All Questions
17
questions
0
votes
1
answer
485
views
which non parametric test is best for ordinal data?
I want to to do the test where i have to check if first column which is income, and 1 shows < 500, 2 = 1000, 3 = 2000 income values from the data, and which the other column which is a dependent ...
1
vote
4
answers
131
views
Business use cases for Wilcoxon signed-rank test
The Wilcoxon signed-rank test is generally used for non-parametric data (i.e. not normally distributed). When the sample gets large, the data will be approximately normally distributed. Therefore ...
1
vote
1
answer
230
views
Why does my omnibus test do not agree with post hoc test?
So I am running a simple Dunn's Test on my data. After running the Kruskal-Wallis H Test, it returned a result
...
4
votes
1
answer
106
views
Nonparemetric tests: how to support the null hypothesis you claim to be testing
Let us assume that we have taken an unbalanced number of independent random samples from 5 different populations, which will be analogous to 5 different locations in this example. Each observation ...
1
vote
0
answers
36
views
2 sample t test
I was thinking that a simple 2 sample t test is not that simple when dealing with skewed data.
Considere 2 samples, each one with diferent skewness and kurtosis, both with a big sample sizes (say n > ...
2
votes
1
answer
8k
views
Fligner-Killeen test of homogeneity of variances interpretation
I have two samples that I want to verify that variances are equals in order to apply Wilcoxon rank sum test that assume that the variance are equals.
Here a boxplot
As you can see the variance ...
0
votes
1
answer
359
views
Do I need to check for normality in a one-way test (in R)?
http://www.sthda.com/english/wiki/one-way-anova-test-in-r
Here it says when the LeveneTest has small p-value we can use the alternative one
...
28
votes
2
answers
19k
views
Non-parametric test if two samples are drawn from the same distribution
I would like to test the hypothesis that two samples are drawn from the same population, without making any assumptions about the distributions of the samples or the population. How should I do this?
...
0
votes
0
answers
54
views
non-parametric statistical testing is better than the parametric one?
Conduct both a parametric and a non-parametric test for the median value of 80. Are the results from both the tests similar?
My code is:
...
10
votes
1
answer
2k
views
Are parametric tests on rank transformed data equivalent to non-parametric test on raw data?
Many non-parametric tests are identical to their parametric equivalent on ranked data. At least, that's what I learned from this blog post on Friedman's test and skimming this 1981 article.. This ...
3
votes
1
answer
3k
views
Critical value for Wilcoxon one-sample signed-rank test in R
I am trying to find the critical value for the Wilcoxon one-sample signed-rank test. Currently, I can find the value using tables. I looked at qwilcox() in R, but ...
3
votes
0
answers
255
views
Testing semi-parametric versus parametric model
I am estimating a (semi)parametric and a parametric model for a panel data set, and I want to test the functional form by applying the method proposed by Henderson et al. (2008, p.266–267). In ...
1
vote
0
answers
304
views
What is the test used in R in sm.regression to compare reference model with kernel regression?
I am doing bivariate kernel regression using the sm.regression function:
http://cran.r-project.org/web/packages/sm/sm.pdf
There is an option to compare the nonparametric estimate with linear model. ...
3
votes
1
answer
3k
views
Hypothesis testing - Wilcoxon test, bootstrapping, or something else?
A colleague has developed a treatment for to "prevent falls" in cognitively impaired, psychiatric patients. Since this would be very useful treatment in this population, we especially do not want to ...
13
votes
1
answer
12k
views
Friedman test vs Wilcoxon test
I'm trying to assess performance of a supervised machine learning classification algorithm. The observations fall into nominal classes (2 for the time being, however I'd like to generalize this to ...