similar to: Null and Alternate hypothesis for Significance test

Dear R users I am using KS test to compare two different distribution for the same variable (temperature) for two different time periods. H0: the two distributions are equal H1: the two distributions are different ks.test (temp12, temp22) Two-sample Kolmogorov-Smirnov test data: temp12 and temp22 D = 0.2047, p-value < 2.2e-16 alternative hypothesis: two-sided Warning message: In

Dear R developers, Currently many (all?) test functions in R describe the alternative hypothesis, but not the the null hypothesis being tested. For example, cor.test: > require(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="kendall")) Kendall's rank correlation tau data: mpg and wt z = -5.7981, p-value = 0.000000006706 alternative hypothesis: true tau is not

Hello R users, I've used the following help two compare two regression line slopes. Wanted to test if they differ significantly: Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope

Dear colleague: Actually that is what is done. When using the z-test between proportions in two different groups, or using chi-squared to test the null hypothesis of equal proportions of two or more groups, the null hypothesis is that H0: p1=p2=p3.....=p where this p is the pooled proportion, the proportion in the summed groups = n1+n2+n3+..../N1+N2+N3.... in the z-test between two

Is this how a t hypothesis test is done when I don't have the actual data, but just the summarized statistics: > #Homework 9.2.6 [1] > n<-31 > xbar<-3.10 > s_x<-1.469 > m<-57 > ybar<-2.43 > s_y<-1.35 > s_pooled<- (((n-1)*s_x^2) + ((m-1)*s_y^2)) / (n + m - 2) > s_pooled [1] 1.939521 > t_obs <- (xbar - ybar) / (s_pooled * (sqrt(1/n + 1/m)))

I have a question regarding significance testing for the difference in the ratio of means. The data consists of a control and a test group, each with and without treatment. I am interested in testing if the treatment has a significantly different effect (say, in terms of fold-activation) on the test group compared to the control. The form of the data with arbitrary n and not assuming equal

Hi, I have been unable to find how to go about changing the significance level in R for hypothesis testing and regression analysis. R has a default setting to alpha=95% (a significance level of 95%), but I want to be able to decrease or increase this level when necessary. If you could give me instructions on how to change the significance level in R for hypothesis testing and regression analysis

I estimate two competing simple regression models, A and B where the LHS is the same in both cases but the predictor is different ( I handle the intercept issue based on other postings I have seen ). I estimate the two models on a weekly basis over 24 weeks. So, I end up with 24 RSquaredAs and 24 RsquaredBs, so essentally 2 time series of Rsquareds. This doesn't have to be necessarily thought

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 First of all, I'm a physicist and therefore I'm not much used to use statistics. So, please forgive me if this is a FAQ or stupid, but I failed to find the answer by myself. I want to use corr.test to test for the correlation of two data sets (actually I have a lot of data set and perform pairwise testing). But I wanted to find sets where the

Hi, I have a problem with Kolmogorov-Smirnov test fit. I try fit distribution to my data. Actualy I create two test: - # First Kolmogorov-Smirnov Tests fit - # Second Kolmogorov-Smirnov Tests fit see below. This two test return difrent result and i don't know which is properly. Which result is properly? The first test return lower D = 0.0234 and lower p-value = 0.00304. The lower 'D'

Hi There, Using t.test to test hypothesis about which one is greater, A or B? where A={10,20,30},B={25,30,15}. My question is which of the following conclusions is right? #################hypothesis testing 1 h0: A greater than or equal to B h1: A less than B below is splus code A=c(10,20,30) B=c(25,30,15) t.test(c(10,20,30),c(25,30,15),alternative="less") output: p-value=0.3359

Hi, I'm using the ks.test() on two vectors. I looked up the reference and also coded up a version of the two sample Smirnov test. My question is that how can I decide from the output of R that the two vectors x & y come from the same distribution? Am I correct in assuming that smaller D values indicate that they come from the same distribution? In addition how can I use the p value that

Hola Ruben, Sí precisamente es lo que comentas, en matemáticas no se suele llamar bucketización (este término se emplea más en informática) sino datos agrupados. Pero la idea es la que tu mismo dices. Respecto a las gráficas que has puesto, me han aclarado mucho sobre el tema, gracias. Si realizo lo mismo, por ejemplo con nbucket=1000 sigo obteniendo un p-valor de 1. Es decir, que casi le

Hi everybody, somehow i dont get the shapiro wilk test for normality. i just can?t find what the H0 is . i tried : shapiro.test(rnorm(5000)) Shapiro-Wilk normality test data: rnorm(5000) W = 0.9997, p-value = 0.6205 If normality is the H0, the test says it?s probably not normal, doesn ?t it ? 5000 is the biggest n allowed by the test... are there any other test ? ( i know qqnorm

Hello. How can I compute the Bootstrap p-Value for a one- and two sided test, when I have a bootstrap sample of a statistic of 1000 for example? My hypothesis are for example: 1. Two-Sided: H0: mean=0 vs. H1: mean!=0 2. One Sided: H0: mean>=0 vs. H1: mean<0 I hope you can help me Thanks in advance Regards, Andreas

Dear all; A very basic question. I have the following data: ************************************************************************************ A <- 1/1000*c(347,328,129,122,18,57,105,188,57,257,53,108,336,163, 62,112,334,249,45,244,211,175,174,26,375,346,153,32, 89,32,358,202,123,131,88,36,30,67,96,135,219,122, 89,117,86,169,179,54,48,40,54,568,664,277,91,290,

Dear stats experts: Me and my little brain must be missing something regarding bootstrapping. I understand how to get a 95%CI and how to hypothesis test using bootstrapping (e.g., reject or not the null). However, I'd also like to get a p-value from it, and to me this seems simple, but it seems no-one does what I would like to do to get a p-value, which suggests I'm not understanding

Hi everybody, while performing ks.test for a standard exponential distribution on samples of dimension 2500, generated everytime as new, i had this strange behaviour: >data<-rexp(2500,0.4) >ks.test(data,"pexp",0.4) One-sample Kolmogorov-Smirnov test data: data D = 0.0147, p-value = 0.6549 alternative hypothesis: two.sided >data<-rexp(2500,0.4)

Hi Let's assume, we have a multiple linear regression, such as the one using the Scottish hills data (MASS, data(hills)): one dependent variable: time two independent var (metric): dist, climb if I am interested, after (!) fitting a lm: my. lm <- lm(time ~ dist + climb, data = hills) in the equivalence (or non-equivalence) of the two predictors "dist" and

Hi R-Help! My question: I have lifetime/failure data of machines with different stress levels and i think an weibull/extreme value distribution would fit this data. So I did: model1 <- survreg(Surv(lfailure)~stress,data=steel,dist="extreme") (where lfailure=log(failure)) Now I would like to do a likelihood ratio test to test the hypothesis H0: location parameters of the