Self-efficacy of the professional: non-linear regression model analysis

2019 ◽  
pp. 41-48
Author(s):  
N. Laptieva ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Self Efficacy ◽  
Model Analysis ◽  
Non Linear

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

scholarly journals Gender Difference in General Self-Efficacy among Young-Old Elderly Aged 60–74 in Rural Shandong China: A Cross-Sectional Survey

2019 ◽  
Vol 16 (24) ◽  
pp. 5070
Author(s):  
Yali Wang ◽  
Lingzhong Xu ◽  
Wenzhe Qin ◽  
Jiao Zhang ◽  
Yu Xia ◽  
...  
Keyword(s):  
Gender Difference ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Self Efficacy ◽  
Multiple Linear Regression Model ◽  
Cross Sectional Survey ◽  
General Self Efficacy ◽  
Young Old

Objective: This study aims to explore the determinants of general self-efficacy (GSE) among young-old elderly, with focus on examining the gender difference of general self-efficacy. Methods: Data were collected from the 2017 Survey of the Shandong Elderly Family Health Service, which was conducted by Shandong University. T-test was used to examine the gender difference in GSE. Univariate models and adjusted multiple linear regression model were used to explore the determinants of GSE by gender. Results: The females’ GSE score was lower than that of male participants (26.1 ± 8.1 vs. 28.7 ± 7.7), and there was a significant gender difference (t = 10.877, p < 0.001). Multiple linear regression model showed that some factors are common significant determinants of GSE such as age, education level, activity of daily living (ADL), self-rated health, mental health, personality, and whether participants have intimate friends and interpersonal relationships. Hypertension and frequent communication with children were specific determinants of GSE among male young-old. Personal income was a specific determinant of female participants. Conclusion: Some influencing factors of GSE in both genders are identical, the others are different. More attention should be paid for the poor young-old females, young-old males with hypertension, and disabled young-old people.

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