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# Pygmy/LaMancha Cross?

Discussion in 'Goats' started by Rechellef, Jul 17, 2011.

1. ### RechellefShow us your teats!!

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Location:
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I know this sounds like a silly question, but some friends of our raise Pygmys and they are interested in breeding one of their does because they are interested in possibly the dairy end of the goat world. My buckling's momma is a heavy milker, so the genese are there. Would this cross result in a potential dairy goat? Would it be the same as a mini-mancha or does it have to be a ND crossed with a LaMancha to produce a mini-dairy breeed?

Last edited: Jul 17, 2011
2. ### Alice In TX/MOMore dharma, less drama.Supporter

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Texas Coastal Bend/S. Missouri
Mini Manchas are made with a LaMancha doe and a Nigerian Dwarf buck.

You should NOT breed a Pygmy doe with a full size buck. The resulting kids could be too big to pass through the pelvis of the doe at birth.

Pygmies are not dairy animals.

3. ### RechellefShow us your teats!!

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I didn't think so, but I am fairly new to this, so I always check on things like this to make sure.

4. ### grandmajoWell-Known Member

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If your friends are interested in a more dairy type of animal, tell them to look for pygmies that are milking, they are out there. A pygmy, if it comes from the right lines, can produce a respectable amount of milk for their size and it's extremely rich.

On a side note, there have been kinder breeders who have started out by breeding a Nubian buck to a pygmy doe. However, the doe is usually from the old style line, which is generally bigger than the pygmies that most people are breeding today.

5. ### goatkidWell-Known MemberSupporter

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I once had a doeling that was out of a Pygmy doe and a La Mancha buck. She was a sweet little thing, but infortunately, I nver got to see how she'd do as a milker. Our neighbor's dog killed her. Though her dam had no problems delivering her, I would not suggest breeding a Pygmy doe to a full size buck. Instead, sell her a La Mancha doeling to breed to one of her bucks.

6. ### LFRJWell-Known MemberSupporter

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Isn't that a pygmy BUCK over a Nubian DOE? The result would probably be similar, I'd imagine, depending on the parents, and I'm not sure, but I thought it was originally the other way around sire/dam wise.

7. ### chewieWell-Known MemberSupporter

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I am agreeing with LFRJ, i am perty sure it was the pygmy buck used. I would be concerned that the other way around would be hard on the doe as others have pointed out.

8. ### grandmajoWell-Known Member

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That is the way that the KGBA recommends that it be done, but if you look on their website they state that it has been done this way. They don't however recommend it. From their website:

"There are a few breeders who have begun successfully with a Pygmy doe and a Nubian buck. The Association feels, however, that it is a safer procedure to use the larger doe with the smaller buck."


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# question about continuous function.

• Jun 23rd 2011, 01:14 PM
taypen
question about continuous function.
hello,

i got continuous function on $[0,1]$ such that $f(0) = f(1)$ i need to prove that exist "a" such that $f(a) = f(a+0.5)$
$a\in \left[0,\frac{1}{2}\right]$

your help would be much appreciated!
• Jun 23rd 2011, 03:20 PM
emakarov
Re: question about continuous function.
The idea is described in the Wikipedia article about the intermediate value theorem.
• Jun 24th 2011, 12:00 AM
ojones
Re: question about continuous function.
Emakarov is correct. Look at the example at the bottom of the article concerning real world applications. Your problem is just a slight variation: consider the function $g(t)=f(t)-f(t+0.5)$.
• Jun 24th 2011, 06:24 AM
taypen
Thanks
I knew it was something around the intermediate value theory
After reading all the article i found simmilar problem and it's solution.
So thanks for your help.

format: plain


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# How to ensure that the «if» statement gets parsed _before_ a given resource/class?

Sometimes there are complicated interactions between packages. To avoid class interface with countless, seemingly unrelated parameters, I though about using tags, defined(<resource>) function to let the resources slightly change their behaviour based on the rest of the configuration (not facts).

Unfortunately I can't figure out a way to let the Puppet first parse the class that can generate conditions, and only then parse the class which behaviour implicitly (via e.g. tags) depends on the way the first class got executed.

Suppose here is a class, that manages a place, where a specific user stores his sensitive personal data. The class can e.g. create the partition (possibly encrypted), or just maintain an external folder (on e.g. /mnt/user). Or it can keep the folders where they are by default, i.e. in the /home/user (spurcetype=>'none')

class userdocs ( $sourcetype) { case$sourcetype {
'cryptpartition' : {
tag 'dedicated_user_docs_partition'
...
}
'partition' : {
tag 'dedicated_user_docs_partition'
...
}
'none' : {
...
}
}
...
}

Apart from fairly standard set of folders, that a user might want to forward to his partition, there are folders which existence depends on the particular software running, like thunderbird or virtualbox. Creation of the link (instead of folder) for e.g. virtualbox should be done only, if two conditions are satisfied: a) user manages separate partition (tagged('dedicated_user_docs_partition')) and user installs VirtualBox.

There must be a place, where I can put that extra code, that checks for those two conditions. I can put it into the class, that manages the virtualbox:

class vbox {
if tagged('dedicated_user_docs_partition') {
file { "${userhome}/.VirtualBox": ensure=>link, require=>Class['userdocs'] ... } } else { file { "${userhome}/.VirtualBox": ensure=>directory, require=>Class['userdocs'] ...
}
}
...
}

The problem is, that although I explicitly require the file resource to be executed before the userdocs class, the file resource is evaluated after that, and puppet wrongly assumes, that user doesn't have the dedicated_user_docs_partition and executes wrong branch of the if statement.

Is there any way to solve this problem?

Adding class {'vbox': require => Class['userdoc'], } in the node declaration does not help. This syntax changes resource-dependency, not the order in which the parser works.

One person have suggested to use custom facts instead of a variable, but I think this is rather last resort idea. Facts are instantiated only once, at the beginning. I would need to run a manifest twice (which is not elegant), and handle three states in place of two: "dedicateddocs", "nodedicateddocs", "firstrun". Besides in real situation (not this mock-up example), both vbox and userdocs are in fact a define, not class, and they have user name as a namevar. I understand, that I would need to have as many facts as there are users, which would greatly proliferate the namespace

Another idea is to use stages. But then I would need awfully lot of stages, if I'd start to handle resource interactions that way.

This is cross-post from Server Fault

edit retag close merge delete

Sort by » oldest newest most voted

Assuming you are managing the software with puppet as well (which appears to be the case from your sample code), I would use virtual resources in the software modules that get collected by the module handling the mounts.

http://docs.puppetlabs.com/puppet/lat...

more

I guess this is the way to go. I've never used the Virtual Resources before.

( 2014-04-24 06:57:48 -0600 )edit

I've copied your answer on http://serverfault.com/questions/590762/how-to-ensure-that-the-if-statement-gets-parsed-before-a-given-resource-clas/591053#591053 . If you want you can post this answer from yourself and I'll accept it.

