This is the detailed log of this Bhumihar Bihar modelling left pops: Bhumihar_Bihar 7 Ror 6 Irula 15 right pops: Ethiopia_4500BP.SG 2 Ust_Ishim.DG 1 Kostenki14 1 Mongolia_North_N 5 EHG 8 ONG.SG 6 PPNB 1 Turkey_N 28 Xinjiang_Xiaohe_BA 4 jackknife block size: 0.050 snps: 748920 indivs: 84 number of blocks for block jackknife: 714 f4info: f4rank: 1 dof: 7 chisq: 6.183 tail: 0.51858862 dofdiff: 9 chisqdiff: -6.183 taildiff: 1 B: scale 1.000 Ust_Ishim.DG -0.438 Kostenki14 0.438 Mongolia_North_N -0.848 EHG 1.270 ONG.SG -1.302 PPNB 1.052 Turkey_N 1.310 Xinjiang_Xiaohe_BA 0.874 A: scale 386.938 Ror 0.377 Irula -1.363 full rank f4info: f4rank: 2 dof: 0 chisq: 0.000 tail: 1 dofdiff: 7 chisqdiff: 6.183 taildiff: 0.51858862 B: scale 1061.359 275.691 Ust_Ishim.DG -0.552 0.416 Kostenki14 -0.365 -0.555 Mongolia_North_N -1.497 0.729 EHG 0.852 -1.309 ONG.SG -1.767 1.198 PPNB 0.728 -1.079 Turkey_N 0.944 -1.332 Xinjiang_Xiaohe_BA 0.231 -0.949 A: scale 1.414 1.414 Ror 1.414 0.000 Irula 0.000 1.414 best coefficients: 0.783 0.217 Jackknife mean: 0.782996317 0.217003683 std. errors: 0.018 0.018 error covariance (* 1,000,000) 342 -342 -342 342 summ: Bhumihar_Bihar 2 0.518589 0.783 0.217 342 -342 342 fixed pat wt dof chisq tail prob 00 0 7 6.183 0.518589 0.783 0.217 01 1 8 113.025 9.08886e-21 1.000 0.000 10 1 8 1303.443 0 0.000 1.000 best pat: 00 0.518589 - - best pat: 01 9.08886e-21 chi(nested): 106.842 p-value for nested model: 4.82086e-25 coeffs: 0.783 0.217 ## dscore:: f_4(Base, Fit, Rbase, right2) ## genstat:: f_4(Base, Fit, right1, right2) details: Ror Ust_Ishim.DG -0.000520 -1.268606 details: Irula Ust_Ishim.DG 0.001510 3.687287 dscore: Ust_Ishim.DG f4: -0.000080 Z: -0.220928 details: Ror Kostenki14 -0.000344 -0.821262 details: Irula Kostenki14 -0.002014 -5.034005 dscore: Kostenki14 f4: -0.000706 Z: -1.889989 details: Ror Mongolia_North_N -0.001411 -4.289988 details: Irula Mongolia_North_N 0.002644 8.002052 dscore: Mongolia_North_N f4: -0.000531 Z: -1.822000 details: Ror EHG 0.000803 2.511002 details: Irula EHG -0.004747 -15.218404 dscore: EHG f4: -0.000400 Z: -1.416355 details: Ror ONG.SG -0.001665 -4.980975 details: Irula ONG.SG 0.004346 12.276955 dscore: ONG.SG f4: -0.000361 Z: -1.201573 details: Ror PPNB 0.000685 1.812403 details: Irula PPNB -0.003912 -10.261378 dscore: PPNB f4: -0.000312 Z: -0.935058 details: Ror Turkey_N 0.000889 3.071308 details: Irula Turkey_N -0.004832 -16.370648 dscore: Turkey_N f4: -0.000352 Z: -1.369217 details: Ror Xinjiang_Xiaohe_BA 0.000218 0.570177 details: Irula Xinjiang_Xiaohe_BA -0.003443 -9.389033 dscore: Xinjiang_Xiaohe_BA f4: -0.000576 Z: -1.699414 gendstat: Ethiopia_4500BP.SG Ust_Ishim.DG -0.221 gendstat: Ethiopia_4500BP.SG Kostenki14 -1.890 gendstat: Ethiopia_4500BP.SG Mongolia_North_N -1.822 gendstat: Ethiopia_4500BP.SG EHG -1.416 gendstat: Ethiopia_4500BP.SG ONG.SG -1.202 gendstat: Ethiopia_4500BP.SG PPNB -0.935 gendstat: Ethiopia_4500BP.SG Turkey_N -1.369 gendstat: Ethiopia_4500BP.SG Xinjiang_Xiaohe_BA -1.699 gendstat: Ust_Ishim.DG Kostenki14 -1.595 gendstat: Ust_Ishim.DG Mongolia_North_N -1.406 gendstat: Ust_Ishim.DG EHG -0.977 gendstat: Ust_Ishim.DG ONG.SG -0.834 gendstat: Ust_Ishim.DG PPNB -0.596 gendstat: Ust_Ishim.DG Turkey_N -0.858 gendstat: Ust_Ishim.DG Xinjiang_Xiaohe_BA -1.352 gendstat: Kostenki14 Mongolia_North_N 0.531 gendstat: Kostenki14 EHG 1.004 gendstat: Kostenki14 ONG.SG 0.989 gendstat: Kostenki14 PPNB 1.056 gendstat: Kostenki14 Turkey_N 1.184 gendstat: Kostenki14 Xinjiang_Xiaohe_BA 0.378 gendstat: Mongolia_North_N EHG 0.531 gendstat: Mongolia_North_N ONG.SG 0.692 gendstat: Mongolia_North_N PPNB 0.669 gendstat: Mongolia_North_N Turkey_N 0.775 gendstat: Mongolia_North_N Xinjiang_Xiaohe_BA -0.155 gendstat: EHG ONG.SG 0.137 gendstat: EHG PPNB 0.301 gendstat: EHG Turkey_N 0.265 gendstat: EHG Xinjiang_Xiaohe_BA -0.757 gendstat: ONG.SG PPNB 0.145 gendstat: ONG.SG Turkey_N 0.038 gendstat: ONG.SG Xinjiang_Xiaohe_BA -0.663 gendstat: PPNB Turkey_N -0.160 gendstat: PPNB Xinjiang_Xiaohe_BA -0.752 gendstat: Turkey_N Xinjiang_Xiaohe_BA -0.824 worst Z-score with right hand mix f4(Target, Fit, Base, mix of Right pops; Z: -2.488 sum: 1.000 Ust_Ishim.DG -0.613 Kostenki14 0.600 Mongolia_North_N 0.818 EHG -0.189 ONG.SG -0.034 PPNB -0.049 Turkey_N 0.008 Xinjiang_Xiaohe_BA 0.459

