A proposed Method for the Analysis of Multiple Regression using Artificial Intelligence

2018 ◽  
Vol 19 (1) ◽  
pp. 1-39
Author(s):  
Mohamed Abdel Salam Agamy
Keyword(s):  
Artificial Intelligence ◽  
Multiple Regression

Research on influencing factors of stock returns based on multiple regression and artificial intelligence model

2020 ◽  
pp. 1-11
Author(s):  
Ying Han
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Stock Returns ◽  
Multiple Regression ◽  
Simulation Analysis ◽  
Five Factor Model ◽  
Stock Management ◽  
Analysis Model ◽  
Data Simulation ◽  
Factor Analysis Model

When choosing stock investment, there are many stock companies, and the stock varieties are also complicated. At present, there are various systems for evaluating stock performance in the market, but there is no uniform standard, so investors often cannot effectively invest in stocks. Simultaneously, stock management companies also have their own characteristics, and there are differences in shareholding structure and internal management structure. Based on this, based on multiple regression models and artificial intelligence models, this paper constructs a stock return influencing factor analysis model to statistically describe the sample data and factor data, and tests the applicability of the five-factor model for performance evaluation of mixed stocks. In addition, this article combines the actual situation to carry out data simulation analysis and uses a five-factor analysis model to carry out quantitative research on stock returns. Through data simulation analysis, we can see that the model constructed in this paper has a certain effect in the analysis of factors affecting stock returns.

Language and Literacy Predictors of Dialect Density Among School-Age African American Children From Two Geographic Regions

2020 ◽  
Vol 51 (3) ◽  
pp. 807-820
Author(s):  
Lena G. Caesar ◽  
Marie Kerins
Keyword(s):  
African American ◽  
Multiple Regression ◽  
Oral Language ◽  
East Coast ◽  
Geographic Region ◽  
Literacy Skills ◽  
School Age ◽  
Regression Analyses ◽  
Geographic Regions ◽  
American Children

Purpose The purpose of this study was to investigate the relationship between oral language, literacy skills, age, and dialect density (DD) of African American children residing in two different geographical regions of the United States (East Coast and Midwest). Method Data were obtained from 64 African American school-age children between the ages of 7 and 12 years from two geographic regions. Children were assessed using a combination of standardized tests and narrative samples elicited from wordless picture books. Bivariate correlation and multiple regression analyses were used to determine relationships to and relative contributions of oral language, literacy, age, and geographic region to DD. Results Results of correlation analyses demonstrated a negative relationship between DD measures and children's literacy skills. Age-related findings between geographic regions indicated that the younger sample from the Midwest outscored the East Coast sample in reading comprehension and sentence complexity. Multiple regression analyses identified five variables (i.e., geographic region, age, mean length of utterance in morphemes, reading fluency, and phonological awareness) that accounted for 31% of the variance of children's DD—with geographic region emerging as the strongest predictor. Conclusions As in previous studies, the current study found an inverse relationship between DD and several literacy measures. Importantly, geographic region emerged as a strong predictor of DD. This finding highlights the need for a further study that goes beyond the mere description of relationships to comparing geographic regions and specifically focusing on racial composition, poverty, and school success measures through direct data collection.

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence

The Relationship of Compliance with Coping Strategies and Self-Esteem

2003 ◽  
Vol 19 (2) ◽  
pp. 117-123 ◽  
Author(s):  
Gisli H. Gudjonsson ◽  
Jon Fridrik Sigurdsson
Keyword(s):  
Sex Differences ◽  
Coping Strategies ◽  
Multiple Regression ◽  
Sex Difference ◽  
Test Scores ◽  
Self Esteem ◽  
Stressful Situation ◽  
Men And Women ◽  
Relationship Of ◽  
The Relationship

Summary: The Gudjonsson Compliance Scale (GCS), the COPE Scale, and the Rosenberg Self-Esteem Scale were administered to 212 men and 212 women. Multiple regression of the test scores showed that low self-esteem and denial coping were the best predictors of compliance in both men and women. Significant sex differences emerged on all three scales, with women having lower self-esteem than men, being more compliant, and using different coping strategies when confronted with a stressful situation. The sex difference in compliance was mediated by differences in self-esteem between men and women.

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