Plant has 2 levels, A and B, for lack of a better descriptor in the question. You have 2 kinds of orders, Falsely executed and Correctly executed. Thus, you have a 2 x 2 table of Plant by Order type, with the number of observations that match each cell. That could be analyzed with a Chi-Square test of independence to determine if order execution is independent of the plant.
A statistical hypothesis test will usually be performed by inductively comparing results of experiments or observations. The number or amount of comparisons will generally dictate the statistical test to use. The researcher is basically making a statement and assuming that it is either correct (the hypothesis - H1) or assuming that it is incorrect (the null hypothesis - H0) and testing that assumption within a predetermined significance level - the alpha.
Kruskal-Wallis H test.
You can use the z test for two proportions. The link below will do this test for you.
The chi-square test is appropriate to use in statistical analysis when you want to determine if there is a significant association between two categorical variables.
statistical goodness of fit test used for categorical data to test if a sample of data came from a population with a specific distribution. It can be applied for discrete distributions.
It can be, but it is also a statistical distribution in its own right - on which the test is based.
The power of a statistical test is the probability that the test will reject the null hypothesis when it is, in fact, false. Please see the link.
You know nothing about how to use statistical analysis to verify or test validity, do u.
* Always when the assumptions for the specific test (as there are many parametric tests) are fulfilled. * When you want to say something about a statistical parameter.
Yes. It is a statistical test.
One way to test for heteroskedasticity in a statistical analysis is to use the Breusch-Pagan test or the White test. These tests examine the relationship between the error terms and the independent variables in a regression model to determine if the variance of the errors is constant. If the test results show that the variance is not constant, it indicates the presence of heteroskedasticity.
Parametric statistical tests assume that your data are normally distributed (follow a classic bell-shaped curve). An example of a parametric statistical test is the Student's t-test.Non-parametric tests make no such assumption. An example of a non-parametric statistical test is the Sign Test.