( 2014-04-24 07:02:43 -0600 )edit

Do not try to build anything that is evaluation order dependent. There is only pain down this road, because in a complex manifest, it is nigh impossible to predict the order in which the parser will be walking your definitions.

Point in fact, we are working on deprecating and removing the defined function altogether to keep users from shooting their feet.

I shall elaborate over on Server Fault when I get a chance.

more

Thank you. I'm open to suggestions.

( 2014-04-23 15:19:30 -0600 )edit

## Stats

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Last updated: Apr 23 '14


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LaTeX printing in ipython qtconsole when running external file

I solved an analytic problem using sympy and saved everything in a .py file. When I run the code (which includes init_printing()) with ipython filename.py I get nice unicode output whenever I print within the file with pprint. So far so good.

When I import sympy in ipython qtconsole, I can get nice LaTeX outputs, just as stated in the documentation of sympy. But I get this nice printing only if the interactive console does the prining, i.e.:

Integral(sqrt(1/x), x)

produced a LaTeX image, while

pprint(Integral(sqrt(1/x), x))

does produce unicode output.

When running code from a file with

run filename.py

the only way I see to create output is to use pprint, i.e. I do not get the LaTeX output.

Does anyone see a solution? Thanks alot.

-

As in many case I think there is a confusion between returning an object (that trigger display hook) and displaying it. is is the same difference than

def funp():
print 1

and

def funr():
return 1

Both will "show" 1 if executed interactivly, but not in a script. In IPython you can see the difference with the Out[] prompt that appear or not depending of wether it is returned or displayed. I think in you case you need from IPython.display import display_pretty

In[1]: display_pretty(I)
⌠
⎮     ___
⎮    ╱ 1
⎮   ╱  ─  dx
⎮ ╲╱   x
⌡

or maybe from IPython.display import display_latex

-
Thanks for the answer! –  user3090163 Dec 11 '13 at 11:01
Thanks for the answer! This means that the display hook method that is called in the qtconsole is able to do the LaTeX output, but I use a different method (pprint) in the script. How can I manually call the display hook method. Btw. alpha prints LaTeX display_latex(alpha) prints unicode –  user3090163 Dec 11 '13 at 11:12
Simply use in the script display: IPython.display import display display(alpha) #or similar –  user3090163 Dec 11 '13 at 12:27
display_something will call the display-hook-something for you. It is not always easy nor make sense to try to get the methods that the display_hook will call by yourself. –  Matt Dec 11 '13 at 14:31


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Vectorised function to calculate US Kidney Donor Risk Index as published by UNOS. Please note that this function uses creatinine measured in µmol/l by default, but can be changed to mg/dl if the optional units parameter is set to "US".

uskdri(age, height, weight, eth, htn, dm, cva, creat, hcv, dcd,
scaling = 1, units = "SI")

Arguments

age numeric vector of donor ages in years numeric vector of donor heights in cm numeric vector of donor weights in kg character string vector of donor ethnicity, "black" or "non-black" numeric vector of donor hypertension history (1 = yes, 0 = no) numeric vector of donor diabetes history (1 = yes, 0 = no) numeric vector of whether donor death due to CVA (1 = yes, 0 = no) numeric vector of donor serum creatinine (µmol/l) numeric vector of donor hepatitis C history (1 = yes, 0 = no) numeric vector of type of donor (1 = DCD, 0 = DBD) single numeric value for OPTN scaling factor (optional, defaults to 1) single string value to indicate creatinine units ("SI" for µmol/l, "US" for mg/dl)

Value

numeric vector of US KDRI values

Details

The KDRI is normalised by a scaling factor based on the median KDRI in the previous year. For 2018, this was approximately 1.250609 as is published on the OPTN website. The scaling parameter in this function defaults to 1, so can be left out to calculate the KDRI without scaling.

Reference: Rao PS, Schaubel DE, Guidinger MK, et al. A Comprehensive Risk Quantification Score for Deceased Donor Kidneys: The Kidney Donor Risk Index. Transplantation 2009; 88:231-236.

Examples

# with creatinine in µmol/l (units = "SI" can be omitted)
uskdri(age = 40, height = 170, weight = 80, eth = "non-black", htn = 0, dm = 0,
cva = 0, creat = 120, hcv = 0, dcd = 0, scaling = 1.250609, units = "SI")#> [1] 0.8650326
# with creatinine in mg/dl
uskdri(age = 40, height = 170, weight = 80, eth = "non-black", htn = 0, dm = 0,
cva = 0, creat = 1.4, hcv = 0, dcd = 0, scaling = 1.250609, units = "US")#> [1] 0.8731651


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# 4.6: Practical Halogenations and Problems of Selectivity

Given the knowledge that a particular reaction will proceed at a suitable rate, a host of practical considerations are necessary for satisfactory operation. These considerations include interference by possible side reactions that give products other than those desired, the ease of separation of the desired product from the reaction mixture, and costs of materials, apparatus, and labor. We shall consider these problems in connection with the important synthetic reactions discussed in this book.

The chlorination of saturated hydrocarbons can be induced by light, but also can be carried out at temperatures of about $$300^\text{o}$$ in the dark. Under such circumstances the mechanism is similar to that of light-induced chlorination, except that the chlorine atoms are formed by thermal dissociation of chlorine molecules. Solid carbon surfaces catalyze thermal chlorination, possibly by aiding in the cleavage of the chlorine molecules.

Direct monohalogenation of saturated hydrocarbons works satisfactorily only with chlorine and bromine. For the general reaction

the calculated $$\Delta H^\text{0}$$ value is negative and very large for fluorine, negative and moderate for chlorine and bromine, and positive for iodine (see Table 4-7). With fluorine, the reaction evolves so much heat that it may be difficult to control, and products from cleavage of carbon-carbon as well as of carbon-hydrogen bonds may be obtained. The only successful, direct fluorination procedure for hydrocarbons involves diffusion of minute amounts of fluorine mixed with helium into liquid or solid hydrocarbons at low temperatures, typically $$-78^\text{o}$$ (Dry Ice temperature). As fluorination proceeds, the concentration of fluorine can be increased. The process is best suited for preparation of completely fluorinated compounds, and it has been possible to obtain in this way amounts of $$\left( CF_3 \right)_4C$$ and $$\left( CF_3 \right)_3 C-C \left( CF_3 \right)_3$$ from 2,2-dimethylpropane and 2,2,3,3-tetramethylbutane corresponding to $$10$$-$$15\%$$ yields based on the fluorine used.

Bromine generally is much less reactive toward hydrocarbons than chlorine is, both at high temperatures and with activation by light. Nonetheless, it usually is possible to brominate saturated hydrocarbons successfully. Iodine is unreactive.

