Improved Comparison of ILI Data and Field Excavations

2012 ◽  
Author(s):  
William V. Harper ◽  
David J. Stucki ◽  
Thomas A. Bubenik ◽  
Clifford J. Maier ◽  
David A. R. Shanks ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Regression Line ◽  
Cost Savings ◽  
Major Axis ◽  
Least Squares Regression ◽  
Text Book ◽  
Probability Of Exceedance ◽  
Management Plans ◽  
Independent Variable ◽  
Reduced Major Axis

The importance of comparing in-line inspection (ILI) calls to excavation data should not be underestimated. Neither should it be undertaken without a solid understanding of the methodologies being employed. Such a comparison is not only a key part of assessing how well the tool performed, but also for an API 1163 evaluation and any subsequent use of the ILI data. The development of unity (1-1) plots and the associated regression analysis are commonly used to provide the basis for predicting the likelihood of leaks or failures from unexcavated ILI calls. Combining such analysis with statistically active corrosion methods into perhaps a probability of exceedance (POE) study helps develop an integrity maintenance plan for the years ahead. The theoretical underpinnings of standard regression analysis are based on the assumption that the independent variable (often thought of as x) is measured without error as a design variable. The dependent variable (often labeled y) is modeled as having uncertainty or error. Pipeline companies may run their regressions differently, but ILI to field excavation regressions often use the ILI depth as the x variable and field depth as the y variable. This is especially the case in which a probability of exceedance analysis is desired involving transforming ILI calls to predicted depths for a comparison to a threshold of interest such as 80% wall thickness. However, in ILI to field depth regressions, both the measured depths can have error. Thus, the underlying least squares regression assumptions are violated. Often one common result is a regression line that has a slope much less than the ideal 1-1 relationship. Reduced Major Axis (RMA) Regression is specifically formulated to handle errors in both the x and y variables. It is not commonly found in the standard literature but has a long pedigree including the 1995 text book Biometry by Sokal and Rohlf in which it appears under the title of Model II regression. In this paper we demonstrate the potential improvements brought about by RMA regression. Building on a solid comparison between ILI data and excavations provides the foundation for more accurate predictions and management plans that reliably provide longer range planning. This may also result in cost savings as the time between ILI runs might be lengthened due to a better analysis of such important data.

scholarly journals On the Allometric Relationship Between Size and Composition of Avian Eggs: A Reassessment

The Condor ◽  
2007 ◽  
Vol 109 (3) ◽  
pp. 705-714 ◽  
Author(s):  
Todd W. Arnold ◽  
Andy J. Green
Keyword(s):  
Maternal Investment ◽  
Major Axis ◽  
Ordinary Least Squares ◽  
Egg Mass ◽  
Correlated Errors ◽  
Least Squares Regression ◽  
Inert Component ◽  
Female Component ◽  
Investment In Reproduction ◽  
Reduced Major Axis

AbstractAbstract. Numerous investigators have used allometric regression to characterize the relationship between proportional egg composition and egg size, which is a potentially important characterization for assessing maternal investment in reproduction. Herein, we document two important shortcomings of this approach. First, regressing log component mass against log egg mass involves regressing Y on itself, since each component (Y) is necessarily a part of the whole egg (X). This creates correlated errors, which leads to biased estimates of the regression slope. To circumvent this problem, we recommend regressing egg component masses on a relatively inert component like total water mass. Secondly, investigators routinely use ordinary least squares regression to estimate the slope of allometric relationships, which assumes that all error resides in Y. We demonstrate that this assumption is false, but so are the underlying error assumptions of commonly used alternatives such as reduced major axis and major axis regression. Because each egg is unique and determining composition involves destructive sampling, there is no obvious way to assess measurement error in Y versus X. As a solution, we recommend that investigators analyze multiple eggs per clutch whenever possible and fit a reduced major axis based on the among-female component of variability.

A comparative analysis of the reduced major axis technique of fitting lines to bivariate data

1987 ◽  
Vol 17 (7) ◽  
pp. 654-659 ◽  
Author(s):  
Daniel J. Leduc
Keyword(s):  
Comparative Analysis ◽  
Major Axis ◽  
Group Method ◽  
Least Squares Regression ◽  
Primary Interest ◽  
Conditional Mean ◽  
Bivariate Data ◽  
Functional Relations ◽  
Reduced Major Axis ◽  
The Relationship

There are many ways of estimating the parameters of an equation to represent the relationship between two variables. While least-squares regression is generally acknowledged to be the best method to use when estimating the conditional mean of one variable given a fixed value for another, it is not usually an appropriate method to use when your primary interest is in the values of the equation parameters themselves (functional relations). In this case there are many other techniques (Bartlett's three-group method, Schnute's trend line, the general structural relationship, major axis regression, and reduced major axis) that may provide better estimates of these values. When all of the above techniques are compared, it is found that reduced major axis is often the most applicable because of its desirable properties and ease of estimation.

