Multi-stage regression linear parametric models of room temperature in office buildings
21
Citation
42
Reference
10
Related Paper
Citation Trend
Data set
Cite
Citations (17)
Abstract : Stepwise multiple regression tables are provided separately for males and females. Each table contains a listing for a series of regression equations for each dependent variable. Each dependent variable is first identified by data base number, abbreviated name, and full name. For each listing five columns are presented, each giving the regression constant and coefficient(s) for the best predictive multiple regression including 1, 2, 3, 4, and 5 independent variables, respectively. The last two rows of each listing contain the standard error of the estimate and adjusted coefficient of determination (R-squared) for each of the five sequential models. All models are significantly different from zero at the 0.001 level.
Standardized coefficient
Path coefficient
Stepwise regression
Variables
Table (database)
Regression diagnostic
Listing (finance)
Multiple correlation
Cite
Citations (6)
Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.
Cite
Citations (2)
This chapter contains sections titled: Summary Introduction Hypothesis testing (using P-values) Estimation (using confidence intervals) Choosing the statistical method Comparison of two independent groups Comparing more than two groups Two groups of paired observations The relationship between two continuous variables Correlation Regression Multiple regression Regression or correlation? Parametric versus non-parametric methods Technical details: Checking the assumptions for a linear regression analysis
Regression diagnostic
Cite
Citations (0)
Least-squares regression has been applied as a tool to understand traffic growth patterns and to predict future growth. Specifically, given a set of historical annual average daily traffic (AADT) values for a location, regression can be used to summarize traffic growth patterns and to predict growth. However, this technique is vulnerable to outliers because standard linear regression techniques can produce arbitrarily large errors in their results if points are badly placed. The situation is made worse when thousands of traffic sites are analyzed at once because it is infeasible to examine each set of regression results individually. In this paper two outlier detection and removal techniques and one robust regression technique are compared with simple least-squares regression for accuracy in traffic growth prediction, with both linear and log-linear models of traffic growth on historical AADT values for several thousand sites in the state of New York. Each method was evaluated by the median absolute error in predictions being computed for 1 year, 4 years, and 8 years beyond the modeled values and also by the mean percent error being computed, giving each site equal weight. When all sites were equally weighted, the robust regression technique produced significantly better results than either plain regression or outlier detection techniques. Using median absolute error, none of the robust techniques produced significantly more accurate results than ordinary regression.
Robust regression
Ordinary least squares
Regression diagnostic
Local regression
Cite
Citations (9)
The least squares regression minimizing deviations only in dependent variable is not suitable for the regression analysis of environmental monitoring datasets, which are all random variables. Three two-variable linear models, i.e., the least squares regression, the reduced major axis regression, and the least normal square regression, were compared for the regression analysis of anions and cations in rain water samples. The results shown that the reduced major axis regression, rather than the others, was likely to be the model of choice for the regression analysis of random datasets, and a higher value was obtained for the regression coefficient b, showing a better relationship between the variables.
Regression diagnostic
Local regression
Standardized coefficient
Cite
Citations (0)
In the method comparison approach, two measurement errors are observed. The classical regression approach (linear regression) method cannot be used for the analysis because the method may yield biased and inefficient estimates. In view of that, the Deming regression is preferred over the classical regression. The focus of this work is to assess the impact of censored data on the traditional regression, which deletes the censored observations compared to an adapted version of the Deming regression that takes into account the censored data. The study was done based on simulation studies with NLMIXED being used as a tool to analyse the data. Eight different simulation studies were run in this study. Each of the simulation is made up of 100 datasets with 300 observations. Simulation studies suggest that the traditional Deming regression which deletes censored observations gives biased estimates and a low coverage, whereas the adapted Deming regression that takes censoring into account gives estimates that are close to the true value making them unbiased and gives a high coverage. When the analytical error ratio is misspecified, the estimates are as well not reliable and biased.
Censoring (clinical trials)
Censored regression model
Regression diagnostic
Robust regression
Cite
Citations (0)
asreg can fit three types of regression models; (1) a model of depvar on indepvars using linear regression in a user's defined rolling window or recursive window (2) cross-sectional regressions or regressions by a grouping variable (3) Fama and MacBeth (1973) two-step procedure. asreg is order of magnitude faster than estimating rolling window regressions through conventional methods such as Stata loops or using the Stata's official rolling command. asreg has the same speed efficiency as asrol. All the rolling window calculations, estimation of regression parameters, and writing of results to Stata variables are done in the Mata language. asreg reports most commonly used regression statistics such as number of observations, r-squared, adjusted r-squared, constant, slope coefficients, standard errors of the coefficients, fitted values, and regression residuals.
Variables
Cite
Citations (0)
Least-squares regression has been applied as a tool to understand traffic growth patterns and to predict future growth. Specifically, given a set of historical annual average daily traffic (AADT) values for a location, regression can be used to summarize traffic growth patterns and to predict growth. However, this technique is vulnerable to outliers because standard linear regression techniques can produce arbitrarily large errors in their results if points are badly placed. The situation is made worse when thousands of traffic sites are analyzed at once because it is infeasible to examine each set of regression results individually. In this paper two outlier detection and removal techniques and one robust regression technique are compared with simple least-squares regression for accuracy in traffic growth prediction, with both linear and log-linear models of traffic growth on historical AADT values for several thousand sites in the state of New York. Each method was evaluated by the median absolute error in predictions being computed for 1 year, 4 years, and 8 years beyond the modeled values and also by the mean percent error being computed, giving each site equal weight. When all sites were equally weighted, the robust regression technique produced significantly better results than either plain regression or outlier detection techniques. Using median absolute error, none of the robust techniques produced significantly more accurate results than ordinary regression.
Robust regression
Ordinary least squares
Regression diagnostic
Cite
Citations (10)