5 votes

There are two p-values of interest. The critical p-value, also known as the $\alpha$-level or significance level, is decided and fixed before the study / analysis is performed. This critical p-value ...

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Formal test for heteroscedasticity
0 votes

One such test is the Brown-Forsythe test, which is derived from ANOVA. The basic idea is that you pick a way to partition your data and then compare the mean absolute deviations from the median of ...

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The positive stable distribution in R
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7 votes

The short answer is that your $\delta$ is fine, but your $\gamma$ is wrong. In order to get the positive stable distribution given by your formula in R, you need to set $$ \gamma = |1 - i \tan \left(\...

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Reference paper and/or books about spatial data analysis, possibly bayesian
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4 votes

The book I have is Hierarchical Modeling and Analysis for Spatial Data by Banerjee, Carlin, and Gelfand. It's used in the spatial statistics course at the University of Minnesota where two of the ...

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Lognormal with negative values
4 votes

I think you've confused what the lognormal distribution is. If Y follows a lognormal distribution, then log(Y) follows a normal distribution. You seem to be thinking, incorrectly, that if Z follows a ...

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Dummy variables in a multiple regression
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3 votes

To get started, let's look at an example of what your regression output might look like. Pred Estimate StdErr t p sig A1 1.0 0.2 5.00 0.0005 * A2 -1.9 2.0 -...

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Multiple regression with categorical and numeric predictors
13 votes

Try this: fit <- glm(wealth_indicator ~ factor(ranking) + age_in_years + factor(ranking) * age_in_years) The factor() command will make sure that R knows that your variable is ...

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Why is logistic regression a linear model?
45 votes

The logistic regression model is of the form $$ \mathrm{logit}(p_i) = \mathrm{ln}\left(\frac{p_i}{1-p_i}\right) = \beta_0 + \beta_1 x_{1,i} + \beta_2 x_{2,i} + \cdots + \beta_p x_{p,i}. $$ It is ...

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