scholarly journals Plotting partial correlation and regression in ecological studies

Web Ecology ◽  
2008 ◽  
Vol 8 (1) ◽  
pp. 35-46 ◽  
Author(s):  
J. Moya-Laraño ◽  
G. Corcobado
Keyword(s):  
Linear Model ◽  
Multiple Regression ◽  
Explanatory Variable ◽  
Regression Line ◽  
Multivariate Techniques ◽  
Least Squares Regression ◽  
Ecological Studies ◽  
Partial Regression ◽  
Explanatory Variables ◽  
Independent Variable

Abstract. Multiple regression, the General linear model (GLM) and the Generalized linear model (GLZ) are widely used in ecology. The widespread use of graphs that include fitted regression lines to document patterns in simple linear regression can be easily extended to these multivariate techniques in plots that show the partial relationship of the dependent variable with each independent variable. However, the latter procedure is not nearly as widely used in ecological studies. In fact, a brief review of the recent ecological literature showed that in ca. 20% of the papers the results of multiple regression are displayed by plotting the dependent variable against the raw values of the independent variable. This latter procedure may be misleading because the value of the partial slope may change in magnitude and even in sign relative to the slope obtained in simple least-squares regression. Plots of partial relationships should be used in these situations. Using numerical simulations and real data we show how displaying plots of partial relationships may also be useful for: 1) visualizing the true scatter of points around the partial regression line, and 2) identifying influential observations and non-linear patterns more efficiently than using plots of residuals vs. fitted values. With the aim to help in the assessment of data quality, we show how partial residual plots (residuals from overall model + predicted values from the explanatory variable vs. the explanatory variable) should only be used in restricted situations, and how partial regression plots (residuals of Y on the remaining explanatory variables vs. residuals of the target explanatory variable on the remaining explanatory variables) should be the ones displayed in publications because they accurately reflect the scatter of partial correlations. Similarly, these partial plots can be applied to visualize the effect of continuous variables in GLM and GLZ for normal distributions and identity link functions.

scholarly journals Comparison of VOC measurements made by PTR-MS, Adsorbent Tube/GC-FID-MS and DNPH-derivatization/HPLC during the Sydney Particle Study, 2012: a contribution to the assessment of uncertainty in current atmospheric VOC measurements

2017 ◽  
Author(s):  
Erin Dunne ◽  
Ian E. Galbally ◽  
Min Cheng ◽  
Paul Selleck ◽  
Suzie B. Molloy ◽  
...  
Keyword(s):  
Climate Models ◽  
Regression Line ◽  
Measurement Techniques ◽  
Major Axis ◽  
Uncertainty Estimates ◽  
Degree Of Confidence ◽  
Benzene Toluene ◽  
Reduced Major Axis ◽  
High Degree ◽  
Dnph Derivatization

Abstract. Understanding uncertainty is essential for utilizing atmospheric VOC measurements in robust ways to develop atmospheric science. This study describes an inter-comparison of the VOC data, and the derived uncertainty estimates, measured with three independent techniques (PTR-MS, AT-GC-FID and DNPH-HPLC) during the Sydney Particle Study campaigns in 2012. The compounds and compound classes compared, based on objective selection criteria from the available data, were: benzene, toluene, C8 aromatics, isoprene, formaldehyde, acetaldehyde and acetone. Bottom-up uncertainty analyses were undertaken for each compound and each measurement system. Top-down uncertainties were quantified via the inter-comparisons. Four metrics were used for the inter-comparisons: the slope and intercept as determined by reduced major axis regression, the correlation, and the root mean standard deviation of the observation from the regression line. In all seven comparisons the correlations between independent measurement techniques were high with R2 values of median 0.93 (range: 0.72–0.98) and small root mean standard deviations of the observations from the regression line with a median of 0.13 (range: 0.04–0.23 ppb). These results give a high degree of confidence that for each comparison the response of the two independent techniques are dominated by the same constituents. The slope and intercept as determined by reduced major axis regression gives a different story. The slopes varied considerably with a median of 1.23 and range 1.08 to 2.03. The intercepts varied with a median of 0.02 and range −0.07 to 0.31 ppb. An ideal comparison would give a slope of 1.00 and an intercept of zero. This analysis identified some poorly understood and poorly quantified sources of uncertainty in the measurement techniques including: the contributions of non-target compounds to the measurement of the target compound for benzene, toluene and isoprene by PTR-MS; and, the under-reporting of formaldehyde, acetaldehyde and acetone by the DNPH technique. As well as these, this study has identified a specific interference of liquid water with acetone measurements by the DNPH technique. These relationships reported for Sydney 2012 were incorporated into a larger analysis with 61 other published inter-comparison studies for the same compounds. Overall for the light aromatics, isoprene and the C1–C3 carbonyls the uncertainty in a set of measurements varies by a factor of between 1.5 and two. These uncertainties (~50 %) are significantly higher than uncertainties estimated using standard propagation of error methods, which in this case were ~22 % or less, and are the result of the presence of poorly understood or neglected processes that affect the measurement and its uncertainty. The uncertainties in VOC measurements identified here should be considered when: assessing the reliability of VOC measurements from individual instruments; when utilising VOC data to constrain and inform air quality and climate models; when using VOC observations for human exposure studies; and, when comparing ambient VOC data with satellite retrievals.

