Applied Regression Modeling

Pardoe, Iain

doi:10.1002/9781118274415

Cited by 36 publications

(48 citation statements)

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“…The predictors for each model were selected in relation to the different resolution of the noise data (Table 1). To develop the LUR models, the forward selection procedure was applied where predictor variables with the highest simple correlation with the modelled noise variable were included step-by-step [43]. A number of predictors were found co-related with the noise level for different resolution of the data (Supplementary Table 4).…”

Section: Overview Of Experiments Designmentioning

confidence: 99%

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

et al. 2017

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Abstract:The impact of temporal aspects of noise data on model development and intra-urban variability on environmental noise levels are often ignored in the development of models used to predict its spatiotemporal variation within a city. Using a Land Use Regression approach, this study develops a framework which uses routine noise monitors to model the prevailing ambient noise, and to develop a noise variability map showing the variation within a city caused by land-use setting. The impact of data resolution on model development and the impact of meteorological variables on the noise level which are often ignored were also assessed. Six models were developed based on monthly, daily and hourly resolutions of both the noise and predictor data. Cross validation highlighted that only the hourly resolution model having 59% explanatory power of the observed data (adjusted R 2 ) and a potential of explaining at least 0.47% variation of any independent dataset (cross validation R 2 ), was a suitable candidate among all the developed models for explaining intraurban variability of noise. In the hourly model, regions with roads of high traffic volumes, with higher concentrations of heavy goods vehicles, and being close to activity centres were found to have more impact on the prevailing ambient noise. Road lengths were found to be the most influential predictors and identified as having an impact on the ambient noise monitors.

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Section: Overview Of Experiments Designmentioning

confidence: 99%

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

et al. 2017

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“…The first step of any data analysis should be to explore the data through drawing appropriate graphs and calculating various summary statistics-Section 6.1 of Pardoe (2006) contains examples for this dataset. The dataset is sufficiently rich and varied that in my experience students engage with the data quite readily and appear to enjoy constructing scatterplots and boxplots for the easily understood variables.…”

Section: Regression Model Buildingmentioning

confidence: 99%

“…For example, they could predict the sale price of a home similar to the one in which they grew up, or explore the relative values of different home characteristics such as, "Is an additional bathroom valued more than an additional bedroom?" This article is based on a case study in Pardoe (2006), which also contains further details on the more routine aspects of a regression analysis. Here I complement that case study by providing additional motivation for the analysis and further background on actual use of this dataset in the classroom.…”

Section: Introductionmentioning

confidence: 99%

Modeling Home Prices Using Realtor Data

Pardoe

2008

Journal of Statistics Education

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It can be challenging when teaching regression concepts to find interesting real-life datasets that allow analyses that put all the concepts together in one large example. For example, concepts like interaction and predictor transformations are often illustrated through small-scale, unrealistic examples with just one or two predictor variables that make it difficult for students to appreciate how these concepts might be applied in more realistic multi-variable problems. This article addresses this challenge by describing a complete multiple linear regression analysis of home price data that covers many of the usual regression topics, including interaction and predictor transformations. The analysis also contains useful practical advice on model building-another topic that can be hard to illustrate realistically-and novel statistical graphics for interpreting regression model results. The analysis was motivated by the sale of a home by the author. The statistical ideas discussed range from those suitable for a second college statistics course to those typically found in more advanced linear regression courses.

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“…Horowitz, 2004;Metcalf, 2008;Berry, 2008;Shaw, 2009;Pardoe, 2006). Hierarchical MRA allowed testing of theoretical propositions (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…to examine independent variables sequentially), such that variables' influence an outcome was judged (Petrocelli, 2003). MRA's coefficient of determination (R 2 ) is of particular interest during analysis (Pardoe, 2006). For each stage of regression R 2 is calculated to provide indication of said individual influence.…”

Section: Methodsmentioning

confidence: 99%

Analysis of US commercial buildings’ energy efficiency programs

Ofori-Boadu

Shofoluwe

Owusu‐Manu

et al. 2015

Built Environment Project and Asset Management

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2015),"Comparative analysis of back-to-back subcontracts in the construction and telecommunications industries", Built Environment Project and Asset Management, Vol. 5 Iss 4 pp. 446-460 http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to investigate relationships between five energy efficiency programs and electricity intensities in the US commercial buildings sector. Design/methodology/approach -Regression methods analyze state-level secondary data from 2006 through 2009 to identify significant energy program drivers of electricity efficiency. Findings -Combined, the five programs studied account for approximately nine percent reduction in commercial electricity intensity. Practical implications -Outcomes will inform stakeholders' decision-making regarding adoption or continuation of energy programs. Social implications -Electricity efficiency gains will help reduce negative environmental aspects and the present dependence on foreign oil. Originality/value -Very little research has considered the impact of multiple programs on commercial electricity efficiency within their complex implementation environment.

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Applied Regression Modeling

Cited by 36 publications

References 0 publications

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

Modeling Home Prices Using Realtor Data

Analysis of US commercial buildings’ energy efficiency programs

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