hwY1731TestIdResetStatisticType INTEGER { invalid(1), singleloss(2), dualloss(3), onewaydelay(4), twowaydelay(5), singlesynloss(6) } Read-write The value of this object identifies the type of deleted data: 1: invalid(1) 2: singleloss(2) 3: dualloss(3) 4: onewaydelay(4) 5:...
Multiple R:对应的数据是相关系数 R Square:对应的数值是测定系数，或称拟合优度，它是相关系数的平方 Adjusted R Square:对应的是校正测定系数，校正公式为R_a=1-((n-1)(1-R^2))/(n-m-1)=1-(n-1)(1-R^2)/v, 其中，n为样本数，你这里为15， m为变量数，你这里为1， v为自由度...
Formula of the test statistic Compute one proportion z-test in R R functions: binom.test() & prop.test() Compute one-proportion z-test Interpretation of the result Access to the values returned by prop.test() See also Infos What is one-proportion Z-test?
For use in significance testing, the distribution of the test statistic is approximated as an ordinary Student's t distribution with the degrees of freedom calculated using This is known as the Welch–Satterthwaite equation. The true distribution of the test statistic actually depends (slightly) on ...
(p-hat) of312/360=0.867, and using the equations above, find that my test statisticzturns out to be-2.108, with a correspondingp-valueof0.0175. I reject the null hypothesis that the true population proportion is 0.90 in favor of the alternative, and start making plans to launch my ...
## variable statistic p ## <chr> <dbl> <dbl> ## 1 len 0.967 0.109 From the output above, the p-value > 0.05 implying that the distribution of the data are not significantly different from normal distribution. In other words, we can assume the normality. ...
for key,value in dftest[4].items(): dfoutput['Critical Value (%s)' % key] = value print(dfoutput) # 第二种显示方式 print(dftest) (1)第一种显示方式如图所示: 具体的参数含义如下所示: Test Statistic : T值,表示T统计量 p-value: p值,表示T统计量对应的概率值 ...
t.test()[stats package]: R base function. Interpret and report the paired t-test Add p-values and significance levels to a plot Calculate and report the paired t-test effect sizeusingCohen’s d. Thedstatistic redefines the difference in means as the number of standard devia...
U=min(rA,rA′) ,这也就是我们的检验统计量(test statistic),这里的 U=16 通过临界值(critical value)表格,我们可以得到临界值是 C=11, 16>11 所以我们可以不拒绝这个假设,即H0:mA=mB。 3.The Wilcoxon 秩和检验的统计量分布的研究 首先,我们先列出唯一一个假设: 1. \Omega_A,\Omega_B 是连续(con...
statistic = -0.76376, runs = 4, n1 = 4, n2 = 4, n = 8, p-value = 0.445 alternative hypothesis: nonrandomness The p-value is slightly different from approach 1, however, it’s pointing to the same inference. Since the p-value of the test is 0.445 that is greater than 0.05, indi...