MANN | Mode-Adaptive Neural Networks for Quadraped Motion Control | Machine Learning library

I have started to using BeautifulSoup yet.I made a script like this:

I have some data from an experiment to analyse with R but I have a problem and after days of search I can't find a solution.

I need to run multiple permutation t-tests and Mann-Whitney tests on my data grouped for different variables.

For examples, I have to say if there are differences in my response variable (exparat) between treatments (treat) on each experimental day (t).

This is how my dataset looks like:

I have two dataframes as shown below:

I'm fairly new to Power BI, so may be overlooking something basic here.

I have a Column which contains a banding number (1 to 3), depending on a customer's total spend.

Band 1: 0 - 500
Band 2: 501 - 1000
Band 3: 1001+

I have created 3 Parameters on my report (Parameter 1, 2 and 3).
Each parameter allows for a decimal range between 5 and 35 with increments of 2.5
This parameter will represent the percentage of the income that is earned by a consultant.

I'm trying to create a measure that multiplies the Income by the respective Parameter. Allowing users to adjust the percentage earning dynamically when viewing the report.

Below is an example of the table.
Table Name: Receipts

I'm a little lost on this one.
As a calculated columns I would do a simple SWITCH statement, however as it's not a measure it will not adjust dynamically as the parameter slider is changed.

Your help is greatly appreciated.

Kind regards,
Morallis

I usually face problems with dataframes that have many columns.

For example, trying to calculate the Mann-Kendall test:

I have a dataset which includes a column of data (X1_What_site_are_you) on study site. There are 8 factors. I want to create a new column that groups these factors by their habitat type with 4 factors representing woodland sites (Mann's Copse 1, Mann's Copse 2, Nash's Copse 1, Nash's Copse 2) and 4 factors representing field sites (Nash's Field 1, Nash's Field 2, Nursery Field 1, Nursery Field 2).

Here is the data structure:

I am learning R and want to manually compute the Mann-Whitney U statistic and p-value using a normal approximation (and not use wilcox.test or equivalent). My pensioner's brain struggles with coding so it has taken me hours to produce the same answers as the textbook. However, my code to sum the 'StateRank' for the state with the fewest values is convoluted. How can I replace the commented section with more efficient code? I've hunted high and low, both here and on Google, but I don't even know which search terms to use! It won't surprise me to hear that there is a one-line solution but I'm no nearer knowing what it is.

I need to compute weighted Mann Whitney U test results a few hundred times. Each iteration involves is a two-sample test for differences between two groups. I can't figure out how to get the existing function to handle missing values without dynamically deleting cases.

The data for a few of the comparisons are here, in a data frame I call dat. All variables with numbers in this sheet are numeric in type.

Here's how I call the sjstats::mannwhitney() function:

Given the following arrays: