F Statistics Puns

I’m new to using R and I ran my first linear model, which I needed help coding. I ran the model and it all worked fine, but now I need to report the results of the linear model. I read that a sort of template for reporting the results is:

R^2, F(df regression, df residual) = [F value], p value

I ran the code

lm=lm(VD~FPFS, data) Summary(lm)

And got the output

Residual standard error: 0.02344 on 389 degrees of freedom Multiple r squared: 0.09371, adjusted r squared: 0.07274 F statistic: 4.469 on 9 and 389 DF, p value: 1.35e-05

I don’t know what in R corresponds to which parts of the F statistic template I found. Which numbers do I plug in where to report my results?

I need to look for the link to the stats that the official MBTI has been collecting over the last two decades to study the likelihood of each function within the population, so you can see the exact numbers. Will post when I find it. Frank James discussed it in a video.

Anyhow, as far as each letter goes, these are the statistics within the world population as of 2020.

I/E - extroversion is slightly rarer despite society’s preference for extroverted qualities. 61.2% of people are Introverts and 38.8% of people are Extroverts.

N/S - iNtuition is rarer than sensing. 68.5% prefer Sensing and 31.5% of people prefer iNtuition.

F/T - feeling is rarer than thinking. 47.9% prefer the Feeling function whereas 52.2% prefer the Thinking function.

P/J - perceiving is the rarer function to have. 52.5% use the Judging function and 47.5% use the Perceiving function.

Less people use:

• Extroversion
• iNtuition
• Feeling
• Perceiving

However, the statistics of self reported ENFPs lands ENFP somewhere in the middle of the 16 personalities as to how many there are compared to other types. While ISTJ was either the most dominant type within the population or the second most dominant type behind ESTJ (can’t quite remember which of the two it was).

Are people mistyping and over reporting as ENFP and skewing the population statistics? Or is there something that I don’t understand about how statistics work???

How could the least likely combination not be the rarest type?

I’ve been assessed by a professional official MBTI analyst as ENFP and have always gotten that result on self testing, as well.

I don’t feel like I meet other people like me often at all. Do other ENFPs feel this way? That is, if you truly ARE ENFPs!!! Lol.

I personally think there are a lot of ESFJ and ESFPs who think they’re ENFPs.

Frank James video discussing these new statistics:

https://youtu.be/PFPeGSdbpVE

Minute 3:24 for statistics on preferences.

Hi,

can someone kindly share Gravetter, F.J., Wallnau, L.B., Forzano, L.B. & Witnauer, J. E. (2021) Essentials of statistics for the behavioral sciences (10th ed.) with me? Please make sure it's the CANADIAN edition.

thanks

I'm looking at the relationship between two variables, and this is the standard. I have my F value, but what value do I compare it with to see if F is 4x that value?

X-Post: https://stats.stackexchange.com/questions/555939/can-we-report-an-f-statistic-on-a-robust-linear-model-with-only-fixed-effects?noredirect=1#comment1022408_555939

Hey all, I have a theoretical question/concern for you.

I'm estimating a fixed effects model using Robust Linear Regression with the Huber weighting function (MASS::rlm). I included a boxplot of the data. I'd like to report an F-statistic and p-value for the entire model as is included in the output of OLS regression, but my concern is, SHOULD I be reporting an F-statistic?

Is an F-statistic valid for Iterative Reweighted estimates? What are our thoughts?

Here is the huber weighted model and the OLS model:

Call: rlm(formula = change ~ test, data = data, 
    psi = "psi.huber")
Residuals:
     Min       1Q   Median       3Q      Max 
-2.09417 -0.18375 -0.09417  0.25552  3.90583 

Coefficients:
                        Value   Std. Error t value
(Intercept)             -0.0561  0.0937    -0.5986
testa_total              0.2828  0.1325     2.1336
testb_total              0.2398  0.1325     1.8097
testc_total              0.1503  0.1325     1.1338

Residual standard error: 0.28 on 72 degrees of freedom

vs. OLS

Call:
lm(formula = change ~ test, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3684 -0.3684 -0.1461  0.2566  3.6316 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)             -0.06053    0.17494  -0.346   0.7304  
testa_total              0.31316    0.24741   1.266   0.2097  
testb_total              0.23421    0.24741   0.947   0.3470  
testc_total              0.42895    0.24741   1.734   0.0872 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7626 on 72 degrees of freedom
Multiple R-squared:  0.04284,   Adjusted R-squared:  0.002955 
F-statistic: 1.074 on 3 and 72 DF,  p-value: 0.3656

