Coefficient Of Relationship Puns

Part of my first semester uni project is a graph relating AoA to CL, and I can't find an equation/set of equations relating the two. Any advice at all is welcome :D Thanks

I made an L match network in AWR office for a homework problem, and I was asked to show the bandwidth of the design.

I can plot the S parameters (using a port) on a smith chart and on a rectangular graph. Is it possible to determine bandwidth by looking at the following graph?

https://preview.redd.it/u8p79hgluil11.png?width=1398&format=png&auto=webp&s=56aa87fdd7a8fb02daea25483febf0bad3523477

Link to Statistics.

Base AD Ranked: 8th lowest > 2nd Lowest

AD growth Ranked: 29th (using lowest of Rank of Champions with 3AD) > 5th lowest.

AD Value at lvl18 Ranked: 21st > 3rd.

The last time Sona was 'actually' Buffed was in Patch 8.9, Base attack & power chord damage were increased . Before that, she had mini reworked buffed in 6.14 which was the last time there was a pleasant healthy change. Buffs otherwise from then to now have been buffs into gutted nerfs.

Personally I dont like the intent of the new "buff to Sona support" as it's pretty much a bandage to cover the inconsistency issues of top lane.

For various reasons I need to stratify my models. My fit statistics show that negative binomial is the more appropriate model due to overdispersion. When running the negative binomial the estimate for the non-stratified data is 1.8 but the estimates for the models stratified by gender are males: 1.7 and females: 1.6. This is because (as I understand) each negative binomial regression model has a different dispersion parameter. What would you suggest in this scenario? Relative comparability between multiple different models from the same dataset, with different levels of stratification, is essential.

Dyatlov uses this phrase to emphasize that discussion about the positive void coefficient should be focused on its magnitude, not on it being positive.

>Both in the INSAG experts’ report and in other documents there is mention of the void coefficient of reactivity, whereas what should have been mentioned is the void coefficient of inadmissible magnitude. It turns out that after the accident at the Leningrad power station in 1975, the Scientific and Technical Council of the Ministry took a decision to set this at no more than 0.5%, a fact which the creators of the reactor “successfully managed” to forget. They were quite happy with the calculated curve 1 in Fig II-12 of INSAG-7 Annex II (curve 1 of Figure 3 in this article).

The RBMK reactor: dependence of the reactivity level p on the coolant density y. 1: design calculations; 2: actual dependence at the time when the accident occurred on 26 April 1986; 3: current status after improvements.

>Curve 2 of Figure 3 is shown in INSAG-7 as the actual dependence at the same time of the accident on 26 April. This is a Jesuitical approach – neither a lie nor the truth. A similar void effect existed at all RBMK reactors, and not only on 26 April. The curve was obtained several years prior to the accident by an employee of the Kurchatov Institute, V. Ivanov, and was subsequently confirmed by measurements. The administration did not believe Ivanov. They understood that an explosion threatened, but they did not check this out either by calculation or by experiment. So there you have it. One might ask why Ivanov did not squeal? Only one person squealed, and that was V. I. Volkov, who was quickly disposed of with an invalidity pension.

Page 37 of INSAG-7:

>The Scientific Manager and Chief Design Engineer of the RBMK-1000 reactor determined the dependence of reactor reactivity on coolant density in the core using calculation codes in order to analyse the development of the design basis accident (DBA). The DBA considered in the design was a rupture in the pressure header of the multipass forced circulation circuit (MFCC) resulting in the loss of the water and steam phases of the core coolant. According to the calculated depend

Sorry, I think I meant to say GDP per capita divided by median income.

Ok, so I am currently building a flight computer simulation using the Unity Engine and need to calculate how much lift is applied with my aircraft. Problem is, I have searched everywhere and the only non-lift necessary equation I could find was 2pi*AngleOfAttack, which only works at small angles. If anyone knows of a formula for this that would be very appreciated

Hoping to get some clarification here:

1. So the slope coefficient tells us how much Y changes for a 1 unit change of X, but it doesn't tell us the importance of X in explaing Y. Why is that?

