# how to compute the p value of t-test a <- 5 s <- 2 n <- 20 xbar <- 7 t <- (xbar-a)/(s/sqrt(n)) http://blog.csdn.net/downmoon/article/details/5939694 # f(x) = Gamma((n+1)/2) / (sqrt(n pi) Gamma(n/2)) (1 + x^2/n)^-((n+1)/2) n=60 x=1.074 (...
T-testsare of different types and can be used under different circumstances. If you need to compare a mean to a hypothesized value, you can use a simple one-sample T-test. On the other hand, if you have two groups and need to compare the means of two independent samples, you can use...
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Dear author, first of all, thanks for your great work. After reading your paper, I really want to know how to calculate the params and the runtime of adding Focals Conv to VoxelRCNN as u mentioned in your Experiments, and I want to try it, but I don't know how to do it, there...
In mmdetection, train_detector() allows seeing validation mAP. But, I'd also like to check mAP on the test dataset, thus I wrote down the below code but it does not work... from mmdet.apis import single_gpu_test from mmdet.datasets impor...
A cluster is a set of computers that you configure to work together and view as a single system. The computers configured in the cluster handle the same kinds of tasks. For example, they'll all host websites, APIs, or run compute-intensive work. ...
To resolve the response time variability that leads to this tail latency problem, we must understand the sources of performance variability.1 Use of shared resources: Many different VMs (and applications within those VMs) contend for a shared pool of compute resources. In rare cases, it is poss...
8). Select the default base port - select "Next". 9). Select "install". 10). Select "finish". 11). Restart your computer Now you are ready to run a smoke test using an LSF compute context(RxLsfCluster) to see if things work....
T-tests analyze hypotheses about one or two sample means. Learn how t-tests use t-values and t-distributions to compute probabilities and test hypotheses.
statistic: Test statistic used to compute the p-value. df: degrees of freedom. p: p-value. Note that, you can obtain a detailed result by specifying the option detailed = TRUE. genderweight %>% t_test(weight ~ group, detailed = TRUE) %>% add_significance() ## ...