Computation and uses of central trend lines

1984 ◽  
Vol 62 (10) ◽  
pp. 1897-1905 ◽  
Author(s):  
W. E. Ricker
Keyword(s):  
Geometric Mean ◽  
Regression Line ◽  
Major Axis ◽  
Sampling Procedure ◽  
Change Of Scale ◽  
Normal Configuration ◽  
Measurement Units ◽  
Reduced Major Axis ◽  
Standard Presentation ◽  
Central Trend

A bivariate array of naturally variable observations can take many different forms, depending on the relative lengths of the measurement units used. Each of these has a different central trend or major axis. In a standard presentation the major axis has a slope of ± 1 obtained when 1 standard deviation (s) of each variate, Y and X, occupies the same distance on its coordinate axis. With any other presentation the position of the standard trend is indicated by a line whose slope is the ratio of the standard deviations; it is called the standard (or reduced) major axis, or geometric mean regression line (GMR). The GMR is symmetrical, invariant with change of scale, and "robust." Besides indicating the central trend, it is a suitable line for estimating Y from X, or X from Y, in two common situations where ordinary regressions fail: (i) when the sampling procedure was not random with respect to the entire population (but was random with respect to its standard trend); (ii) when the population sampled departs seriously from a bivariate normal configuration. In the latter case an alternative "Schnute" line is appropriate if components of the population may have different sY/sX ratios.

PENGARUH MOTIVASI KERJA, KOMUNIKASI INTERPERSONAL DAN BUDAYA ORGANISASI TERHADAP KINERJA GURU (STUDI KASUS DI SMPN 5 PARIAMAN)

2020 ◽  
Author(s):  
Hasnah Mila
Keyword(s):  
Organizational Culture ◽  
Regression Analysis ◽  
Interpersonal Communication ◽  
Work Motivation ◽  
Employee Performance ◽  
High Reliability ◽  
Eligibility Criteria ◽  
Validity Test ◽  
Independent Variable ◽  
Performance Teachers

The independent independent variables in this study are Work Motivation, Interpersonal Communication and Organizational Culture while the dependent variable is the Performance Teachers and Employees SMPN 5 Pariaman. The sample used in this study as many as 32 respondents determined by using saturated samples. To know the influence of independent variable to dependent variable partially, used t test. While to know the effect of independent variable to dependent variable simultaneously, used F test. The assumption used in the validity test is if R-count> R-table item is declared valid. The R-arithmetic shown in the table above, from each item indicates that R-arithmetic> R-table so the item is declared valid. Based on the validity test of Work Motivation instrument, Interpersonal Communication and Organizational Culture on Teacher and Employee Performance, all items are declared valid and reliability test results indicate that the instrument has high reliability. This means that the eligibility criteria Instrument Motivation Work, Interpersonal Communication and Organizational Culture on Performance Teachers and Employees have met the criteria of good instrument requirements, namely valid and reliable. Regression analysis results obtained t count = 2.550 while t table = 2.042 so thitung> ttable and significance value is 0.000, this value is smaller than α = 0,05 so it can be said that motivation factor (X1) (Y) Regression analysis results obtained t count = 1.076 while the value of t table = 2.042 so that tcount < ttable or and its not significance value is 0.000, this value is smaller than α = 0,05, and proved variable of Interpersonal Communication (X2) (Y) Regression analysis results obtained t count = 1.715 while the value of t table = 2.042 so thitung< ttable and its not significance value is 0.000, this value is smaller than α = 0,05, and proved Organizational Culture variable (X3) The value of correlation coefficient (R) turns out that the correlation is positive. This means that there is a strong one-way relationship, where the change of increment that occurs in the free factor of Work Motivation, Personal Communication and Organizational Culture is accompanied by the change of the bound factor increase that is Teacher Performance (Y).