2. The solution is to use a t-test to see if the slope coefficient=0, but we are given a slope coefficent from the regression. What is the purpose of the test then? Is it because we've estimated the slope so we are testing to see how certain we are that the true slope isnt zero?

3. What does a slope of 0 imply? Intuitivley an increase in X has no effect on Y, so X has zero predictive power in Y...

So at Formula North I was curious of what general range teams are sitting at downforce wise, so I asked them. Here's what resulted from that:
https://i.imgur.com/PMgovV2.png

Note that these values came from a mixture of good and bad CFD and validation. Some teams provided suspiciously rough numbers like 350 [lbf] @ 60 [mph], others gave rough numbers off the top of their head, others didn't have an aero guy to represent their aero.

It averages to a value of 3.03, with the lowest being 1.9, and the highest being somewhere in the 5.4-5.9 range.

Hi all, I'm working on a SolidWorks simulation for heat treatment/quenching of steel. I'm all but ready to run my simulation, but am having trouble finding one key value. I am submerging a steel shaft previously heated to 800C into water at 20C, so to simulate this I am running a transient analysis with water as the convection medium. However, I am having trouble finding any reliable information for the convection coefficient of still water at 20C. I'd appreciate any help!

I have put my data as column vectors in my workspace. When I run corrceof(X,Y) I get the output desired. However, I would like to do corrcoef(X,Y,Z...,A) but the command does not allow me to do so. I get error “corrcoef>getparams” and “[alpha, use rows] = getparams(Varangian{:});” To put it succinctly, how do I run a correlation coefficient of more than 2 variables? Thank you.

A block slides up an incline of 50.0 deg above horizontal. The coefficient of kinetic friction is 0.40, and the initial speed is 2.00 m/s. What is the acceleration of the block and distance traveled before coming to rest?

Today in class we studied correlations and looked at r. Teacher said that there is no correlation if r = 0, but even if r = 0.0000000000001, we'd say there is a correlation, albeit an extremely weak one.

This made me wonder if r = 0 actually exists in real life, even if things that absolutely seem like they have no correlation like (apologies for the stupid example) the amount of farts and lung cancer.

Because I'd imagine if the two variables were drawn on a graph, it would have to be symmetric around the x axis like a perfectly reflected parabola or circle, which seems impossible.

Hey guys, I'm trying to do a basic test of whether two coefficients are different in different regressions.

So, let's say I have two regressions:

ivreg2 profit i.rnd c.assets c.cash c.debt i.industry, cluster(companyID year)

ivreg2 profit i.rnd_type1 i.rnd_type2 c.assets c.cash c.debt i.industry, cluster(companyID year)

I want to run a test to see if the coefficient on RND in the first regression statistically smaller than (unequal to) the coefficient of RND_Type1 in the second regression.

How could I do this?

I am trying to create a kind of glove which can catch objects very well by having a lot of friction and then throw them, which would be easier the more similar the coefficient of static friction is to that of human skin. Does this kind of material exist?

Question - https://imgur.com/a/atrkdnr

Doubt is in the picture.

I need help.

I've a question on the fitting of FIR filter coefficients, specifically methods of doing so when the filter has an unequal number of precursor and postcursor taps. I've posted on the DSP Stack Exchange, but no luck so far so I thought I'd chance my arm here. Any help is very much appreciated. :)

I'm trying to fit an FIR filter to the measured frequency response (magnitude, and eventually phase) of a device. The frequency response is essentially a low-pass filter. I'd like to vary the number of taps to see how many are necessary to appropriately match the response, but also give differing number of precursor and postcursor taps in the design.

I've investigated Matlab's fdesign.arbmag and fdesign.arbmagnphase functions, but these seem to only give designs with the same number of pre- and post-cursor taps.

Here's some sample Matlab code with simplified frequency (F) and amplitude (A) values for reference.