Table 4-7: Calculated Heat of Reaction for Halogenation fo Hydrocarbons
Halogen (X) $$\Delta H^o$$ (kcal/mole)a
F -116
Cl -27
Br -10
I 13
aCalculated from the bond energies of Table 4-3.

The chlorination of methane does not have to stop with the formation of chloromethane (methyl chloride). It is usual when chlorinating methane to obtain some of the higher chlorination products: dichloromethane (methylene chloride), trichloromethane (chloroform), and tetrachloromethane (carbon tetrachloride):

In practice, one can control the degree of substitution to a considerable extent by controlling the methane-chlorine ratio. For example, for monochlorination to predominate, a high methane-chlorine ratio is necessary such that the chlorine atoms react with $$CH_4$$ and not with $$CH_3Cl$$.

## Selectivity in Alkane Halogenation

For propane and higher hydrocarbons for which more than one monosubstitution product is generally possible, difficult separation problems bay arise when a particular product is desired. For example, the chlorination of 2-methylbutane $$3$$ at $$300^\text{o}$$ gives all four possible monosubstitution products, $$4$$, $$5$$, $$6$$, and $$7$$:

On a purely statistical basis, we may expect the ratio of products from $$3$$ to correlate with the number of available hydrogens at the various positions of substitution. That is, $$4$$, $$5$$, $$6$$, and $$7$$ would be formed in the ratio 6:3:2:1 ($$50\%$$:$$25\%$$:$$17\%$$:$$8\%$$). However, as can be seen from Table 4-6, the strengths of hydrogen bonds to primary, secondary, and tertiary carbons are not the same and, from the argument given in Section 4-4E we would expect the weaker $$C-H$$ bonds to be preferentially attacked by $$Cl \cdot$$. The proportion of $$7$$ formed is about three times that expected on a statistical basis which is in accord with our expectation that the tertiary $$C-H$$ bond of 2-methylbutane should be the weakest of the $$C-H$$ bonds. (See Table 4-6.)

The factors governing selectivity in halogenation of alkanes follow:

1. The rates at which the various $$C-H$$ bonds of 2-methylbutane are broken by attack of chlorine atoms approach 1:1:1 as the temperature is raised above $$300^\text{o}$$. At higher temperatures both chlorine atoms and hydrocarbons become more reactive because of increases in their thermal energies. Ultimately, temperatures are attained where a chlorine atom essentially removes the first hydrogen with which it collides regardless of position on the hydrocarbon chain. In such circumstances, the composition of monochlorination products will correspond to that expected from simple statistics.

2. Bromine atoms are far more selective than chlorine atoms. This is not unexpected because is endothermic, whereas corresponding reactions with a chlorine atoms usually are exothermic (data from Table 4-6). Bromine removes only those hydrogens that are relatively weakly bonded to a carbon atom. As predicted, attack of $$Br \cdot$$ on 2-methylbutane leads mostly to 2-bromo-2-methylbutane, some secondary bromide, and essentially no primary bromides:

3. The selectivity of chlorination reactions carried on in solution is increased markedly in the presence of benzene or alkyl-substituted benzenes because benzene and other arenes form loose complexes with chlorine atoms. This substantially cuts down chlorine-atom reactivity, thereby making the chlorine atoms behave more like bromine atoms.

## Chemical Initiation of Radical-Chain Substitution

It is possible to achieve chlorination of alkanes using sulfuryl chloride ($$SO_2Cl_2$$, bp $$69^\text{o}$$) in place of chlorine:

The reaction has a radical-chain mechanism and the chains can be initiated by light or by chemicals, usually peroxides, $$ROOR$$. Chemical initiation requires an initiator with a weak bond that dissociates at temperatures between $$40$$-$$80^\text{o}$$. Peroxides are good examples. The $$O-O$$ bond is very weak ($$30$$-$$50 \: \text{kcal}$$) and on heating dissociates to alkoxyl radicals, $$RO \cdot$$, which are reactive enough to generate the chain-propagating radicals from the reactants. The exact sequence of chemical initiation is not always known, but a plausible route in the present case would have $$RO \cdot$$ abstract hydrogen from the alkane:

The propagation steps that would follow are

Chlorination with sulfuryl chloride of alkanes with more than one kind of hydrogen gives a mixture of alkyl chlorides resembling that obtained with chlorine itself. However, in some circumstances the mixture of chlorides is not the same mixture obtained with chlorine itself and when this is true, the hydrogen-abstraction step probably involves $$\cdot SO_2Cl$$ rather than $$Cl \cdot$$. The alternative propagation steps then are

Different product ratios are expected from $$Cl \cdot$$ and $$ClSO_2 \cdot$$ for the same reason that $$Cl \cdot$$ and $$Br \cdot$$ lead to different product ratios (Section 4-5A). Other reagents that sometimes are useful halogenating agents in radical-chain reactions include


text:
## New Paper: Evolved Massive Stars at Low-metallicity III. A Source Catalog for the Large Magellanic Cloud

Posted December 18, 2020 By grigoris

Evolved Massive Stars at Low-metallicity III. A Source Catalog for the Large Magellanic Cloud

Ming Yang, Alceste Z. Bonanos, Biwei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, Xiao-Dian Chen, Man I Lam, Yi Ren, Frank Tramper, Zoi T. Spetsieri

We present a clean, magnitude-limited (IRAC1 or WISE115.0 mag) multiwavelength source catalog for the LMC. The catalog was built upon crossmatching (1′′) and deblending (3′′) between the SEIP source list and Gaia DR2, with strict constraints on the Gaia astrometric solution to remove the foreground contamination. The catalog contains 197,004 targets in 52 different bands including 2 ultraviolet, 21 optical, and 29 infrared bands. Additional information about radial velocities and spectral/photometric classifications were collected from the literature. The bright end of our sample is mostly comprised of blue helium-burning stars (BHeBs) and red HeBs with inevitable contamination of main sequence stars at the blue end. After applying modified magnitude and color cuts based on previous studies, we identify and rank 2,974 RSG, 508 YSG, and 4,786 BSG candidates in the LMC in six CMDs. The comparison between the CMDs of the LMC and SMC indicates that the most distinct difference appears at the bright red end of the optical and near-infrared CMDs, where the cool evolved stars (e.g., RSGs, AGB, and RGs) are located, which is likely due to the effect of metallicity and SFH. Further quantitative comparison of colors of massive star candidates in equal absolute magnitude bins suggests that, there is basically no difference for the BSG candidates, but large discrepancy for the RSG candidates as LMC targets are redder than the SMC ones, which may be due to the combined effect of metallicity on both spectral type and mass-loss rate, and also the age effect. The Teff of massive star populations are also derived from reddening-free color of (JKS)0. The Teff ranges are 3500<Teff<5000 K for RSG population, 5000<Teff<8000 K for YSG population, and Teff>8000 K for BSG population, with larger uncertainties towards the hotter stars.
arXiv.org: 2012.08623

