Multiple linear regression with correlations among the predictor variables. Theory and computer algorithm ridge (FORTRAN 77)

1990 ◽  
Vol 16 (7) ◽  
pp. 933-952 ◽  
Author(s):  
P.F.M. van Gaans ◽  
S.P. Vriend
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Computer Algorithm ◽  
Predictor Variables ◽  
Fortran 77

scholarly journals Husband-wife Roles as a Correlate of Contraceptive and Fertility Behaviour

1996 ◽  
Vol 35 (2) ◽  
pp. 145-170 ◽  
Author(s):  
Muhammad Iqbal Zafar
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Child Mortality ◽  
Family Income ◽  
Population Policy ◽  
Reproductive Behaviour ◽  
Women's Status ◽  
Predictor Variables ◽  
Women’S Status ◽  
Fertility Behaviour

In this paper, an investigation of reproductive behaviour within the socioeconomic and cultural frameworks is carried out to find the extent to which socioeconomic, cultural, and attitudinal variables (such as husband and wife’s education, family income, husband’s occupation, child mortality, exposure to the mass media, and husbandand- wife relationship in terms of egalitarian roles, role-segregation, husband’s authority, and domination in family and non-family decisions) influence the fertility decision-making process. The quantitative and qualitative techniques are used for exploring the respondents’ views regarding contraceptive and fertility behaviour. Principle Component Analysis (PCA) is applied to identify new meaningful underlying variables and to reduce the multi-dimensionality of variables. The chi-square test is employed to explore the relationships between the predictor variables and the dependent variables. Multiple linear regression is also used to establish the relative importance of each of the predictor variables. Bivariate, multiple linear regression and qualitative analysis demonstrate that preferences for smaller families and contraceptive use were found to be consistently associated with modern attitudes and behaviour towards the husband-and-wife relationship. Family income, husband’s occupation, child mortality, and age at marriage offered no explanation of the reproductive behaviour. It is concluded that cultural setting and tradition exert an important influence on reproductive behaviour independent of development in economic realities. It is suggested that for the attainment of demographicdevelopmental objectives, the issue of women’s status is not incidental; it is essential. The argument is not that improvements in women’s status need to be pursued only for population policy purposes, but rather that they comprise a crucial social developmental goal in their own right.

scholarly journals Naturalist intelligence and personality: An understanding students’ responsible enviromental behavior

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Nina Wirdianti ◽  
Ratna Komala ◽  
Mieke Miarsyah
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Environmental Problems ◽  
Research Data ◽  
Predictor Variables ◽  
Essential Step ◽  
Correlational Approach ◽  
Responsible Environmental Behavior ◽  
Quantitative Descriptive ◽  
Descriptive Method

Notwithstanding that many efforts to overcome environmental problems have been carried out by several parties, yet the issues still occur. Improving students’ responsible environmental behavior (REB) can be an alternative to solve environmental problems. This study aimed to analyze the relation between the both variables (i.e. naturalist intelligence and personality) and students’ REB at SJHS 51 of Bandung. The research was carried out using quantitative descriptive method through a correlational approach. Naturalist intelligence, personality, and REB data were collected using questionnaires. The research data were analyzed using multiple linear regression at α = 0.05. The research results showed that there was a relation between: (1) naturalist intelligence and REB;. (2) personality and REB; and (3) the both predictor variables and REB. Therefore, empowering the both competencies (naturalist intelligence and personality) is the essential step to improve students’ REB. 

scholarly journals PEMODELAN REGRESI ROBUST S-ESTIMATOR UNTUK PENANGANAN PENCILAN MENGGUNAKAN GUI MATLAB (Studi Kasus : Faktor-Faktor yang Mempengaruhi Produksi Ikan Tangkap di Jawa Tengah)

2019 ◽  
Vol 8 (1) ◽  
pp. 81-92
Author(s):  
Dhea Kurnia Mubyarjati ◽  
Abdul Hoyyi ◽  
Hasbi Yasin
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Predictor Variables ◽  
Fish Production ◽  
Significance Level ◽  
Central Java ◽  
Approximate Estimation

Multiple Linear Regression can be solved by using the Ordinary Least Squares (OLS). Some classic assumptions must be fulfilled namely normality, homoskedasticity, non-multicollinearity, and non-autocorrelation. However, violations of assumptions can occur due to outliers so the estimator obtained is biased and inefficient. In statistics, robust regression is one of method can be used to deal with outliers. Robust regression has several estimators, one of them is Scale estimator (S-estimator) used in this research. Case for this reasearch is fish production per district / city in Central Java in 2015-2016 which is influenced by the number of fishermen, number of vessels, number of trips, number of fishing units, and number of households / fishing companies. Approximate estimation with the Ordinary Least Squares occur in violation of the assumptions of normality, autocorrelation and homoskedasticity this occurs because there are outliers. Based on the t- test at 5% significance level can be concluded that several predictor variables there are the number of fishermen, the number of ships, the number of trips and the number of fishing units have a significant effect on the variables of fish production. The influence value of predictor variables to fish production is 88,006% and MSE value is 7109,519. GUI Matlab is program for robust regression for S-estimator to make it easier for users to do calculations. Keywords: Ordinary Least Squares (OLS), Outliers, Robust Regression, Fish Production, GUI Matlab.

scholarly journals Revisit Intention Regression Model of Agritourism Destinations of Pelaga and Buyan-Tamblingan, Bali, Indonesia

2021 ◽  
Vol 12 (6) ◽  
pp. 2504-2512
Author(s):  
I Gusti Bagus Rai Utama, Christimulia Purnama Trimurti, Ni Putu Dyah Krismawintari, I Wayan Ruspendi Junaedi
Keyword(s):  
Statistical Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Research Study ◽  
Predictor Variables ◽  
Revisit Intention ◽  
Influence Variable ◽  
Close Distance

