ON STEPWISE MULTIPLE LINEAR REGRESSION

1967 ◽  
Author(s):  
Harold J. Breaux
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stepwise Multiple Linear Regression

scholarly journals Comparison of muscle quality and functional capacity between Japanese and Brazilian older individuals

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243589
Author(s):  
Hiroshi Akima ◽  
Akito Yoshiko ◽  
Régis Radaelli ◽  
Madoka Ogawa ◽  
Kaori Shimizu ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Muscle Quality ◽  
Older Individuals ◽  
Stepwise Multiple Linear Regression ◽  
Independent Variables

Muscle quality is well-known to decrease with aging and is a risk factor for metabolic abnormalities. However, there is a lack of information on race-associated differences in muscle quality and other neuromuscular features related to functional performance. This study aimed to compare muscle quality, function, and morphological characteristics in Japanese and Brazilian older individuals. Eighty-four participants aged 65–87 years were enrolled in the study (42 Japanese: 23 men, 19 women, mean age 70.4 years; 42 Brazilians: 23 men, 19 women, mean age 70.8 years). Echo intensity (EI) and muscle thickness (MT) of the quadriceps femoris were measured using B-mode ultrasonography. A stepwise multiple linear regression analysis with EI as a dependent variable revealed that MT was a significant variable for Japanese participants (R2 = 0.424, P = 0.001), while MT and subcutaneous adipose tissue (SCAT) thickness were significant variables for Brazilian participants (R2 = 0.490, P = 0.001). A second stepwise multiple linear regression analysis was performed after excluding MT and SCAT thickness from the independent variables. Sex and age for Japanese participants (R2 = 0.381, P = 0.001) and lean body mass and body mass index for Brazilian participants (R2 = 0.385, P = 0.001) were identified as significant independent variables. The present results suggest that MT is closely correlated with muscle quality in Japanese and Brazilian older individuals. Increases in muscle size may induce decreases in intramuscular adipose tissue and/or connective tissues, which are beneficial for reducing the risks of metabolic impairments in Japanese and Brazilian older individuals.

Global Burden of Cataract and Its Association With Socioeconomic Development Status, 1990-2019

2021 ◽  
Author(s):  
Xiaotong Han ◽  
Minjie Zou ◽  
Zhenzhen Liu ◽  
Yi Sun ◽  
Charlotte Aimee Young ◽  
...  
Keyword(s):  
Linear Regression ◽  
Visual Impairment ◽  
Multiple Linear Regression ◽  
Disease Burden ◽  
Multiple Linear Regression Model ◽  
Global Burden ◽  
Disability Adjusted Life Years ◽  
Stepwise Multiple Linear Regression ◽  
Disability Adjusted Life ◽  
Life Years

Abstract Background: Cataract is the leading cause of blindness globally and more people will be at risk for this common cause of vision loss in the coming years.To estimate the disease burden of cataract and evaluate contributions of various risk factors to cataract-associated disability-adjusted life years (DALYs).Methods: Prevalence of visual impairment due to cataract and disability-adjusted life years of cataract were extracted from the Global Burden of Disease (GBD) study 2019 to explore time trends and annual changes. Regional and country-level socioeconomic indexes were acquired from open databases, and stepwise multiple linear regression was used to evaluate associations between age-standardized rate of DALYs of cataract and potential predictors.Results: Global Prevalence rate of visual impairment due to cataract rose by 58.45% from 791.4 per 100,000 population (95%CI: 705.2 to 890.0 per 100,000 population) to 1253.9 per 100,000 population 95%CI: 1103.3 to 1417.7 per 100,000 population) in 2019 and DALYs rate of cataract rose by 32.18% from 65.3 per 100,000 population (95%CI: 46.4 to 88.2 per 100,000 population) in 1990 to 86.3 per 100,000 population (95%CI: 61.5 to 116.4 per 100,000 population) in 2019.Stepwise multiple linear regression model showed that higher refractive error prevalence (β=0.036, 95% CI: 0.022, 0.050, P<0.001), lower number of physicians per 10,000 population (β=-0.959, 95% CI: -1.685, -0.233, P=0.010) and lower level of HDI (β=-134.93, 95% CI: -209.84, -60.02, P=0.001) were associated with higher disease burden of cataract.Conclusions: Substantial increases in the prevalence of visual impairment and DALYs of cataract were observed from 1990 to 2019. Successful global initiatives targeting improving cataract surgical rate and quality, especially in regions with lower socioeconomic status, is a prerequisite to combating this growing burden of cataract in the aging society.

