statistics

问题 When I call rnorm passing a single value as mean, it's obvious what happens: a value is generated from Normal(10,1). y <- rnorm(20, mean=10, sd=1) But, I see examples of a whole vector being passed to rnorm (or rcauchy , etc..); in this case, I am not sure what the R machinery really does. For example: a = c(10,22,33,44,5,10,30,22,100,45,97) y <- rnorm(a, mean=a, sd=1) Any ideas? 回答1: The number of random numbers rnorm generates equals the length of a. From ?rnorm : n: number of observations.

问题 It seems like a pretty simple question, but I can't find the answer. I have a dataframe (lets call it df ), containing n=100 columns ( C1 , C2 ,..., C100 ) and 50 rows ( R1 , R2 ,..., R50 ). I tested all the column in the data frame to be sure they are numeric. I want to know if the data in each columns has a normal distribution using the shapiro.test() function. I am able to do it column by colums using the code : > shapiro.test(df$Cn) or > shapiro.test(df[,c(Cn)]) However when I try to do

问题 I have a list of 100,000 objects. Every list element has a "weight" associated with it that is a positive int from 1 to N. What is the most efficient way to select a random element from the list? I want the behavior that my distribution of randomly chosen elements is the same as the distribution of weights in the list. For example, if I have a list L = {1,1,2,5}, I want the 4th element to be selected 5/9ths of the time, on average. Assume inserts and deletes are common on this list, so any

问题 How do I do an F-test to check if the variance is equivalent in two vectors in Python? For example if I have a = [1,2,1,2,1,2,1,2,1,2] b = [1,3,-1,2,1,5,-1,6,-1,2] is there something similar to scipy.stats.ttest_ind(a, b) I found sp.stats.f(a, b) But it appears to be something different to an F-test 回答1: The test statistic F test for equal variances is simply: F = Var(X) / Var(Y) Where F is distributed as df1 = len(X) - 1, df2 = len(Y) - 1 scipy.stats.f which you mentioned in your question

问题 I'm interested in making a plot with a least squares regression line and line segments connecting the datapoints to the regression line as illustrated here in the graphic called perpendicular offsets: http://mathworld.wolfram.com/LeastSquaresFitting.html (from MathWorld - A Wolfram Web Resource: wolfram.com) I have the plot and regression line done here: ## Dataset from http://www.apsnet.org/education/advancedplantpath/topics/RModules/doc1/04_Linear_regression.html ## Disease severity as a

问题 I am not a statistician (more of a researchy web developer) but I've been hearing a lot about scipy and R these days. So out of curiosity I wanted to ask this question (though it might sound silly to the experts around here) because I am not sure of the advances in this area and want to know how people without a sound statistics background approach these problems. Given a set of real numbers observed from an experiment, let us say they belong to one of the many distributions out there (like

问题 Google Spreadsheets currently does not support the standard function TDIST - i.e. the Student's t-distribution. This function is critical for calculating p-values. It seems that this is related to the fact that no integral-using functions (AFAICT) are implemented either. However, Google Docs allows people to add and publish their own scripts, in JavaScript. So ideally we should have something like: function tdist(t_value, degrees_of_freedom, two_tailed [defaults true]) {...} Anyone know of

问题 I have a data frame x1 , which was generated with the following piece of code, x <- c(1:10) y <- x^3 z <- y-20 s <- z/3 t <- s*6 q <- s*y x1 <- cbind(x,y,z,s,t,q) x1 <- data.frame(x1) I would like to extract the y-axis intercept and the slope of the linear regression fit for the data, x y z s t q 1 1 1 -19 -6.333333 -38 -6.333333 2 2 8 -12 -4.000000 -24 -32.000000 3 3 27 7 2.333333 14 63.000000 4 4 64 44 14.666667 88 938.666667 5 5 125 105 35.000000 210 4375.000000 6 6 216 196 65.333333 392

问题 I used geom_tile() for plot 3 variables on the same graph... with tile_ruined_coop<-ggplot(data=df.1[sel1,])+ geom_tile(aes(x=bonus, y=malus, fill=rf/300))+ scale_fill_gradient(name="vr")+ facet_grid(Seuil_out_coop_i ~ nb_coop_init) tile_ruined_coop and I am pleased with the result ! But What kind of statistical treatment is applied to fill ? Is this a mean ? 回答1: To plot the mean of the fill values you should aggregate your values, before plotting. The scale_colour_gradient(...) does not

问题 I have the following Problem: In a data frame I have a lot of rows and columns with the first row being the date. For each date I have more than 1 observation and I want to summarize them. My df looks like that (date replaced by ID for ease of use): df: ID Cash Price Weight ... 1 0.4 0 0 1 0.2 0 82 ... 1 0 1 0 ... 1 0 3.2 80 ... 2 0.3 1 70 ... ... ... ... ... ... I want to group them by the first column and then summarize all rows BUT with different functions: The function Cash and Price