scholarly journals Adapting Hellwig’s method for selecting concomitant variables under a certain growth curve model

2018 ◽  
Vol 55 (2) ◽  
pp. 139-146
Author(s):  
Mirosława Wesołowska-Janczarek ◽  
Monika Różańska-Boczula
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Growth Curve ◽  
Multiple Regression Model ◽  
Growth Curve Model ◽  
Concomitant Variables ◽  
Curve Model ◽  
Theoretical Considerations ◽  
Over Time ◽  
Selection Of

SummaryThis paper presents an application of Hellwig’s method for selecting concomitant variables under a growth curve model, where the values of the concomitant variables change over time and are the same for all experimental units. The authors present a simple adaptation of the growth curve model to the multiple regression model for which Hellwig’s method applies. The theoretical considerations are applied to the selection of significant concomitant variables for raspberry fruiting.

Selection of covariables in the growth curve model

Biometrika ◽  
1991 ◽  
Vol 78 (4) ◽  
pp. 779-785 ◽  
Author(s):  
YASUNORI FUJIKOSHI ◽  
C. RADHAKRISHNA RAO
Keyword(s):  
Growth Curve ◽  
Growth Curve Model ◽  
Curve Model ◽  
Selection Of

scholarly journals THE MULTIPLE REGRESSION MODEL WITH DICHOTOMOUS VARIABLES IN ANALYSIS OF MULTI-SECTIONAL DEMAND FOR CONNECTIVITY SERVICES – APPROACH BASED ON PER SECOND BILLING

2021 ◽  
Vol 20 (2) ◽  
pp. 47-57
Author(s):  
Paweł Kaczmarczyk
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Explanatory Variables ◽  
First Order ◽  
Forecasting Method ◽  
Telephone Calls ◽  
Dichotomous Variables ◽  
Short Term Forecasting ◽  
Selection Of

The article presents the results of comparative research of the effectiveness of two types of models in terms of approximation and short-term forecasting of the multi-sectional demand for connectivity services. The presented results of the analyses are related to the selection of an appropriate forecasting method as an element of the Prediction System dedicated to telecommunications operators. The first tested model was a multiple regression model with dichotomous explanatory variables. The second model was a multiple regression model with dichotomous explanatory variables and autoregression. In both models, the dependent variable was the hourly counted seconds of outgoing calls within the network of the selected operator. Telephone calls were analysed in terms of such classification factors as: type of day, category of call, group of subscribers. Taking into account all levels of classification factors of the explanatory variable, 35 dichotomous explanatory variables were specified. The defined set of dichotomous explanatory variables was used in the estimation process of both compared regression models. However, in the second model, first-order autoregression was additionally applied. The second model (multiple regression model with dichotomous explanatory variables with first-order autoregression) was found to have higher approximation and predictive capabilities than the first model (multiple regression model with dichotomous explanatory variables without autoregression).

Problem gambling symptomatology and alcohol misuse among adolescents: A parallel-process latent growth curve model.

2017 ◽  
Vol 31 (4) ◽  
pp. 447-456 ◽  
Author(s):  
Seema Mutti-Packer ◽  
David C. Hodgins ◽  
Nady el-Guebaly ◽  
David M. Casey ◽  
Shawn R. Currie ◽  
...  
Keyword(s):  
Problem Gambling ◽  
Growth Curve ◽  
Alcohol Misuse ◽  
Parallel Process ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Mental health outcomes in military veterans: A latent growth curve model.

2018 ◽  
Vol 24 (3) ◽  
pp. 228-235 ◽  
Author(s):  
Justin T. McDaniel ◽  
Kate H. Thomas ◽  
David L. Albright ◽  
Kari L. Fletcher ◽  
Margaret M. Shields
Keyword(s):  
Mental Health ◽  
Health Outcomes ◽  
Growth Curve ◽  
Military Veterans ◽  
Latent Growth Curve ◽  
Mental Health Outcomes ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Decreased COVID-related adaptive behavior and increased negative affect: A multivariate latent growth curve model

2021 ◽  
pp. 135910532110216
Author(s):  
Hai-Ping Liao ◽  
Xiao-Fu Pan ◽  
Xue-Qin Yin ◽  
Ya-Fei Liu ◽  
Jie-Yang Li ◽  
...  
Keyword(s):  
Negative Affect ◽  
Adaptive Behavior ◽  
Growth Curve ◽  
Initial Level ◽  
Behavioral Changes ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Data from a longitudinal questionnaire investigation of three time waves were used to investigate affective and behavioral changes and their covariant relationship among Chinese general population during the COVID-19 pandemic from March to May 2020. 145 participants aging from 15 to 63 completed three waves of survey. Latent growth curve analyses found that negative affect gradually increased as the pandemic continued. A faster increase in negative affect was related to a greater decrease in adaptive behavior and faster increase in non-adaptive behavior. A higher initial level of negative affect was related to a slower increase in non-adaptive behavior.

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