Someone might say, "But do these models work in qpAdm?". Well, here we are presenting a model for Meena using Ror and Bhil as proxies (p-value > 0.95, very strong model). left pops: Meena 5 Ror 6 Bhil 8 right pops: Ethiopia_4500BP.SG 2 Ust_Ishim.DG 1 Kostenki14 1 Mongolia_North_N 5 EHG 8 ONG.SG 6 PPNB 1 Turkey_N 28 Xinjiang_Xiaohe_BA 4 jackknife block size: 0.050 snps: 748920 indivs: 75 number of blocks for block jackknife: 714 f4info: f4rank: 1 dof: 7 chisq: 0.998 tail: 0.994855599 dofdiff: 9 chisqdiff: -0.998 taildiff: 1 B: scale 1.000 Ust_Ishim.DG -0.413 Kostenki14 0.432 Mongolia_North_N -1.010 EHG 1.248 ONG.SG -1.369 PPNB 0.993 Turkey_N 1.224 Xinjiang_Xiaohe_BA 0.841 A: scale 602.595 Ror 1.153 Bhil -0.819 full rank f4info: f4rank: 2 dof: 0 chisq: 0.000 tail: 1 dofdiff: 7 chisqdiff: 0.998 taildiff: 0.994855599 B: scale 519.184 741.162 Ust_Ishim.DG -0.375 0.474 Kostenki14 0.434 -0.420 Mongolia_North_N -0.944 1.102 EHG 1.241 -1.252 ONG.SG -1.348 1.394 PPNB 1.080 -0.863 Turkey_N 1.276 -1.142 Xinjiang_Xiaohe_BA 0.791 -0.909 A: scale 1.414 1.414 Ror 1.414 0.000 Bhil 0.000 1.414 best coefficients: 0.415 0.585 Jackknife mean: 0.415149944 0.584850056 std. errors: 0.025 0.025 error covariance (* 1,000,000) 633 -633 -633 633 summ: Meena 2 0.994856 0.415 0.585 633 -633 633 fixed pat wt dof chisq tail prob 00 0 7 0.998 0.994856 0.415 0.585 01 1 8 360.891 0 1.000 0.000 10 1 8 206.778 0 0.000 1.000 best pat: 00 0.994856 - - best pat: 10 2.3799e-40 chi(nested): 205.780 p-value for nested model: 1.14435e-46 coeffs: 0.415 0.585 ## dscore:: f_4(Base, Fit, Rbase, right2) ## genstat:: f_4(Base, Fit, right1, right2) details: Ror Ust_Ishim.DG -0.000721 -1.645461 details: Bhil Ust_Ishim.DG 0.000640 1.544434 dscore: Ust_Ishim.DG f4: 0.000075 Z: 0.202023 details: Ror Kostenki14 0.000835 1.854928 details: Bhil Kostenki14 -0.000567 -1.386642 dscore: Kostenki14 f4: 0.000015 Z: 0.041166 details: Ror Mongolia_North_N -0.001819 -5.104447 details: Bhil Mongolia_North_N 0.001486 4.536255 dscore: Mongolia_North_N f4: 0.000114 Z: 0.390186 details: Ror EHG 0.002391 7.020298 details: Bhil EHG -0.001689 -5.039449 dscore: EHG f4: 0.000004 Z: 0.015385 details: