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I am undertaking stepwise linear regression to predict missing values. I can do this on a variable by variable basis, but I have a very large data frame with over 50 variables and need to find away to automated the process of getting the fitted values for multiple variables. I am aware that many statisticians do not like stepwise procedures, but I would still like to implement them.

Below is the code I am using to do this on a variable by variable basis:

I have have been performing stepwise linear regression (direction = "both") in r. I know how to do this on a variable by variable basis, and I also know how to run linear regression on multiple variables at once. I was wondering if there is a way to loop through this process. I am aware that many statisticians do not like stepwise procedures, but I would still like to implement them.

To run multiple regression models the following code is used:

I have a set of user data that can be filtered by 3 select dropdowns which filter by location, department, and client.

I am running into some edge case issues where if multiple dropdowns have been selected and I make a change to one that is not set to "All" the users are not properly filtered.

Here is a sample set of user data that is set to the "users" state:

I state that I do not have much experience, I am a statistician. I am creating a small App and I would like to copy the data entered in a form in the main dashboard of the site. Dashboard and form are two separate components. I can only copy the form data within itself but I cannot export the data to the dashboard.

What I want to achieve is that the logged in user can post a personal ad in the public dashboard and that it remains saved and visible to all users.

Our assignment is to have a class which takes in integer values one at a time. With those integer values I am supposed to do a multitude of different things. Add them, take the average, find the largest and smallest value etc. I can write the functions for average and largest value and whatever but it is the very beginning that I'm struggling with.

How do I get the user inputs into my class? I have a member function and constructor. The function "next" is what I'll be using to store and pull my numbers from:

I have a pandas dataframe of 229 rows. Each row represents a "strain". The data comes from 4 sites. The strains are encoded with the site codes.

Once upon a time, this data was clustered and

The 229 strains examined formed a large group at the S (similarity) 231% level, using the Jaccard similarity coefficient and unweighted average linkage. Within this group, 10 clusters, or phena, were distinguished at varying levels of similarity above 65%. Twenty-one strains did not fall into any one of these phenetic groups. No cluster with less than five members was considered further.

Disclaimer: I am not a statistician; I know essentially nothing about statistics past mean and median. Way back then I had a statistician to work with. I also know next to nothing about Machine Learning algorithms although I know what clustering means from a general point of view.

I want to try to reproduce the clustering with more modern methods. I thought I'd try k-means (if that's a bad choice, please enlighten me).

The data is Boolean. I have transposed it so that each column is a "strain" and the rows are the features. (Was that right?)

The code:

I've found a few answers similar to what I'm looking for, but the code has not worked.

I need to replace all outliers (as defined for our purposes as more than 2 SD away from the mean), of each column with NA or NULL. I'm trying to have this iterate across all columns, calculating the outliers for each column and replacing those values.

Disclosure: I also know statistically there are strong views on the removal of outliers, and the use of SD as a measure to exclude them given that SD is calculated including the outliers in this case. These were my instructions from our statistician, so I am working off that for now.

This is a glimpse of my dataset:

Instead of just substituting NAs with the mean value, I need to substitute the NA with the minimum value in the column, divided by 2. (Our statistician asked me to perform this on our data - I am not a statistician so following their instructions)

This is glimpse of my data:

Hello statisticians and data enthusiasts !!

I am working on a dataset to apply linear discriminant analysis on it. And i'm having trouble to finding out columns with good enough normal distributions score of shapiro.test, because all the p-values are up-to the mark of 0.05.

Below is the shapiro.test results on my Data