Elements for Building Supervised Statistical Machine Learning Models

This chapter gives details of the linear multiple regression model including assumptions and some pros and cons, the maximum likelihood. Gradient descendent methods are described for learning the parameters under this model. Penalized linear multiple regression is derived under Ridge and Lasso penalties, which also emphasizes the estimation of the regularization parameter of importance for its successful implementation. Examples are given for both penalties (Ridge and Lasso) and but not for penalized regression multiple regression framework for illustrating the circumstances when the penalized versions should be preferred. Finally, the fundamentals of penalized and non-penalized logistic regression are provided under a gradient descendent framework. We give examples of logistic regression. Each example comes with the corresponding R codes to facilitate their quick understanding and use.

The Use of Econometric Analysis in Forecasting Macroeconomic Processes

The article discusses the issues related to the influence of various price categories of goods on the level of inflation. The possibility of using econometric analysis for predictive purposes is considered. An econometric model of multiple regression of the influence of consumer prices on the level of inflation is formed. A linear multiple regression equation is constructed. The selection of factors for the construction of an econometric model is made. The main groups of goods are identified, the prices of which form the level of inflation and can have a primary impact. The viability of the model and the possibility of forecasting macroeconomic indicators based on econometric analysis are proved.

Evaluation and reparametrization of mathematical models for prediction of the leaf area of Megathyrsus maximus cv. BRS Zuri

The aim of this study was to verify the precision and accuracy of 5 models for leaf area prediction using length and width of leaf blades of Megathyrsus maximus cv. BRS Zuri and to reparametrize models. Data for the predictor variables, length (L) and width (W) of leaf blades of BRS Zuri grass tillers, were collected in May 2018 in the experimental area of Embrapa Gado de Corte, Mato Grosso do Sul, Brazil. The predictor variables had high correlation values (P<0.001). In the analysis of adequacy of the models, the first-degree models that use leaf blade length (Model A), leaf width × leaf length (Model B) and linear multiple regression (Model C) promoted estimated values similar to the leaf area values observed (P>0.05), with high values for determination coefficient (>80%) and correlation concordance coefficient (>90%). Among the 5 models evaluated, the linear multiple regression (Model C: β0 = -5.97, β1 = 0.489, β2 = 1.11 and β3 = 0.351; R² = 89.64; P<0.001) and as predictor variables, width, length and length × width of the leaf blade, are the most adequate to generate precise and exact estimates of the leaf area of BRS Zuri grass.

PENGARUH HUMAN RELATION, IKLIM ORGANISASI DAN ETOS KERJA TERHADAP KINERJA PEGAWAI DINAS

: This research goal is to analyze how big the influence of humanrelation, organizational climate, work ethic to eployee’s performance in DinasPendidikan Banjarbaru City. The method used in this research is quantitativemethod, with 158 population, 40 samples, and the analyzing technique used arevalidity test, reliability test, and linear multiple regression. The result of thisresearch show that human relation variable, organizational climate, and work ethicindependently influence eployee’s performance in Dinas Pendidikan BanjarbaruCity. Human relation is the dominant variable to influence eployee’s performancein Dinas Pendidikan Banjarbaru City. Meanwhile, by analyzing with F Test on 5%alpha shows that all the independent variable simultaneously influence dependentvariable. Keywords : Human Relation, Organizational Climate, Work Ethic Abstrak : Penelitian ini bertujuan untuk mengetahui dan menganalisis seberapabesar Pengaruh Human Relation, Iklim Organisasi dan Etos Kerja TerhadapKinerja Pegawai Dinas Pendidikan Kota Banjarbaru. Metode yang digunakandalam penelitian ini adalah metode kuantitatif, dengan populasi sebanyak 158,sampel 40 orang dan teknik analisis data dengan instrument penelitian ujivaliditas, uji reliabilitas, dan menggunakan regresi linear berganda, Hasilpenelitian ini menunjukkan bahwa variabel Human Relation, Iklim Organisasi danEtos Kerja secara sendiri - sendiri berpengaruh terhadap Kinerja Pegawai DinasPendidikan Kota Banjarbaru, Sedangkan variabel Human Relation merupakanvariabel yang dominan mempengaruhi terhadap Kinerja Pegawai DinasPendidikan Kota Banjarbaru, Sementara itu berdasarkan hasil Uji F pada tingkat αsebesar 5% membuktikan semua variabel bebas tersebut memiliki pengaruhsignifikan terhadap variabel terikat secara bersama-sama (simultan). Kata kunci : Human Relation, Iklim Organisasi, Etos Kerja

D-optimal experimental designs for linear multiple regression under heteroscedastic observations

The problem of construction of «continuous» (number of observations is not fixed) and «exact» (number of observations is fixed) D-optimal experimental designs for linear multiple regression in the case when variance of errors of observations depends on regressor value is studied in this paper. Families of functions that determine heteroscedastic observations are found for which it is possible to construct «continuous» and «exact» D-optimal experimental designs. «Continuous» D-optimal experimental designs under four different types of heteroscedasticity are constructed for linear multiple regression with three regressors.

COVID-19 in Italy: Is the Virus Running Through an Ancient Roman road?

In late February 2020, Coronavirus disease-2019 (COVID-19) has aggressively spread around many bordering provinces of the three most productive regions of northern Italy (Lombardy, Veneto and Emilia Romagna). The first outbreak exploded in the municipality of Codogno (Lombardy). The province of Ferrara (Emilia Romagna) has been indicated as an anomaly due to the lower number of confirmed cases (1&middot;065 cases per 1000 population). We argue that the spread of the virus throughout Emilia Romagna has a possible explanation in the geographical location of most of its provinces along the Via Emilia, an ancient Roman road that runs throughout the region, which we consider as a proxy for citizens&rsquo; movement, number of contacts, and social interactions. In order to test this hypothesis, we used a non-linear multiple regression analysis on aggregate province data to investigate the association between the rates of confirmed cases and the distance from the Via Emilia. The results indicate that the infection rate decreases proportionally to the distance from Via Emilia (-14% every 10 km, p&lt;0&middot;001). Further studies are needed, but Ferrara&rsquo;s &ldquo;peculiarity&rdquo; might have a geographical/behavioral explanation, due to its isolation from the regional main connection routes, which are still revolving around a road built by the ancient Romans 2,000 years ago.