This investigation was driven in Pelaga and Buyan-Tamblingan Area was organized using a research study that about 500 respondents for the start of 2020 and dictated by an accidental sampling method. The statistical analysis uses multiple linear regression, ANOVA, and R square to make an investigation conclusion. Main findings are simultaneously predictors variables consisting of beautiful scenery, most recently facilities, unique attractions, completeness of facilities, close distance, easy transportation, easy to reach location, and community hospitality significantly influence variable dependent intention to visit with influence strength of the determinant value at 0.539 or 53.9%. This study will enable the marketing practitioners to better understand the motivation of visitors to visit agritourism areas. Some predictor variables that significantly influence the intention to visit are the latest facilities, unique attractions, easy to reach location, and community hospitality.

Multiple linear regression-based physical fitness prediction models for Turkish secondary school students

2018 ◽  
Vol 5 (4) ◽  
pp. 58-64
Author(s):  
Mehmet Fatih Akay ◽  
Ozge Bozkurt ◽  
Ebru Cetin ◽  
Imdat Yarim
Keyword(s):  
Linear Regression ◽  
Secondary School ◽  
Physical Fitness ◽  
Multiple Linear Regression ◽  
Secondary School Students ◽  
Prediction Models ◽  
Daily Activities ◽  
Predictor Variables ◽  
School Students ◽  
Hand Grip

Physical fitness is a necessary component for daily activities. Measurement of physical activity is essential for determining physical fitness rate. This study aims to develop new prediction models for predicting the physical fitness of Turkish secondary school students by using multiple linear regression (MLR). The datasets comprise data of various number of subjects according to the target variables including the test scores of the 30m speed, 20m stage run, balance and hand-grip (right/left). The predictor variables used to develop the prediction models are gender, age, body mass index (BMI), body fat, number of curl-up and push-ups in 30 seconds. Eight physical fitness prediction models for each target have been created with the predictor variables listed above. The performance of the prediction models has been calculated by using standard error of estimate (SEE). The results show that MLR-based prediction models can be safely used to predict the physical fitness of Turkish secondary school students.Keywords: Physical fitness, multiple linear regression, machine learning, validation.

scholarly journals A Multiple Linear Regression Analysis of Rural-Urban COVID-19 Risk Disparities in Texas

2021 ◽  
Author(s):  
Amber K. Luo ◽  
Sophia Zhong ◽  
Charles Sun ◽  
Jasmine Wang ◽  
Alexander White
Keyword(s):  
African American ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Case Fatality ◽  
Multiple Linear Regression Analysis ◽  
Incidence Rates ◽  
Predictor Variables ◽  
Major Predictor ◽  
Major Factors

As the number of COVID-19 cases in the U.S. rises, the differential impact of the pandemic in urban and rural regions becomes more pronounced, and the major factors relating to this difference remain unclear. Using the 254 counties of Texas as units of analysis, we utilized multiple linear regression to investigate the influence of 83 county-level predictor variables including race demographics, age, demographics, healthcare and financial status, and prevalence of and mortality rate from COVID-19 risk factors on the incidence rate and case fatality rate from COVID-19 in Texas on September 15, 2020. Here, we report that urban counties experience, on average, 41.1% higher incidence rates from COVID-19 than rural counties and 34.7% lower case fatality rates. Through comparisons between our models, we found that this difference was largely attributable to four major predictor variables: namely, the proportion of elderly residents, African American residents, and Hispanic residents, and the presence of large nursing homes. According to our models, counties with high incidence rates of COVID-19 are predicted to have high proportions of African American residents and Hispanic residents coupled with low proportions of elderly residents. Furthermore, we found that counties with the highest case fatality rates are predicted to have high proportions of elderly residents, obese residents, and Hispanic residents, coupled with low proportions of residents ages 20-39 and residents who report smoking cigarettes. In our study, major variables and their effects on COVID-19 risk are quantified, highlighting the most vulnerable populations and regions of Texas.

scholarly journals SPATIAL ESTIMATION OF AVERAGE DAILY PRECIPITATION USING MULTIPLE LINEAR REGRESSION BY USING TOPOGRAPHIC AND WIND SPEED VARIABLES IN TROPICAL CLIMATE

2018 ◽  
Vol 26 (4) ◽  
pp. 299-316
Author(s):  
Mohd Talha Anees ◽  
Khiruddin Abdullah ◽  
M. N. M. Nawawi ◽  
Nik Norulaini Nik Ab Rahman ◽  
Abd. Rahni Mt. Piah ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Wind Speed ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Distribution Patterns ◽  
Predictor Variables ◽  
Precipitation Estimation ◽  
Special Distribution ◽  
Tropical Climates ◽  
Spatial Discontinuity

Complex topography and wind characteristics play important roles in rising air masses and in daily spatial distribution of the precipitations in complex region. As a result, its spatial discontinuity and behaviour in complex areas can affect the spatial distribution of precipitation. In this work, a two-fold concept was used to consider both spatial discontinuity and topographic and wind speed in average daily spatial precipitation estimation using Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR) in tropical climates. First, wet and dry days were identified by the two methods. Then the two models based on MLR (Model 1 and Model 2) were applied on wet days to estimate the precipitation using selected predictor variables. The models were applied for month wise, season wise and year wise daily averages separately during the study period. The study reveals that, Model 1 has been found to be the best in terms of categorical statistics, R2 values, bias and special distribution patterns. However, it was found that sets of different predictor variables dominates in different months, seasons and years. Furthermore, necessities of other data for further enhancement of the results were suggested.

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