## Creating OSIRIS mosaic images

Posted September 15, 2020 By grigoris

To properly process data (either imaging or multi-slit spectra) from the OSIRIS instrument at GTC you need first to convert the original data to single images. The raw fits data contain two extensions corresponding to the two CCD sensors. To combine these into a single 2110 X 2051 pixels image (including the gap between the sensors) they provide with a convenient IRAF script called Mosaic Tool. This takes into account more than simply adding the extensions. As written in the ReadmeFirst.txt file:

(i) corrects the input OSIRIS pre-image for overscan to provide a uniform background, (ii) executes the mosaic assembly according to the rotation/traslation parameters obtained from the MD Polynomial Manual (current version: V5; see contents of the attached MD_polynomials.pdf file) on a new created image, (iii) copies the zero extension of the input image in the new one, and (iv) updates the WCS keywords. Such updating consists in switch the input reference pixel (CRPIX1, CRPIX2) to the center of the mosaic (see MD_polynomials.pdf, p.12) and calculate the celestial coordinates (CRVAL1, CRVAL2) corresponding to the new reference, based on the original WCS parameters. Hence, the correction of the astrometric solution is the same of the input image.

So it does quite a work! To run it you have to start IRAF, define the task and apply it to an raw image:

-->task \$mosaic_2x2=mosaic_2x2_v2.cl -->mosaic_2x2 0002604740-20200628-OSIRIS-OsirisBroadBandImage.fits

(the name for the task, in this case mosaic_2x2 is not really important). When I tried that (using PyRAF v2.1.5 with IRAF 2.16.1 on a Debian 10 machine) I got an error:

Traceback (innermost last): File "", line 1, in File "", line 16, in mosaic iraf.noao.PY() AttributeError: Parameter PY not found

which I couldn’t understand. Even after contacting the support the mystery remained. Back in early August we had only a couple of images from our pre-imaging run (and not time to waste…) and the support team was kind enough to do it for us. But now that we have the actual spectroscopic data the images are way too more to ask again for this. Therefore, I tried to search a bit deeper than the first time.

I started by checking the mosaic_2x2_v2.cl file, and I removed all tasks to search where could be the problem. So I edited it to run this:

procedure mosaic (fobjeto)

file fobjeto {"",prompt="Object file:"}

begin

string objeto
file   text

objeto=mosaic.fobjeto

noao.
noao.artdata.
noao.imred
noao.imred.bias.
images.imgeom
ctio

#stsdas
#toolbox
#imgtools

end

In this form it only calls the basic packages of IRAF. Still though, when I attempted to run it again I took the same error! So the problem was already at the beginning. Then, I noticed these dots after the packages (e.g. noao, noao.artdata.) where obviously python was trying to get a non-existing attribute. After removing these dots it actually proceeded! Only after this revelation, it came to my mind to actually try the IRAF cl directly (when you have the python supported version there is no need to look back to cl!). However, it made a difference as (obviously) the cl doesn’t care about the cots… So the problem was python specific.

The script continued running only to … stuck again:

 Traceback (innermost last): File "", line 1, in File "", line 44, in mosaic iraf.imcreate(image = 'CCD_mosaic.fits', naxis = 2, naxis1 = 2110,naxis2 = 2051,pixtype = 'real') AttributeError: Undefined IRAF task imcreate'

However, this is easily interpreted. imcreate is a task in the ctio package, which is external (not part of the original core of IRAF). So you need to find the ctio package and put it under the iraf/extern/. This was actually not easy to spot since the support of IRAF by the STScI has dropped and most links are broken. The package is supported by the iraf-community, but I didn’t try to check exactly how to download (as I didn’t find it under my iraf/ directory). On the other hand, I found an alternative (in the iraf.net/forum) using the mkpattern task, under the artdata package which is already loaded in the file. Then, I just replaced the following line:

imcreate(image="CCD_mosaic.fits",naxis=2,naxis1=2110,naxis2=2051,pixtype="real")

with

mkpattern(input="CCD_mosaic.fits",pixtype="real", ndim=2, ncols=2110, nlines=2051)`

and all problems are finally solved! The script runs to the end and provides the dinal mosaic image.

Bottomline, or what to do in three single steps

• If you are running cl/ecl directly AND have ctio package installed you don’t need to do anything
• If you are running cl/ecl directly and you don’t have ctio, just replace imcreate with mkpattern
• If you are running on python then remove the dots when the IRAF packages are called

I would therefore strongly suggest the support team to remove the dots and replace the imcreate task so that it can work in all cases.
(For any potential user here is my modified version: mosaic_2x2_v2_gm.cl.txt – remove .txt)

## Preparing for MOS observations with the GTC

Posted August 18, 2020 By grigoris

The pandemic of Covid-19 has obviously affected our observing runs also. ESO is (still) totally shut down while GTC after some off period it seems that it is under some limited operation.

Our accepted observing program with GTC includes Mulit-Object Spectroscopic observations with the OSIRIS instrument. For this we need to prepare masks, i.e. metal plates on which a number of slits is cut to allow the light of specific targets to pass and acquire their spectrum. In order to optimize the positioning of those slits (and make sure that they are placed on the correct coordinates) we have asked for pre-imaging, e.g. the acquisition of short exposures of the FOVs we have requested (for all our targets). With these and the appropriate software (Mask Designer) it becomes easy to create the masks.

Some weeks ago we actually received (quite unexpectedly, given the pandemic status) a couple of images. That allowed us to prepare the corresponding masks (after resolving all technical difficulties and questions of course). An example of these masks is shown below for the galaxy IC 10. When creating the mask we have to avoid slits that result in overlapping spectra (because we will end up with useless data). That’s why we need to prioritize our targets and select the best combination which will allow for the maximum non-colliding number of targets to be observed, respecting all constraints imposed by the program and the instrument involved. Although mask designing with modern software tools can become easy it is still a time-consuming step that needs caution and accuracy.

The image shows the mask designed for the galaxy IC 10. The small white line are the slits with the corresponding spectra visible as vertical thick green lines. Smaller lines correspond to fiducial stars, i.e. stars that help to the alignment of the mask. The yellow box corresponds to the physical limits of the maks. The background image is a short exposure of IC 10 in the r band.

So, the masks have been prepared, verified, constructed, … and now we wait for the real spectroscopic observations to be obtained! Fingers crossed!

note: the current article has been written originally for the ASSESS group.

## Some 2020 Perseids observed …

Posted August 17, 2020 By grigoris

It has been many many years since my last visual observations of meteors (back in 2010…). Last week I found the opportunity to observe a bit the peak of the Perseid shower.

I observed for a couple of hours, just before the moon rise, from my place in the relatively light polluted Heraklion of Crete. I was a bit tired since I have waken up in the morning and didn’t manage to get any sleep so definitely I missed some fainter ones. But there were a few bright Perseids that I saw with a couple of them being kind of spectacular!