Ror ONG.SG -0.002597 -7.219302 details: Bhil ONG.SG 0.001881 5.487526 dscore: ONG.SG f4: 0.000022 Z: 0.072929 details: Ror PPNB 0.002080 4.899661 details: Bhil PPNB -0.001164 -2.802844 dscore: PPNB f4: 0.000183 Z: 0.498940 details: Ror Turkey_N 0.002458 7.792816 details: Bhil Turkey_N -0.001540 -5.035728 dscore: Turkey_N f4: 0.000120 Z: 0.444917 details: Ror Xinjiang_Xiaohe_BA 0.001523 3.676915 details: Bhil Xinjiang_Xiaohe_BA -0.001227 -3.099144 dscore: Xinjiang_Xiaohe_BA f4: -0.000085 Z: -0.242810 gendstat: Ethiopia_4500BP.SG Ust_Ishim.DG 0.202 gendstat: Ethiopia_4500BP.SG Kostenki14 0.041 gendstat: Ethiopia_4500BP.SG Mongolia_North_N 0.390 gendstat: Ethiopia_4500BP.SG EHG 0.015 gendstat: Ethiopia_4500BP.SG ONG.SG 0.073 gendstat: Ethiopia_4500BP.SG PPNB 0.499 gendstat: Ethiopia_4500BP.SG Turkey_N 0.445 gendstat: Ethiopia_4500BP.SG Xinjiang_Xiaohe_BA -0.243 gendstat: Ust_Ishim.DG Kostenki14 -0.145 gendstat: Ust_Ishim.DG Mongolia_North_N 0.116 gendstat: Ust_Ishim.DG EHG -0.207 gendstat: Ust_Ishim.DG ONG.SG -0.154 gendstat: Ust_Ishim.DG PPNB 0.274 gendstat: Ust_Ishim.DG Turkey_N 0.138 gendstat: Ust_Ishim.DG Xinjiang_Xiaohe_BA -0.412 gendstat: Kostenki14 Mongolia_North_N 0.280 gendstat: Kostenki14 EHG -0.033 gendstat: Kostenki14 ONG.SG 0.019 gendstat: Kostenki14 PPNB 0.420 gendstat: Kostenki14 Turkey_N 0.343 gendstat: Kostenki14 Xinjiang_Xiaohe_BA -0.262 gendstat: Mongolia_North_N EHG -0.439 gendstat: Mongolia_North_N ONG.SG -0.378 gendstat: Mongolia_North_N PPNB 0.201 gendstat: Mongolia_North_N Turkey_N 0.023 gendstat: Mongolia_North_N Xinjiang_Xiaohe_BA -0.680 gendstat: EHG ONG.SG 0.063 gendstat: EHG PPNB 0.554 gendstat: EHG Turkey_N 0.599 gendstat: EHG Xinjiang_Xiaohe_BA -0.362 gendstat: ONG.SG PPNB 0.453 gendstat: ONG.SG Turkey_N 0.375 gendstat: ONG.SG Xinjiang_Xiaohe_BA -0.328 gendstat: PPNB Turkey_N -0.242 gendstat: PPNB Xinjiang_Xiaohe_BA -0.716 gendstat: Turkey_N Xinjiang_Xiaohe_BA -0.709 worst Z-score with right hand mix f4(Target, Fit, Base, mix of Right pops; Z: 1.000 sum: 1.000 Ust_Ishim.DG 0.099 Kostenki14 -0.517 Mongolia_North_N 2.086 EHG -1.190 ONG.SG -1.061 PPNB 0.938 Turkey_N 2.428 Xinjiang_Xiaohe_BA -1.783

