Trends in Data Analysis in 2020 - International Journal of Computational Science and Information Technology (IJCSITY)
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The results of an experiment can be presented as a data table or as an equation that represents them In thecase of adjusting a polynomial, Excel allows us to change its degree and calculates the R2 of the Adjustedequation. It is considered that if it is 1 the equation goes through all the experimental points. By adjustingTungsten resistivity data, it is found that the equations do not go through all the points (even with R2 =1), which is verified by calculating the differences for each point. In those cases, the best fit is the linearinterpolation between consecutive points. The equation adjusted by Excel, Mat lab or Origin requireschecking if it corresponds to the minimum in the sum of squared differences, it is possible that it can bereduced by changing the coefficients value
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RESIDUALS AND INFLUENCE IN NONLINEAR REGRESSION FOR REPEATED MEASUREMENT DATA
All observations don’t have equal significance in regression analysis. Diagnostics of observations is an important aspect of model building. In this paper, we use diagnostics method to detect residuals and influential points in nonlinear regression for repeated measurement data. Cook distance and Gauss newton method have been proposed to identify the outliers in nonlinear regression analysis and parameter estimation. Most of these techniques based on graphical representations of residuals, hat matrix and case deletion measures. The results
show us detection of single and multiple outliers cases in repeated measurement data. We use these techniques
to explore performance of residuals and influence in nonlinear regression model.
information theoretic subspace clustering
Information Theoretic Subspace Clustering
Ran He, Liang Wang, Zhenan Sun, Yingya Zhang, and Bo Li
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,
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Método Topsis - multiple decision makers
This document summarizes a study that used the fuzzy TOPSIS method to select the optimal type of spillway for a dam in northern Greece called Pigi Dam. Five alternative spillway types were evaluated based on nine criteria. The criteria were expressed as triangular fuzzy numbers to account for uncertainty. Weights for the criteria were determined using the AHP method and also expressed linguistically as fuzzy numbers. The fuzzy TOPSIS method was then used to rank the alternatives based on their distances from the ideal and negative-ideal solutions. The alternative with the highest relative closeness to the ideal solution was determined to be the optimal spillway type.
Numerical modeling to evaluate pile head deflection under the lateral load
Abstract The complex behavior of pile head deflection under the lateral load can be studied using various analytical methods and the softwares. Often the lateral pile load testing is carried out in the field to confirm the calculated lateral pile capacity. However, even with the use of sophisticated latest softwares, the accurate deflection of pile head cannot be estimated. Hence an attempt has been made in this paper to evaluate the pile head deflection using the field load-deflection data and the corresponding soil and pile properties. A preliminary mathematical model has been developed using a technique of dimensional analysis (DA) to evaluate pile head deflection under different pile diameters, different pile materials and varying soil conditions. The estimated pile head deflection using DA equation is compared with 14nos. of measured lateral pile load test results conducted at the site. It can be observed from this study that, the dimensional analysis can be used effectively to estimate the pile head deflection. More variables based on more field results can be introduced in the mathematical model to increase the accuracy in the estimation of pile head deflection. Keywords: Pile head deflection, Lateral pile load, Dimensional analysis
IEOR 265 Final Paper_Minchao Lin
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012
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- 1. “Trends in Data Analysis in 2020” International Journal of Computational Science and Information Technology (IJCSITY) ISSN : 2320-7442 (Online) ; 2320 - 8457 (Print) http://aircconline.com/ijcsity/V7N4/7419ijcsity02.pdf
- 2. DATA TABLE, EQUATION FIT OR INTERPOLATION Héctor G. Riveros Instituto de Física UNAM, Mexico ABSTRACT The results of an experiment can be presented as a data table or as an equation that represents them In the case of adjusting a polynomial, Excel allows us to change its degree and calculates the R2 of the Adjusted equation. It is considered that if it is 1 the equation goes through all the experimental points. By adjusting Tungsten resistivity data, it is found that the equations do not go through all the points (even with R2 = 1), which is verified by calculating the differences for each point. In those cases, the best fit is the linear interpolation between consecutive points. The equation adjusted by Excel, Mat lab or Origin requires checking if it corresponds to the minimum in the sum of squared differences, it is possible that it can be reduced by changing the coefficients values. KEYWORDS Equation adjustment, interpolation, uncertainties. Full Text: http://aircconline.com/ijcsity/V7N4/7419ijcsity02.pdf
- 3. REFERENCES [1] Chambers J M, Cleveland W S, Kleiner B and Tukey P A 1983 Graphical Methods for Data Analysis (Pacific Grove, CA: Wadsworth & Brooks/Cole) [2] Mersereau, A.,Metz J. 1998 True lines The Physics Teacher 36, 174 doi: 10.1119/1.879995 [3] Blikensderfer R 1985 Systematic errors and graphical extrapolation The Physics Teacher 23, 545 doi: 10.1119/1.2341909 [4] Ruprigth ME.2011 Measuring Systematic Error with Curve Fits The Physics Teacher 49, 54; doi: 10.1119/1.3527759 [5] Zetie KP.2016 Can students draw lines of best fit? Phys.Educ .51065017 [6] Sebastião PJ. 2013 The art of model fitting to experimental results European Journal of Physics, Volume 35, Number1 [7] Vasilyeva D ,Giannotti M, Goehl JF2015 Comparison of different approaches in extraction of aparameter in a linear fit Eur.J.Phys.36045011 [8] Peterlin P 2010 Data analysis and graphing in an introductory physics laboratory:spreadsheet versus statistics suite Eur.J.Phys.31 919 [9] Minitab Blog, https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis- howdo-i-interpret-r-squared-and-assess-the-goodness-of-fit [10] Frost https://statisticsbyjim.com/regression/interpret-r-squared-regression/ [11] Espe Materials of high vacuum technology Vol 1 page 46, Pergamon 1966