P Schnell
• Member for 7 years, 11 months
• Last seen more than 3 years ago

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 ...

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, ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 $V$ ...

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 $q\in ... View answer Accepted answer 18 votes As mugen mentioned,$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 ... View answer 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 ... View answer Accepted answer 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 ... View answer 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 ... View answer Accepted answer 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$\mathrm{normal}(0,\sigma^2)$... View answer 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 ... View answer Accepted answer 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 ... View answer 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), ... View answer 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 ... View answer Accepted answer 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 ...

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 ...

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 ...

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 $$\gamma = |1 - i \tan \left(\... View answer Accepted answer 4 votes The book I have is Hierarchical Modeling and Analysis for Spatial Data by Banerjee, Carlin, and Gelfand. It's used in the spatial statistics course at the University of Minnesota where two of the ... View answer 4 votes I think you've confused what the lognormal distribution is. If Y follows a lognormal distribution, then log(Y) follows a normal distribution. You seem to be thinking, incorrectly, that if Z follows a ... View answer Accepted answer 3 votes To get started, let's look at an example of what your regression output might look like. Pred Estimate StdErr t p sig A1 1.0 0.2 5.00 0.0005 * A2 -1.9 2.0 -... View answer 13 votes Try this: fit <- glm(wealth_indicator ~ factor(ranking) + age_in_years + factor(ranking) * age_in_years) The factor() command will make sure that R knows that your variable is ... View answer 45 votes The logistic regression model is of the form$$ \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 ...