Strategic Aspects of Numerosity Judgment: The Effect of Task Characteristics

2003 ◽  
Vol 50 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Koen Luwel ◽  
Lieven Verschaffel ◽  
Patrick Onghena ◽  
Erik De Corte
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Strategic Change ◽  
Strategy Use ◽  
Point Of View ◽  
Cognitive Tasks ◽  
Task Characteristics ◽  
Linear Regression Models ◽  
Grid Analysis ◽  
Numerosity Judgment

In previous investigations we documented that people use several strategies to determine different numerosities of blocks that are presented in a square grid. One of these strategies is the clever subtraction strategy, wherein the number of empty squares in the grid is subtracted from the total number of squares in the grid. In the present study we investigated participants’ flexibility in strategy use when varying the shape of the grid. Analysis of the results in terms of the theoretical framework of Lemaire and Siegler (1995 ) regarding strategic change shows that this contextual variable affected the frequency, execution time, and accuracy of subjects’ use of the subtraction strategy. The usefulness of this framework for analyzing the nature of the adaptation to contextual variations is discussed. From a methodological point of view, this study documents the potential of Beem’s (1993 , 1999) segmented linear regression models for assessing subjects’ strategy use in cognitive tasks.

FUZZY LINEAR REGRESSION ANALYSIS FROM THE POINT OF VIEW RISK

2004 ◽  
Vol 12 (05) ◽  
pp. 635-649 ◽  
Author(s):  
MOHAMMAD MODARRES ◽  
EBRAHIM NASRABADI ◽  
MOHAMMAD MEHDI NASRABADI
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Fuzzy Numbers ◽  
Linear Regression Analysis ◽  
Point Of View ◽  
Risk Averse ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression ◽  
Risk Seeking ◽  
The Difference

In this paper, fuzzy linear regression models with fuzzy/crisp output, fuzzy/crisp input are considered. In this regard, we define risk-neutral, risk-averse and risk-seeking fuzzy linear regression models. In order to do that, two equality indices are applied to express the degree of equality between a pair of fuzzy numbers. We also develop three mathematical models to obtain the parameters of fuzzy linear regression models. Minimizing the difference between the total spread of the observed and estimated values is the objective of these models. The advantage of our proposed models is the simplicity in programming and computation.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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