Although the total number recorded was low (7 Perseids and 5 Sporadics) I enjoyed the observation. Sitting relaxed in a chair and viewing the nigh sky feels nice, especially when the anticipation on the next bright meteor is added.

You can find the particular observation log as Session ID 80974 at IMO.

## eROSITA’s first all-sky image

Posted June 26, 2020 By grigoris

Recently the first all-sky image of the X-ray sky was released by the eROSITA instrument on-board the Spectrum-Roentgen-Gamma” (SRG) mission. It is fascinating that they refer to a million X-ray, which is almost 10 times more than what has been discovered from all X-ray surveys so far, so a bright X-ray future lies ahead! The image is captivating, especially when you try to pinpoint some of the brightest and interesting sources.

The energetic universe as seen with the eROSITA X-ray telescope. The first eROSITA all-sky survey was conducted over a period of six months by letting the telescope rotate continuously, thus providing a uniform exposure of about 150-200 seconds over most of the sky, with the ecliptic poles being visited more deeply. As eROSITA scans the sky, the energy of the collected photons is measured with an accuracy ranging from 2% – 6%. To generate this image, in which the whole sky is projected onto an ellipse (so-called Aitoff projection) with the centre of the Milky Way in the middle and the body of the Galaxy running horizontally, photons have been colour-coded according to their energy (red for energies 0.3-0.6 keV, green for 0.6-1 keV, blue for 1-2.3 keV). The original image, with a resolution of about 10”, and a corresponding dynamic range of more than one billion, is then smoothed (with a 10’ FWHM Gaussian) in order to generate the above picture.The red diffuse glow away from the galactic plane is the emission of the hot gas in the vicinity of the solar system (the Local Bubble). Along the plane itself, dust and gas absorb the lowest energy X-ray photons, so that only high-energy emitting sources can be seen, and their colour appears blue in the image. The hotter gas close to the galactic centre, shown in green and yellow, carries imprinted the history of the most energetic processes in the life of the Milky Way, such as supernova explosions, driving fountains of gas out of the plane, and, possibly, past outburst from the now dormant supermassive black hole in the centre of the galaxy. Piercing through this turbulent, hot diffuse medium, are hundreds of thousands of X-ray sources, which appear mostly white in the image, and uniformly distributed over the sky. Among them, distant active galactic nuclei (including a few emitting at a time when the Universe was less than one tenth of its current age) are visible as point sources, while clusters of galaxies reveal themselves as extended X-ray nebulosities. In total, about one million X-ray sources have been detected in the eROSITA all-sky image, a treasure trove that will keep the teams busy for the coming years.

Credit: Jeremy Sanders, Hermann Brunner and the eSASS team (MPE); Eugene Churazov, Marat Gilfanov (on behalf of IKI)

Source: Max Planck Institute for Extraterrestrial Physics, Our deepest view of the X-ray sky, (accessed 26 June 2020)

Annotated version of the eROSITA First All-Sky image. Several prominent X-ray features are marked, ranging from distant galaxy clusters (Coma, Virgo, Fornax, Perseus) to extended sources such as Supernova Remnants (SNRs) and Nebulae to bright point sources, e.g. Sco X-1, the first extrasolar X-ray source to be detected. The Vela SNR is to the right of this image, the Large Magellanic Cloud in the bottom right quadrant, the Shapley supercluster in the upper right (though not easily visible in this projection).

Source: Max Planck Institute for Extraterrestrial Physics, Presskit for the eROSITA First All-Sky Survey (accessed 26 June 2020)

## New paper: Evolved Massive Stars at Low-metallicity II. Red Supergiant Stars in the Small Magellanic Cloud

Posted May 21, 2020 By grigoris

Evolved Massive Stars at Low-metallicity II. Red Supergiant Stars in the Small Magellanic Cloud

Ming Yang, Alceste Z. Bonanos, Bi-Wei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, Xiao-Dian Chen, Frank Tramper, Yi Ren, Zoi T. Spetsieri, Meng-Yao Xue

We present the most comprehensive RSG sample for the SMC up to now, including 1,239 RSG candidates. The initial sample is derived based on a source catalog for the SMC with conservative ranking. Additional spectroscopic RSGs are retrieved from the literature, as well as RSG candidates selected from the inspection of CMDs. We estimate that there are in total ∼ 1,800 or more RSGs in the SMC. We purify the sample by studying the infrared CMDs and the variability of the objects, though there is still an ambiguity between AGBs and RSGs. There are much less RSGs candidates (∼4%) showing PAH emission features compared to the Milky Way and LMC (∼15%). The MIR variability of RSG sample increases with luminosity. We separate the RSG sample into two subsamples (“risky” and “safe”) and identify one M5e AGB star in the “risky” subsample. Most of the targets with large variability are also the bright ones with large MLR. Some targets show excessive dust emission, which may be related to previous episodic mass loss events. We also roughly estimate the total gas and dust budget produced by entire RSG population as ∼1.9(+2.4/−1.1)×10−6 M⊙/yr in the most conservative case. Based on the MIST models, we derive a linear relation between Teff and observed J−KS color with reddening correction for the RSG sample. By using a constant bolometric correction and this relation, the Geneva evolutionary model is compared with our RSG sample, showing a good agreement and a lower initial mass limit of ∼7 M⊙ for the RSG population. Finally, we compare the RSG sample in the SMC and the LMC. Despite the incompleteness of LMC sample in the faint end, the result indicates that the LMC sample always shows redder color (except for the IRAC1−IRAC2 and WISE1−WISE2 colors due to CO absorption) and larger variability than the SMC sample.

arXiv.org: 2005.10108

## Packaging Skinakas’ primary mirror

Posted March 9, 2020 By grigoris

Back in November of 2019, the primary mirror (of 1.3m) at the Skinakas Observatory was dismounted to send it for recoating (a procedure performed once every 4-5 years). The following short clip shows the packaging of the mirror, a very delicate procedure (the whole process took several hours … compressed to a few minutes).

## The second nearest exoplanet – Proxima c

Posted January 20, 2020 By grigoris

It is exciting to see that the nearest star to our Sun has more than one planet (Proxima b, [1]). In a paper led by Mario Damasso and Fabio Del Sordo [2] revealed the existence of another one. The Proxima c is a planet with an orbital period of about 1900 days (~5.21 years), a semi-major axis ac = 1.48 ± 0.08 AU, minimum mass (mc sin ic) = 5.8 ± 1.9 M⊕, and equilibrium temperature Teq=39(+16/−18) K. This super-Earth is found further away than the typical distances for this type of planets, something that poses interesting questions with respect to the planet formation and evolution.

Apart from the very interesting results of this paper what is equally exciting is the fact the Fabio has been my office mate since 2017 at the University of Crete. I  am really happy for him and his success. And, as he moved to a new position in Italy, I wish him all the best and new discoveries.