F = 0:0.1:1;
A = [1 0.85 0.9 0.7 0.5 0.3 0.35 0.1 0.2 0.1 0];
filterOrder = 7;


d = fdesign.arbmag('N,F,A',filterOrder,F,A);
Hd = design(d);
fvtool(Hd)
tapWeights = [Hd.numerator];
disp(tapWeights)

The above code runs happily, and produces a low-pass filter that matches the frequency response reasonably well, with tap weights as follows: [0.0030 0.0361 0.2161 0.4500 0.2161 0.0361 0.0030].

However, the central tap (the cursor, 0.45) is surrounded by three precursor and three postcursor taps. Is there a way to design, say, with one precursor and six postcursors?

Suggestions for Matlab methods would be brilliant, but even suggestions towards algorithms, etc., would be great too.

In a nutshell, I'm using scipy.signal.butter to generate ba coeffients to set the register values for a Butterworth HP filter; this is a HW filter that accepts ba numbers directly (as fixed point).

I'm trying derive these numbers in C. I've found quite a few examples online of how to generate the ba values. None of the numbers I get ever match what is generated by scipy.signal.butter.

As you can see below, the numbers don't really match at all, though I suspect there's some sort of normalization going on for the scipy example that I'm not accounting for.

scipi:

Code from here

Code

Python3, Scipy:

>>> from scipy import signal
>>> signal.butter(2, .035, 'highpass', 'ba')
(array([ 1.,  0.,  0.]), array([ 1.        ,  0.04949747,  0.001225  ]))

C code converted to Python

#!/usr/bin/env python3
from math import pi, tan, sqrt
def simpleHP(sampleRate, cutoffFreq):

    ff = float(cutoffFreq) / float(sampleRate)

    # Generate low pass
    ita =1.0/ tan(pi*ff)
    q=sqrt(2.0)
    b0 = 1.0 / (1.0 + q*ita + ita*ita)
    b1= 2*b0
    b2= b0
    a1 = 2.0 * (ita*ita - 1.0) * b0
    a2 = -(1.0 - q*ita + ita*ita) * b0

    # Convert to high pass

    b0 = b0*ita*ita
    b1 = -b1*ita*ita
    b2 = b2*ita*ita

    return (b0, b1, b2), (1, a1, a2) # Assumie a0 is 1?

print(simpleHP(16000, 560)

Output:

((0.010432413371093418, 0.020864826742186836, 0.010432413371093418), (1, 1.6909963768874428, -0.7327260303718164))

I know that ridge is equivalent to bayesian linear regression with normal priors (with mu=0) on the coefficients. Would the equivalent to ridge with coefficients constrained to be non-negative -- as you can do, e.g., in sklearn's elasticnet implementation by setting l1_ratio=0 and positive=true -- be equivalent to, say, setting halfnormal priors on the coefficients?

I ask because I'm trying to replicate some good results I got with elasticnet(l1_ratio=0, positive=True), in a bayesian setting implemented in pymc3. I get quite good accuracy in sklearn, but can't get nearly as good performance with my bayesian linear regression. Not sure what the culprit is. I do have about as many observations as independent variables, and many IV's are likely correlated with one another. For bayesian inference, I'm using ADVI rather than MCMC because MCMC is just too damn slow with my dataset.

How do I create a table like the one from the 25 regressions on the picture attached with the coefficients seperated? I have already been running the regressions using both the classic reg p(x) mktrf smb hml (25 times, yes) and *foreach p of varlist p1-p25 { *regress `p' mktrf smb hml *}. In the last case I have not been able to store the results though.

I am able to obtain the coefficients, but I would like to have the coefficients seperated like in the table attached. So if anybody knows exactly how to do this I would be very thankful.

FYI the table is Table I in "Multifactor Explanations of Asset Pricing Anomalies" By Eugene F. Fama and Kenneth R. French.

Sorry, I think I meant to say GDP per capita divided by median income.