PENGARUH GAYA KEPEMIMPINAN, MOTIVASI KERJA DAN KOMPETENSI TERHADAP KINERJA PEGAWAI PADA DINAS PEMBERDAYAAN MASYARAKAT DAN DESA PROVINSI SUMATERA BARAT

2020 ◽  
Author(s):  
Mafral
Keyword(s):  
Regression Analysis ◽  
Leadership Style ◽  
Work Motivation ◽  
Employee Performance ◽  
High Reliability ◽  
Test Results ◽  
Eligibility Criteria ◽  
Validity Test ◽  
Negative Effect ◽  
Independent Variable

The independent independent variables in this study as many as 89 respondents are determined by using saturated samples. To know the influence of independent variable to dependent variable partially, used t test. While to know the effect of independent variable to dependent variable simultaneously, used F test. The assumption used in the validity test is if R-count> R-table item is declared valid. The R-arithmetic shown in the table above, from each item indicates that R-arithmetic> R- table so the item is declared valid. Based on the validity test of the instrument of Leadership Style, Work Motivation, and Competence on Employee Performance, all items are declared valid and reliability test results indicate that the instrument has high reliability. This means that the eligibility criteria of the Instrument of Leadership Style, Work Motivation, Competency and Employee Performance have met the criteria of good instrument requirements, that is valid and reliable. The result of regression analysis of Leadership Style obtained by tcount = 20,91 while ttable value = 1,988 tcount> ttable proved variable of Leadership Style influence to Employee Performance. Work Motivation regression analysis obtained tcount = 17.62 while the value ttable = 1.988 tcount> ttabel proven Motivational Work variables influence on Employee Performance. Regression analysis Competence obtained value tcount = - 06.85 while ttable =1.988 so thitung> ttable and proven variable Competence have a negative effect on Employee Performance.

Analisa Pemilihan Moda Transportasi Umum Rute Tanjung Karang – Bandara Radin Inten II Dengan Stated Preference dan Uji Crame’s V

2018 ◽  
Vol 2 (2) ◽  
pp. 137
Author(s):  
Muhammad Abi Berkah Nadi
Keyword(s):  
Regression Analysis ◽  
Utility Function ◽  
Stated Preference ◽  
Technical Analysis ◽  
Chi Square ◽  
Time Headway ◽  
Independent Variable ◽  
The Relationship ◽  
Selection Of

Radin Inten II Airport is a national flight in Lampung Province. In this study using the technical analysis stated preference which is the approach by conveying the choice statement in the form of hypotheses to be assessed by the respondent. By using these techniques the researcher can fully control the hypothesized factors. To determine utility function for model forecasting in fulfilling request of traveler is used regression analysis with SPSS program. The analysis results obtained that the passengers of the dominant airport in the selection of modes of cost attributes than on other attributes. From the result of regression analysis, the influence of independent variable to the highest dependent variable is when the five attributes are used together with the R square value of 8.8%. The relationship between cost, time, headway, time acces and service with the selection of modes, the provision that states whether or not there is a decision. The significance of α = 0.05 with chi-square. And the result of Crame's V test average of 0.298 is around the middle, then the relationship is moderate enough.

Estimation of response slopes in respiratory control

1984 ◽  
Vol 56 (2) ◽  
pp. 536-539 ◽  
Author(s):  
D. L. Sherrill ◽  
G. D. Swanson
Keyword(s):  
Statistical Approach ◽  
Ventilatory Response ◽  
Respiratory Control ◽  
Major Axis ◽  
Bivariate Normal Distribution ◽  
Directional Statistics ◽  
Control Behavior ◽  
Slope Estimate ◽  
Partial Pressure Of Co2 ◽  
Reduced Major Axis

The ventilatory response to changes in alveolar (arterial) CO2 is widely used as an index of respiratory control behavior. Methods for estimating these response slopes should incorporate the possibility that there may be errors in both the independent (partial pressure of CO2) and dependent (ventilation) variables. In a recent paper Daubenspeck and Ogden (J. Appl. Physiol. Respirat. Environ. Exercise Physiol. 45:823–829, 1978) have suggested problems inherent in the traditional technique of reduced major axis and have suggested a more contemporary technique of directional statistics. We have previously analyzed both techniques and developed a method to overcome the problems of reduced major axis and problems inherent in the use of directional statistics. Under the assumption of a bivariate normal distribution, we demonstrate that our slope estimate is similar to the maximum likelihood estimate proposed by Mardia et al. (J. Appl. Physiol.: Respirat. Environ. Exercise Physiol. 54: 309–313, 1983) for this problem. In addition, we demonstrate a bootstrap statistical approach when the distributions are not normally distributed. These concepts are illustrated using O2-CO2 interaction data.

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