scholarly journals Estimation of the instantaneous peak flow from maximum daily flow: a comparison of three methods

2014 ◽  
Vol 46 (5) ◽  
pp. 671-688 ◽  
Author(s):  
J. Ding ◽  
U. Haberlandt ◽  
J. Dietrich
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Peak Flow ◽  
Linear Regression Analysis ◽  
Extreme Value Distribution ◽  
Maximum Flow ◽  
Multiple Linear Regression Analysis ◽  
Generalized Extreme Value Distribution ◽  
Stepwise Multiple Linear Regression ◽  
Scaling Properties

Three different methods are compared to estimate the instantaneous peak flow (IPF) from the corresponding maximum daily flow (MDF), as the daily data are more often available at gauges of interest and often with longer recording periods. In the first approach, simple linear regression is applied to calculate IPF from MDF values using probability weighted moments and quantile values. In the second method, the use of stepwise multiple linear regression analysis allows to identify the most important catchment descriptors of the study basin. The resulting equation can be applied to transfer MDF into IPF. With the third method, the temporal scaling properties of annual maximum flow series are investigated based on the hypothesis of piece wise simple scaling combined with the generalized extreme value distribution. The scaling formulas developed from three 15 min stations in the Aller-Leine river basin of Germany are transferred to all daily stations to estimate the IPF. The method based on stepwise multiple linear regression gives the best results compared with the other two methods. The simple regression method is the easiest to apply given sufficient peak flow data, while the scaling method is the most efficient method with regard to data use.

scholarly journals A Comparative Study of the Peak Expiratory Flow Rate of Indian and Nepalese Young Adults in a Teaching Institute

2009 ◽  
Vol 47 (169) ◽  
Author(s):  
Parthasarathi Debray ◽  
B M Shreevatsa ◽  
R B MG ◽  
T K Sen ◽  
S Roy ◽  
...  
Keyword(s):  
Young Adults ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Adult Population ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Prediction Equation ◽  
Regression Equation ◽  
Stepwise Multiple Linear Regression ◽  
The Mean

Peak expiratory fl ow rate (PEFR) measurement is the easiest and cheapest method to evaluaterespiratory functions. So, the study was carried out to evaluate PEFR of healthy Nepalese adults andcompare their values with healthy Indian counterparts to know whether Indian prediction equationsfor PEFR can be used for Nepalese adult population or not.One hundred twenty-three healthy, young, non smoker adult Indian (64: 28 Males, 36 Females) andNepalese (59: 32 Males, 27 Females) medical students of 18 to 20 years of age participated in the study.The mean PEFR of Indian (male: 490.4 liter/min, female: 386.0 liter/min) and Nepalese (male: 485.9liter/min, Female: 365.2 liter/min) young adults were found to have no signifi cant differences.As there is no signifi cant difference in the mean PEFR of Indian and Nepalese young adults, predictionequations made for Indian adults can be used to predict PEFR of Nepalese subjects. Therefore, anattempt has been made to formulate a regression equation from the combined Indian and Nepalesesubjects. A stepwise, multiple, linear, regression analysis was performed for this purpose. The analysisshowed that height is the best predictor for PEFR in the present study. The regression equation basedon height for the combined Indian and Nepalese young adults is calculated as: PEFR = 5.687 × Height(cm) – 495.787. However, a stepwise, multiple, linear, regression equation with residual analysisfor the best fi t model was performed to formulate prediction equation for PEFR and this showed achange of the earlier regression equation to PEFR = 5.930 × Height (cm) – 536.131.Keywords: Peak expiratory fl ow rate, peak fl ow meter, prediction equation, spirometry, youngadults

Sign in / Sign up

Export Citation Format

Share Document