Standard errors are corrected later to account for clustering and heteroskedasticity.

https://preview.redd.it/ry1mhio4sd581.png?width=1054&format=png&auto=webp&s=2d560b8f382e1fb71213bc9c8681c8a7d85fb78e

I'm a beginner in data science and I am having some trouble with the APA formatting of the F-statistic. I understand the statistic. I just don't understand where the values in the bracket are coming from.

For example (F3,196)=570.27. I can see where on the regression table the 570.27 is, but I can see no corresponding values for 3 or 196?

I understand they mean degrees of freedom. I just don't see where the values are coming from.

Hey guys,

I am working on forecasting a time series using two separate models to produce one step ahead forecasts using R. The original time series has 150 observations {y} and the 50 one step ahead forecasts produced from each model are {f1} and {f2}. My textbook says that I can asses the forecasts using the regression y(100+i) = a0 + a1f1(i) + v1(i) and y(100+i) = b0 + b1f2(i) + v2(i). It says if the forecasts an unbiased an F-Test on each regression should allow me to impose the restrictions a0 = 0 and a1 = 1 (and b0 = 0 and b1 = 1).

Up until this point I've only ever used F tests as a joint test that multiple coefficients in a regression are zero. Intuitively I understand the idea of creating a restricted model to impose the null hypothesis on each regression. However, I am not sure how I would input this into R.

I am using the function linearHypothesis() from the car library, however each tutorial I find for this function only explains how to use it for joint hypothesis that the coefficients equal zero, so I am not sure how to change that null hypothesis. Any help on this would be appreciated!

Thanks!

Like, do you divide the hits by the number of days your fic has been posted to get the average number of hits per day?

Or do you do some math to figure out the hits to kudos ratio? Or hits to comments percentage? Or anything that's pretty much masochistic mindfuckery in nature? Like it's not GOOD ENOUGH for you to see this big X number of total hits. You gotta mess it all up by figuring out well what is that PER WEEK or DAY??? I have to know!!!

Like you're just going along getting hits and kudos and bookmarks and comments and thinking, "o i am such a fanfiction writing BAD ASS! but lemme just completely wipe that from my brain" and then you do some math only to find out that like 1.6% of the people that read your fic leave a kudos, so then you're like "oh :( it kinda sucks then :( like i always thought :("

Anybody else do this?

Yeah, me neither.

Let's start with this - I am male. I have talked to number of people about rape culture, and a very common argument I hear when a woman brings up topic of feeling unsafe (so far only from man) is: "yeah, but rape on man happen too and they don't get so much recognition, and are often marginalised". Which I 100% agree with. But every single man that I have talked to, and has said this statement didn't do a thing to change the situation, just used it as a defense mechanism, and started being passive aggressive, and I hate it. Did any of You feel the same about it?

https://preview.redd.it/ongmcx7c9ji61.png?width=837&format=png&auto=webp&s=060e2cc7f845f0940dabc14df2a079a5ac404590

I would try to explain the context, but a picture is probably better.

What is this mysterious value F(x; mu) and how is it different from p(x; mu)? Never once in any of this presentation does she show her work for how she got F(x; mu) or even give it a name. What is this value and how am I supposed to use it?

Here's an example problem that can apparently be obtained two ways, one with p() and one with F(). What is she doing here?

I remember being taught that this is the case but I don't understand the reasoning behind it. Does anyone have input on this?

Does it matter for a hypothesis based on F-statistic that which sample I am considering as numerator and which one I consider as denominator? Whether this would impact the hypothesis conclusion? Or there is a convention followed in this.

I am doing a problem where it gets regression results and it says the f statistic is 157.699 but then right next to it is a box that says Significance of F and that is 0. what is that second thing

Hi,

can someone kindly share with me Gravetter, F.J., Wallnau, L.B., Forzano, L.B. & Witnauer, J. E. (2021) Essentials of statistics for the behavioral sciences (10th ed.) .

please make sure it's the CANADIAN edition

thanks