aih jadi kangen ospek, rabraw jalan bareng anak plus, probin sm genstat jalan bareng anak kerto

You can see (water) discharge so the bilge pump or genstat is running for electrical generation. Marine engines are installed during the build. My guess is they haven't finish connecting the exhaust to the funnel.

Adiga magacaaga video laga dhex sheegi karaaa jaahilyahow cadeen intaas ka badan loo bahan yahay marka hore nimanka falka genstat lasoo qabto la cadeeyo sababta ayaa ka horeyso

بحوث تخرج بحوث ماجستير بحوث دكتوراه بحوث نشر مشاريع بحث مشروع برمجة ماثلاب (MATLAB) بايثون (Python) آر (R) جوليا (Julia) سايثون (Syththon إس بي إس إس (SPSS) إس إيه إس (SAS) ستاتا (Stata) جينستات (Genstat) إم إل وين (Minitab) جافا (Java) 2. سي (C ) 3. سي# (C#)

بحوث تخرج بحوث ماجستير بحوث دكتوراه بحوث نشر مشاريع بحث مشروع برمجة ماثلاب (MATLAB) بايثون (Python) آر (R) جوليا (Julia) سايثون (Syththon إس بي إس إس (SPSS) إس إيه إس (SAS) ستاتا (Stata) جينستات (Genstat) إم إل وين (Minitab) جافا (Java) 2. سي (C ) 3. سي# (C#)

genstat final chapter?? jadi udah selesai nih? serius udah kelar?

Write a Genstat package for R?

Baffling. You all should be using Genstat. R doesn’t even do designed experiments properly. I doubt Python is better.

besok pas genstat ketawa dong bang kan kocak x.com/statboyyy/status/18352…

I learnt R back in 2010, when it wasn’t a thing. Self taught during NYSC. It was hard to learn because resources were limited but I knew it so well. I couldn’t find anyone outside academics who could use it. Then we called it a statistical tool; a lot more powerful than SPSS and GENSTAT; the same thing folks now call data analytics software. Of course I don’t know how it world anymore as I haven’t touched it since 2011. A lot must have changed now. If you’re going to learn anything, you must work so hard, sometimes you’ll have to toil. Scratching the surface and hoping you’ll learn a thing is a big lie.

There is a description in my first book, Cross-over Trials in Clinical Research senns.uk/cticr2.html and there is code here senns.uk/CTiCR/CTICR2Program… See the 'Sample size' programs under @Genstat SAS SPlus etc plus the R program put up by Anna Schritz.

ILRI Hiring Vacancy : Project Research Support Officer Requirements 1. Master of Science degree in either Soil Sciences, Agronomy, Land and Water Management, climate change 2. Minimum of three years relevant research experience post Masters degree 3. MS Office programs, internet explorer, Cloud storage applications, primary and secondary data collection and extraction. 4. Working knowledge of ArcGIS, GENSTAT, R programming language, and Atlas.ti, Kobo toolbox, SPSS. Location : Nairobi, KE Deadline : 19 August 2024 Apply opportunitiesforyoungkenyans… #IkoKaziKE

Statistics as a subject is lucky in that it gets to use the software as a scapegoat. The student will be inspired to say they hate R / GenStat / Prism / Excel / STATA / Matlab / SPSS more than statistics itself.

#statistics/#data people: ran Simple Linear Regression in GenStat 2 see relations between weather & # of #species in a #MothTrap. Precip unexpected; p-val of Regression is 0.5 p-val of Constant is <.001 p-val of Product is 0.5. This means precip has effect but is not main factor?

2/2) This is a paper that describes the issues ncbi.nlm.nih.gov/pmc/article… My educational approach would be to get everybody to use @Genstat , which makes these matters clear. Here's a blog (or perhaps a rant) linkedin.com/pulse/standard-…

My favourite error because so big, so obvious and so obviously wrong is imagining that you can sample from the future.

haha..I like that answer :)

I think two key points from the paper are a) it is often safer to compare predictions than parameter estimates b) predictions depend on purpose and context. A cause for caution however is that experiments don’t involve sampling from target populations.