Quant Research

16/03/2026

01/07/2025

Zbog izuzetno velikog interesa ponavljamo stručni skup na temu darovitog djeteta koji će se održati u ponedjeljak, 15. rujna 2025. godine, u Osnovnoj školi Antuna Gustava Matoša u Zagrebu, a namijenjen je odgojiteljima, učiteljima, stručnim suradnicima, ravnateljima dječjih vrtića i osnovnih škola. Polaznici će osim osnovnih znanja na temu darovitosti i odgojno-obrazovne podrške darovitom djetetu, dobiti i vrijedne primjere tehnika i vještina za poticanje kreativnosti i misaonih procesa kod djece.

U prilogu se nalazi info letak o edukaciji, poziv na edukaciju i prijavnica te vas molimo da proslijedite ovaj mail svim zainteresiranima u vašoj ustanovi.

Veselimo se zajedničkom učenju i suradnji!

Srdačan pozdrav,
QUANT RESEARCH d.o.o.

15/03/2025

Poštovani,

pozivamo vas na edukaciju na temu darovitog učenika koja će se održati 29. travnja 2025. godine u Područnoj školi Pongračevo u Zagrebu, a namijenjena je odgojiteljima, učiteljima, stručnim suradnicima, ravnateljima dječjih vrtića i osnovnih škola kao i zainteresiranim roditeljima. Polaznici će osim osnovnih znanja na temu darovitosti i odgojno-obrazovne podrške darovitom djetetu, dobiti i vrijedne primjere tehnika i vještina za poticanje kreativnosti i misaonih procesa kod djece.

U prilogu se nalazi poziv na edukaciju i prijavnica te vas molimo da proslijedite ovaj mail svim zainteresiranima u vašoj ustanovi.

Veselimo se zajedničkom učenju i suradnji!

Srdačan pozdrav,

QUANT RESEARCH d.o.o.
OIB 71189480415 MBS 081536210
IBAN: HR6123400091111253732
https:// quant-research-cooperation.com
e-mail: [email protected]
Tel: 099/2550-337
prof.dr.sc. Siniša Opić

24/02/2025

These tiny things will change life as we know it

01/02/2025

Quant Research Ltd. operates in Sydney

26/01/2025

LASSO and RIDGE Linear regressions & L1 and L2
regularizations - the impact of the penalty term (in publishing, author - Sinisa Opic

Summary - Lasso and Ridge regressions are useful enhancements to ordinary least squares (OLS) regression, utilizing L1 and L2 regularization techniques. The main idea behind these methods is to balance the simplicity of the model with its accuracy by adding a penalty term to the existing regression formula. This approach leads to a significant reduction in the regression coefficients, potentially reducing some to zero (in the case of L1 regularization). These enhancements improve accuracy, increase R², lower mean squared error (MSE), eliminate non-significant coefficients, address the issue of multicollinearity among predictors, and most importantly, help prevent overfitting. In the empirical section of the study, both L1 (LASSO) and L2 (RIDGE) regression analyses were conducted on a dataset comprising 156 samples. A cross-validation procedure was employed with λ values ranging from 1 to 20 (with λmax=20, λmin = 0.5, and λ intervals of 1), using 5 folds. The results indicated that the application of Ridge regression, when compared to OLS, led to a reduction in the Beta coefficient, an increase in R², and a decrease in the Mean Squared Error (MSE). Furthermore, no significant discrepancies were found between the R² values for the training and holdout datasets, suggesting that the model is robust. However, in comparison, L1 regularization (LASSO) was performed, and the values of all betas for all categories of predictor variables are zero (βstd=0; B=0);∑_(i=0,5)^20 β=0 which indicates the impossibility of interpreting the regression model (type 2 error). LASSO and RIDGE regressions are iterative procedures, and their results depend on randomization, the number of k-fold cross-validations and k-iterations, the percentage of training and testing data, and the value of lambda (although this is a minor problem), so you should be careful when using them.

Keywords: Lasso regression, L1, L2 regularization, MSE, overfitting, penalty term, Ridge regression, The sum of squared residuals,

in publishing - prof.dr.sc.Sinisa Opic

30/12/2024

Od ovog trenutka imamo 100 pratitelja! Hvala vam na kontinuiranoj podršci. Bez vas to ne bi bilo moguće.

27/12/2024

Statističke obrade - Quant research Ltd
Najsloženije statističke obrade iz područja društvenih, tehničkih, medicinskih znanosti, STEM; supervizija međunarodnih projekata, data analysis znanstvenih radova; SPSS, Statistic, AMOS; R, Phyton... parametric/non-parametric test, robust; univariate/multivariate approaches; linear vs nonlinear; MANOVA (n way), ANOVA (factorial), Descriptive, CFA, EFA (PCA FA...), Regressions (OLS, Lasso, Ridge; regulations L1, L2), SEM (CFA, mediation, moderations/bootstrapping), bayesian (ML); Log-linear (qualitative data); ANCOVA (n way), Longitudinal Canonical Correlation Analysis (LCCA), MANCOVA (n way)... RASCH measurement theory, Cox regression (survival analysis, Proportional Hazards Model, Fratility), Testiranje hipoteza NHST, proširenje na Polystohastic statistical inference). Izrada mjernih instrumenata, testiranje metrijskih karakteristika, kontrola bias-a, related samples, determiniranje probabilističkih uzoraka...
Suradnici Quant Research d.o.o su sveučilišni profesori (statistics, quantitative methodology, data mining) s niza prestižnih sveučilišta u svijetu te eksperti, matematičari u području obrade podataka u gospodarstvu (gaming,aktuarska matematika).
Poseban dio odnosi se na SVMs, non-parametric Kernel regression, Neural Networks (CNN, GNN, RNN...) u području složenih prediktivnih modela)

Tražimo suradnike statističare/ qualitative and quantitative methodology/ za suradnju (službeni jezik engleski). Pošaljite CV na mail [email protected]