Abstract
Our nearest neighbor, Proxima Centauri, hosts a temperate terrestrial planet. We detected in radial velocities evidence of a possible second planet with minimum mass mc sin ic = 5.8 ± 1.9M and orbital period Pc=5.21+0.260.22 years. The analysis of photometric data and spectro-scopic activity diagnostics does not explain the signal in terms of a stellar activity cycle, but follow-up is required in the coming years for confirming its planetary origin. We show that the existence of the planet can be ascertained, and its true mass can be determined with high accuracy, by combining Gaia astrometry and radial velocities. Proxima c could become a prime target for follow-up and characterization with next-generation direct imaging instrumentation due to the large maximum angular separation of ~1 arc second from the parent star. The candidate planet represents a challenge for the models of super-Earth formation and evolution.

References:

[1] Anglada-Escudé et al. 2016, “A terrestrial planet candidate in a temperate orbit around Proxima Centauri“, Nature, 536, 437 | NASA/ADS

[2] Mario Damasso, Fabio Del Sordo, Guillem Anglada-Escudé, Paolo Giacobbe, Alessandro Sozzetti, Alessandro Morbidelli, Grzegorz Pojmanski, Domenico Barbato, R. Paul Butler, Hugh R. A. Jones, Franz-Josef Hambsch, James S. Jenkins, María José López-González, Nicolás Morales, Pablo A. Peña Rojas, Cristina Rodríguez-López, Eloy Rodríguez, Pedro J. Amado, Guillem Anglada, Fabo Feng and Jose F. Gómez, 2020, “A low-mass planet candidate orbiting Proxima Centauri at a distance of 1.5 AU”, Science Advances, Vol. 6, no. 3, eaax7467 | NASA/ADS


text:
# Odds ratio

The odds ratio is a measure of effect size, describing the strength of association or non-independence between two binary data values. It is used as a descriptive statistic, and plays an important role in logistic regression.

## Definition

### Definition in terms of group-wise odds

The odds ratio is the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or to a sample-based estimate of that ratio. These groups might be men and women, an experimental group and a control group, or any other dichotomous classification. If the probabilities of the event in each of the groups are p1 (first group) and p2 (second group), then the odds ratio is:

${ p_1/(1-p_1) \over p_2/(1-p_2)}={ p_1/q_1 \over p_2/q_2}=\frac{\;p_1q_2\;}{\;p_2q_1\;},$

where qx = 1 − px. An odds ratio of 1 indicates that the condition or event under study is equally likely in both groups. An odds ratio greater than 1 indicates that the condition or event is more likely in the first group. And an odds ratio less than 1 indicates that the condition or event is less likely in the first group. The odds ratio must be greater than or equal to zero if it is defined. It is undefined if p2q1 equals zero.

### Definition in terms of joint and conditional probabilities

The odds ratio can also be defined in terms of the joint probability distribution of two binary random variables. The joint distribution of binary random variables X and Y can be written

 Y = 1 Y = 0 X = 1 p11 p10 X = 0 p01 p00

where p11, p10, p01 and p00 are non-negative "cell probabilities" that sum to one. The odds for Y within the two subpopulations defined by X = 1 and X = 0 are defined in terms of the conditional probabilities given X:

 Y = 1 Y = 0 X = 1 p11 / (p11 + p10) p10 / (p11 + p10) X = 0 p01 / (p01 + p00) p00 / (p01 + p00)

Thus the odds ratio is

${ \dfrac{p_{11}/(p_{11}+p_{10})}{p_{10}/(p_{11}+p_{10})} \bigg / \dfrac{p_{01}/(p_{01}+p_{00})}{p_{00}/(p_{01}+p_{00})}} = \dfrac{p_{11}p_{00}}{p_{10}p_{01}}.$

The simple expression on the right, above, is easy to remember as the product of the probabilities of the "concordant cells" (X = Y) divided by the product of the probabilities of the "discordant cells" (X ≠ Y). However note that in some applications the labeling of categories as zero and one is arbitrary, so there is nothing special about concordant versus discordant values in these applications.

### Symmetry

If we had calculated the odds ratio based on the conditional probabilities given Y,

 Y = 1 Y = 0 X = 1 p11 / (p11 + p01) p10 / (p10 + p00) X = 0 p01 / (p11 + p01) p00 / (p10 + p00)

we would have gotten the same result

${ \dfrac{p_{11}/(p_{11}+p_{01})}{p_{01}/(p_{11}+p_{01})} \bigg / \dfrac{p_{10}/(p_{10}+p_{00})}{p_{00}/(p_{10}+p_{00})}} = \dfrac{p_{11}p_{00}}{p_{10}p_{01}}.$

Other measures of effect size for binary data such as the relative risk do not have this symmetry property.

### Relation to statistical independence

If X and Y are independent, their joint probabilities can be expressed in terms of their marginal probabilities px = P(X = 1) and py = P(Y = 1), as follows

 Y = 1 Y = 0 X = 1 pxpy px(1 − py) X = 0 (1 − px)py (1 − px)(1 − py)

In this case, the odds ratio equals one, and conversely the odds ratio can only equal one if the joint probabilities can be factored in this way. Thus the odds ratio equals one if and only if X and Y are independent.

## Example

Suppose that in a sample of 100 men, 90 have drunk wine in the previous week, while in a sample of 100 women only 20 have drunk wine in the same period. The odds of a man drinking wine are 90 to 10, or 9:1, while the odds of a woman drinking wine are only 20 to 80, or 1:4 = 0.25:1. The odds ratio is thus 9/0.25, or 36, showing that men are much more likely to drink wine than women. Using the above formula for the calculation yields the same result:

${ 0.9/0.1 \over 0.2/0.8}=\frac{\;0.9\times 0.8\;}{\;0.1\times 0.2\;} ={0.72 \over 0.02} = 36.$

The above example also shows how odds ratios are sometimes sensitive in stating relative positions: in this sample men are 90/20 = 4.5 times more likely to have drunk wine than women, but have 36 times the odds. The logarithm of the odds ratio, the difference of the logits of the probabilities, tempers this effect, and also makes the measure symmetric with respect to the ordering of groups. For example, using natural logarithms, an odds ratio of 36/1 maps to 3.584, and an odds ratio of 1/36 maps to −3.584.

## Statistical inference

Confidence intervals and hypothesis tests relating to odds ratios are constructed in terms of the "log odds ratio," which is the natural logarithm of the odds ratio. If we use the joint probability notation defined above, the population log odds ratio is

${\log\Big(\frac{p_{11}p_{00}}{p_{01}p_{10}}\Big) = \log\big(p_{11}) + \log(p_{00}\big) - \log(p_{10}) - \log(p_{01})}.\,$

If we observe data in the form of a contingency table

 Y = 1 Y = 0 X = 1 n11 n10 X = 0 n01 n00

then the probabilities in the joint distribution can be estimated as

 Y = 1 Y = 0 X = 1 $\hat{p}_{11}$ $\hat{p}_{10}$ X = 0 $\hat{p}_{01}$ $\hat{p}_{00}$

where $\hat{p}_{ij} = n_{ij}/n$, with n = n11 + n10 + n01 + n00 being the sum of all four cell counts. The sample log odds ratio is

${L=\log\Bigg(\dfrac{\hat{p}_{11}\hat{p}_{00}}{\hat{p}_{10}\hat{p}_{01}}\Bigg) = \log\Bigg(\dfrac{n_{11}n_{00}}{n_{10}n_{01}}\Bigg)}$.

