I just want to understand the formula and not memorize it just to solve questions.
Based on an actual, fun debate I had last week:
Recall standard deviation is a measure of dispersion. Is the standard error (SE) truly a standard deviation? (There is only one correct answer and three false choices)
(same LI poll here https://www.linkedin.com/posts/bionicturtle_frm-activity-6887464252769206272-pCMY)
I kind of understand each one from a technical perspective but I still can't understand when is one better than the other or what are the main differences between each technique or why both are needed besides to solve the problem of constant variance violation.
Would someone clarify the concept with examples or point out a source to understand the difference between both and their applications?
I see that with logistic regression that the standard error can be computed as in How to compute the standard errors of a logistic regressions coefficients which amounted to taking
`[;\sqrt{(X^TVX)^{-1}};]` where V is a diagonal matrix where the diagonal entries was probability of being in class A, `[;\pi_a * (1-\pi_a);]`
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Looking at the same for linear regression (based on my understanding of Standard errors for multiple regression coefficients? ) we can compute the standard error of the coefficients by
`[;\sqrt{\sigma^2(X^TVX)^{-1}};]`
where s is the variance of the residuals (as per my understanding of
___
From the above I have 2 questions:
- It seems like from the above we are using more or less the same form (square root of the inverse of something). Am I on to something? How do we determine that "something"? In logistic regression it was V, a diagonal matrix, and in linear regression it is the variance of the residuals). It seems like we're encompassing a notion of "how wrong" our prediction is compared to some label.
- How might I derive the same for a Poisson regression?
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Normally I'd just use R or statsmodels, but I'm building a custom library for encrypted ML/stats and I need to build all of this from scratch
Q:“why not just run termagants?”
A: I have a strict “no humanoids” rule for my fluffy nids list, so no boneswords/lashwhips or weapons held like rifles. its just a personal preference and i’d rather put in the work to keep that standard within my list. Also, i only have 2nd hand hormagants and devourer bits to work with
Just the title. How do you know what standard errors to choose in a regression model? If there’s any helpful resources for learning this that someone could point me to that would be great. Thanks.
I want to create a script that will list in long form the contents of a directory. However, I want to make sure first that the user does not add input to the script command line until prompted if so I need to apply an error and exit. If it passes that test, then the script should then ensure the directory exists before attempting to list it. If it doesn't then another error out. If it exists I need to display the contents in long listing.
I'm really new at this and here is what I have so far. I'm not sure that I'm even on the right track. Any help would be appreciated.
#!/bin/bash
# File Name: dirlist
# Usage: dirlist prompt: [/directory/path/here]
# Synopsis: The dlist script will prompt the user to input a directory path and will
# return a long listing of the contents in that directory.
# Author: Romero
# Notify user of incoming prompt
echo -e "You will be prompted to enter the name of a directory you want to list in long form. "
# Prompt the user to input the directory to list on the same line as the input prompt
read -p "Directory to list: "
# Ensure User has not input directly into command line; if so issue std error 1 and exit
# Check to ensure directory exists before listing; if not issue std error 2 and exit
# If directory exists, list it in long form
if [ ! -d "$DIRECTORY" ]; then
echo "This directory does not exist to list!" 1>&2
exit 2
elif [ -d "$DIRECTORY" ]; then
echo ls -l "$DIRECTORY"
fi
Why does multicollinearity necessarily increase standard errors? I do not understand the intuition. I do know that the independent variables move in unison when there is multicollinearity, but I cannot figure out the increase in SE.
This is more of a statistics question but I'm having trouble understanding the real-life usage of Standard Error. I understand that it is inversely affected by the sample size and that the closer the sample size is to the population size, the SE decreases. I guess I'm confused by 2 different aspects.
1. If you already know population data, why use a sample and have SE at all?
2. If you are working with a population of data, why is SE even a factor?
for example, in R, when using the MTCARS dataset, the following lines of code:
## Load GGPLOT2
require(ggplot2)
## Plot MTcars, wt against mpg
ggplot(mtcars, aes(wt, mpg)) +
## add scatterplot
geom_point() +
## add SE and trend
geom_smooth()
will display ubiquitous mtcars dataset, with the wt variable on the X axis and the mpg variable on the Y axis. The geom_smooth function includes a default value of SE=TRUE (display standard error) and shows the standard error in a gray fill alongside the trendline.
Im a bit confused about this, though.
-
The MTCars dataset in its entirety is plotted so its the population, right? So how can there be a standard error if the data is the population?
-
If for some reason this is not considered the population, then how would the standard error be calculated since the population must be known?
Sorry for the confusion but I am just a bit confused about this concept being applied to real life scenarios. Any and all insight would be greatly appreciated.
Thank you.
Greetings everyone.
Many web resources such as this one (https://davidmlane.com/hyperstat/A13660.html) state that "The standard deviation of the sampling distribution of the mean is called the standard error of the mean". Therefore I have a question - Why don't we use ordinary sample standard deviation formula and instead use standard error formula.
Thanks in advance for your answers.
I have to regularly clean data sets at work where the descriptions of what each line are are often convoluted with data that should be in other rows such as order number, and date, in addition to what I need. I currently use a chain of 24 formulas like =IF(COUNTIF(U3,"*ups*"),"ups",U3), each with its own column to correct these errors. Extending this chain of cells to the bottom seams to take up a lot of time, and always overshoots the last row. I am looking to make this faster to process. The number of rows is different each time I use this template.
I can't seem to join the Standard 2022 or Standard 2022 Ranked queues whatsoever. It immediately comes up with Network Error. If I change to Play, or anything else, it works fine.
Anyone have similar or have any suggestions how to resolve it?
Unable to join standard 2022 queue both play and ranked, however 2021 standard and play queue both work fine. This is happening after the scheduled maintenance that was completed today. Not sure where to submit the log file.
I preordered the game Sunday evening (using PayPal) so I could preload the game but when I went to download it I only see Back4Blood Beta in my purchased games.
I never played the Beta during the August window.
When I go onto the PS Store I see the Standard Edition but it won’t allow me the ability to download it from there.
Finally I downloaded the beta as maybe it would force the update to Standard Edition but no luck with that either.
Just checking here to see if anyone had a similar issue.
The only material I had on the avatar was this one, and it already has SDK compatible material, and I imported some models that had incompatible materials, but I've already changed them, it's the first time that happens, what should I do?
https://preview.redd.it/uejv9hrddyz71.png?width=521&format=png&auto=webp&s=5c0345eb649c037a513e4d8996b565beee32e5be
https://preview.redd.it/3i4uc4zddyz71.png?width=506&format=png&auto=webp&s=49aeefd4bd722c6d579d6e61b8a1583ad5aaff47
https://preview.redd.it/q1zdx6u2dyz71.png?width=513&format=png&auto=webp&s=4935a1dd4ab3589b733091fc5c97f938663888e9
I am given a task to find the minimizing the relation between sum-of-squares error averaged over the noisy data and minimizing the standard sum-of-squares error with an L2 weight-decay regularization of the term, in which the bias parameter w0 is omitted from the regularizing, but the problem is I cannot find the difference between the sum-of-squares error and the standard sum-of-squares error.