The standard error for the log odds ratio is approximately

${{\rm SE} = \sqrt{\dfrac{1}{n_{11}} + \dfrac{1}{n_{10}} + \dfrac{1}{n_{01}} + \dfrac{1}{n_{00}}}}$.

This is an asymptotic approximation, and will not give a meaningful result if any of the cell counts are very small. If L is the sample log odds ratio, an approximate 95% confidence interval for the population log odds ratio is $L\pm 2{\rm SE}$. This can be mapped to $\exp(L-2{\rm SE}),\;\exp(L+2{\rm SE})$ to obtain a 95% confidence interval for the odds ratio. If we wish to test the hypothesis that the population odds ratio equals one, the two-sided p-value is 2P(Z < − | L | / SE), where P denotes a probability, and Z denotes a standard normal random variable.

## Role in logistic regression

Logistic regression is one way to generalize the odds ratio beyond two binary variables. Suppose we have a binary response variable Y and a binary predictor variable X, and in addition we have other predictor variables Z1, ..., Zp that may or may not be binary. If we use multiple logistic regression to regress Y on X, Z1, ..., Zp, then the estimated coefficient $\hat{\beta}_x$ for X is related to a conditional odds ratio. Specifically, at the population level

$\exp(\beta_x) = \frac{P(Y=1|X=1, Z_1, \ldots, Z_p)/P(Y=0|X=1, Z_1, \ldots, Z_p)}{P(Y=1|X=0, Z_1, \ldots, Z_p)/P(Y=0|X=0, Z_1, \ldots, Z_p)},$

so $\exp(\hat{\beta}_x)$ is an estimate of this conditional odds ratio. The interpretation of $\exp(\hat{\beta}_x)$ is as an estimate of the odds ratio between Y and X when the values of Z1, ..., Zp are held fixed.

## Use in quantitative research

Due to the widespread use of logistic regression in medical and social science research, the odds ratio is commonly used as a means of expressing the results in some forms of clinical trials, in survey research, and in epidemiology, such as in case-control studies. It is often abbreviated "OR" in reports. When data from multiple surveys is combined, it will often be expressed as "pooled OR". The odds ratio, while in itself difficult to interpret, is in such cases used as an estimate of the relative risk. This, however, is only valid when dealing with low-probability events: The formula for the odds is defined as p / (1 − p), so when p moves towards zero, (1 − p) moves towards 1, meaning that as p approaches zero, the odds approaches the risk, and the odds ratio approaches the relative risk.

## Worked example

Experimental group (E) Control group (C) Total
Events (E) EE or "A" = 15 CE or "B" = 100 115
Non-events (N) EN or "C" = 135 CN or "D" = 150 285
Total subjects (S) ES = 150 CS = 250 400
Event rate (ER) EER = EE / ES = 0.1, or 10% CER = CE / CS = 0.4, or 40%
 Abbreviation Variable Equation Value ARR absolute risk reduction (or increase) = EER − CER -0.3, or -30% RRR relative risk reduction (or increase) = |(EER − CER)| / CER[1] 0.75 NNT number needed to treat/number needed to harm = 1 / ARR 3.33 RR relative risk = EER / CER or = (EE / ES) / (CE / CS) 0.25 OR odds ratio = (EE / EN) / (CE / CN) 0.167


text:
# A survey was conducted with 120 students in a school to see how many students were members of extra-curricular clubs .The following

###### Question:

A survey was conducted with 120 students in a school to see how many students were members of extra-curricular clubs .The following results were recorded for the All Red club, Boys and Girls club and Foreigner's band 1) 10 students were not members of any of the three clubs
2) 50 were members of All Red club
3) 50 were members of Boys and Girls club
4) 50 were members of Foreigner's band
5)15 were members of All Red club and Boys and Girls Club
6)10 were members of Boys and Girls Club and Foreigner's Band
7) 20 were members of All Red club and Foreigner's Band.
Draw a venn diagram and use it to find how many students were in all three clubs

### AwDFJHg, nkuxbwdui683760198we1t7t-9dn1n8uds

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### Commonlit -Langston Hughes' hidden influence on MLK1. PART B: Which detail from the text best supports the answer to Part A

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### 15. An increase in atomic number is related to an increase in atomic mass becausea. more electrons are present in the atomic nucleusb. more electrons

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text:
# 0.2 Practice tests (1-4) and final exams  (Page 8/36)

 Page 8 / 36

37 . You had an unusually long wait time, which is bad: 82 percent of patients had a shorter wait time than you, and only 18 percent had a longer wait time.

## 2.4: box plots

38 . 5

39 . 3

40 . 7

41 . The median is 86, as represented by the vertical line in the box.

42 . The first quartile is 80, and the third quartile is 92, as represented by the left and right boundaries of the box.

43 . IQR = 92 – 80 = 12

44 . Range = 100 – 75 = 25

## 2.5: measures of the center of the data

45 . Half the runners who finished the marathon ran a time faster than 3:35:04, and half ran a time slower than 3:35:04. Your time is faster than the median time, so you did better than more than half of the runners in this race.

46 . 61.5, or $61,500 47 . 49.25 or$49,250

48 . The median, because the mean is distorted by the high value of one house.

## 2.6: skewness and the mean, median, and mode

49 . c

50 . a

51 . They will all be fairly close to each other.

## 2.7: measures of the spread of the data

52 . Mean: 15
Standard deviation: 4.3
$\mu =\frac{10+11+15+15+17+22}{6}=15$
$s=\sqrt{\frac{\sum {\left(x-\overline{x}\right)}^{2}}{n-1}}=\sqrt{\frac{94}{5}}=4.3$

53 . 15 + (2)(4.3) = 23.6

54 . 13.7 is one standard deviation below the mean of this data, because 15 – 4.3 = 10.7

55 . $z=\frac{95-85}{5}=2.0$
Susan’s z -score was 2.0, meaning she scored two standard deviations above the class mean for the final exam.

## 3.1: terminology

56 . $P\left(B\right)=\frac{25}{90}=0.28$

57 . Drawing a red marble is more likely.
$P\left(R\right)=\frac{50}{80}=0.62$
$P\left(Y\right)=\frac{15}{80}=0.19$

58 . P ( F AND S )

59 . P ( E | M )

## 3.2: independent and mutually exclusive events

60 . P ( A AND B ) = (0.3)(0.5) = 0.15

61 . P ( C OR D ) = 0.18 + 0.03 = 0.21

## 3.3: two basic rules of probability

62 . No, they cannot be mutually exclusive, because they add up to more than 300. Therefore, some students must fit into two or more categories (e.g., both going to college and working full time).

63 . P ( A and B ) = ( P ( B | A ))( P ( A )) = (0.85)(0.70) = 0.595

64 . No. If they were independent, P ( B ) would be the same as P ( B | A ). We know this is not the case, because P ( B ) = 0.70 and P ( B | A ) = 0.85.

## 3.4: contingency tables

65 .

Honor roll No honor roll Total
Study at least 15 hours/week 482 200 682
Study less than 15 hours/week 125 193 318
Total 607 393 1,000

66 .

67 .

68 . Let P ( S ) = study at least 15 hours per week
Let P ( H ) = makes the honor roll
From the table, P ( S ) = 0.682, P ( H ) = 0.607, and P ( S AND H ) =0.482.
If P ( S ) and P ( H ) were independent, then P ( S AND H ) would equal ( P ( S ))( P ( H )).
However, ( P ( S ))( P ( H )) = (0.682)(0.607) = 0.414, while P ( S AND H ) = 0.482.
Therefore, P ( S ) and P ( H ) are not independent.

69 .

70 .

## 4.1: probability distribution function (pdf) for a discrete random variable

Use the following information to answer the next five exercises. You conduct a survey among a random sample of students at a particular university. The data collected includes their major, the number of classes they took the previous semester, and amount of money they spent on books purchased for classes in the previous semester.

1. If X = student’s major, then what is the domain of X ?

in a large restaurant an average of every 7 customers ask for water with the their meal. A random sample of 12 customers is selected, find the probability that exactly 6 ask for water with their meal
any body with idea
Rufai
conditional probability
Ramesh
Rufai
iam really sorry. it's been long since I used these things. I just gave you a hint though
Ramesh
ok
Rufai
this follows binomial distribution. p(X=6)=12C6*(0.6)^6*0.4^6 use this formula n find.
syeda
can you explain the cosidered variable in the formula
Divya
x is variable wich is exactly 6 costumers
syeda
n is number of customers
syeda
ncx*p^X*q^X?
Divya
q^n-x
syeda
oh right !!! thanks yaar
Divya
I agree with Seyda too
Hoshyar
I agree with Syeda too
Hoshyar
7/12 =0.58is it?
yousaf
.
yousaf
r8
khalid
what is descriptive statistic
Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire or a sample of a population. Descriptive statistics are broken down into measures of central tendency and measures of variability (spread).
Divya
are you getting this ?
Divya
if so let me know
Divya
yes m getting
Ramesh
fine
Divya
what's taking place can l join u
Anest
yeah !!why not? sure
Divya
okey thanks
Anest
where are statistics used
hello
Giannis
Hi
Makhosi
how u doing
Muhid
the upper quartile of the population 10,12,14,16,18,20,25,15,11,11,17,is................?
Gach
The probability range is 0 to 1... but why we take it 0 to 1....
because in probability 1 means success and 0 means failure and it cnnt be more or less than 1 and 0.
syeda
b/c v hv mazimum probibliy 1 and minimum which is.no.probiblity is 0.so.v hv the range from 0 to 1
khalid
the size of a set is greeter than its subset
Hoshyar
The probability of an event will not be less than 0.  This is because 0 is impossible (sure that something will not happen).The probability of an event will not be more than 1. This is because 1 is certain that something will happen
Divya
what do they mean in a question when you are asked to find P40 and P88
Mani
hi
Mehri
you're asked to find page 40 and page 88 on that particular book.
Joseph
hi
ravi
any suggestions for statistics app better than this
ravi
sorry miss wrote the question
omar
No problem) By the way. I NEED a program For statistical data analysis. Any suggestion?
Mani
Eviews will help u
Hello
Okonkwo
arey there any data analyst and working on sas statistical model building
ravi
Hi guys ,actually I have dicovered that the P40 and P88 means finding the 40th and 88th percentiles 😌..
Megrina
who can explain the euclidian distance
ravi
I am fresh student of statistics (BS) plz guide me best app or best website relative to stat topics
Noman
IMAGESNEWSVIDEOS A Dictionary of Computing. measures of location Quantities that represent the average or typical value of a random variable (compare measures of variation). They are either properties of a probability distribution or computed statistics of a sample. Three important measures are the mean, median, and mode.
define the measures of location
IMAGESNEWSVIDEOS A Dictionary of Computing. measures of location Quantities that represent the average or typical value of a random variable (compare measures of variation). They are either properties of a probability distribution or computed statistics of a sample. Three important measures are th
Ahmed
hi i have a question....
what is confidence interval estimate and its formula in getting it
discuss the roles of vital and health statistic in the planning of health service of the community
given that the probability of
BITRUS
good morning
Rufai
hi bitrus.
Bratit
hi
yousaf
hallo
Bratit
hey
Divya
uses in health policy and health research
Divya
1) social inequalities I health 2) Trends in mortality
Divya
hey do you know any about any of the statistics courses which is best ?
Divya
hello
Hoshyar
hi
Hoshyar
online courses and convenient fees
Divya
can man city win Liverpool ?
There are two coins on a table. When both are flipped, one coin land on heads eith probability 0.5 while the other lands on head with probability 0.6. A coin is randomly selected from the table and flipped. (a) what is probability it lands on heads? (b) given that it lands on tail, what is the Condi
0.5*0.5+0.5*0.6
Ravasz
It should be a Machine learning terms。
Mok
it is a term used in linear regression
Saurav
what are the differences between standard deviation and variancs?
Enhance
what is statistics
statistics is the collection and interpretation of data
Enhance
the science of summarization and description of numerical facts
Enhance
Is the estimation of probability
Zaini
mr. zaini..can u tell me more clearly how to calculated pair t test
Haai
do you have MG Akarwal Statistics' book Zaini?
Enhance
Haai how r u?
Enhance
maybe .... mathematics is the science of simplification and statistics is the interpretation of such values and its implications.
Miguel
can we discuss about pair test
Haai
what is outlier?
outlier is an observation point that is distant from other observations.
Gidigah
what is its effect on mode?
Usama
Outlier  have little effect on the mode of a given set of data.
Gidigah
How can you identify a possible outlier(s) in a data set.
Daniel
The best visualisation method to identify the outlier is box and wisker method or boxplot diagram. The points which are located outside the max edge of wisker(both side) are considered as outlier.
Akash
@Daniel Adunkwah - Usually you can identify an outlier visually. They lie outside the observed pattern of the other data points, thus they're called outliers.
Ron
what is completeness?
I am new to this. I am trying to learn.
Dom
I am also new Dom, welcome!
Nthabi
thanks
Dom
please my friend i want same general points about statistics. say same thing
alex
outliers do not have effect on mode
Meselu
also new
yousaf