Quantitative Analysis of Shells from a Site in Goleta, California

1961 ◽  
Vol 26 (3Part1) ◽  
pp. 416-420 ◽  
Author(s):  
Roberta S. Greenwood
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Analysis Of Variance ◽  
Least Squares Method ◽  
Applied Mathematics ◽  
Regression Line ◽  
Dry Weight ◽  
Convincing Evidence ◽  
Least Squares Regression ◽  
Significant Difference

AbstractThis shell study was designed to test and perfect methods of quantitative analysis in addition to providing the usual identification of species. Experiments were performed to determine the optimum size of sample, dimension of screen, number of samples, and the practicability of rapid analysis in the field to guide the progress of excavation. Analysis of variance was used to measure up to five variables at once; differences were shown graphically with direction of change indicated by a least-squares regression line. The analysis of 69 excavation units revealed an overwhelming preference for mud-dwelling species which showed no change in horizontal distribu-give guidance to the excavation team. Used with dry-weight analysis, field sorting would also indicate areas of richer occupation debris in time for this information to be useful. The use of applied mathematics in midden analysis adds a precise tool to the archaeological inventory. The data compiled on screen size, sample size, horizontal and vertical distributions, and such, were subjected to the analysis of variance, and where significant difference was indicated, the statistics were fitted to a regression line by the least-squares method. By using standardized systems and quantitative analysis, the archaeologist may obtain convincing evidence to support his conclusions. These procedures would be equally applicable to the study of skeletal, artifactual, or other ecological remains, and would add authority to the theories derived from such analysis.

Incorrect least-squares regression coefficients in method-comparison analysis.

1979 ◽  
Vol 25 (3) ◽  
pp. 432-438 ◽  
Author(s):  
P J Cornbleet ◽  
N Gochman
Keyword(s):  
Standard Deviation ◽  
Least Squares ◽  
Least Squares Method ◽  
Regression Line ◽  
Method Comparison ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Data Set ◽  
Data Points ◽  
Regression Slopes

Abstract The least-squares method is frequently used to calculate the slope and intercept of the best line through a set of data points. However, least-squares regression slopes and intercepts may be incorrect if the underlying assumptions of the least-squares model are not met. Two factors in particular that may result in incorrect least-squares regression coefficients are: (a) imprecision in the measurement of the independent (x-axis) variable and (b) inclusion of outliers in the data analysis. We compared the methods of Deming, Mandel, and Bartlett in estimating the known slope of a regression line when the independent variable is measured with imprecision, and found the method of Deming to be the most useful. Significant error in the least-squares slope estimation occurs when the ratio of the standard deviation of measurement of a single x value to the standard deviation of the x-data set exceeds 0.2. Errors in the least-squares coefficients attributable to outliers can be avoided by eliminating data points whose vertical distance from the regression line exceed four times the standard error the estimate.

Comparison of the Least-Squares Regression Line and a Truncated Discrete Fourier Transform when describing Linearly Correlated data

2020 ◽  
Author(s):  
Chris Anto
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Discrete Fourier Transform ◽  
Least Squares Method ◽  
Regression Line ◽  
Correlated Data ◽  
Least Squares Regression

For long, the least squares regression line has been a primary tool for analyzing linear data. In this paper, the author suggests a method involving a truncated fourier transform that would achieve everything the least squares method achieves and more, including details about the magnitude of deviation.

scholarly journals Mortalité du sapin baumier défolié par la tordeuse des bourgeons de l'épinette dans la région de la Gatineau au Québec

1982 ◽  
Vol 58 (5) ◽  
pp. 213-219 ◽  
Author(s):  
Jean Beaulieu ◽  
Yvan J. Hardy
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Random Number ◽  
Least Squares Method ◽  
Random Number Generator ◽  
Balsam Fir ◽  
Number Generator ◽  
Profit Analysis ◽  
Significant Difference

This paper presents a method of analysis which differentiates between spruce budworm caused mortality and regular mortality on balsam fir in the Gatineau region in Quebec. A first attempt was made using multiple linear regression and a uniform random number generator. In order to overcome the bias inherent to the least squares method when dealing with a binary (0,1) dependent variable, a profit analysis was also conducted. In this case, the parameters and their variance were estimated using likehood method. These two approaches proved to be equivalent when percent budworm caused mortality was compared within the 1958 to 1979 period covered by the data at hand, while the outbreak lasted from 1968 to 1975.In 1979, approximately 55% of the stems had been killed by the budworm, accounting for 53% of the volume. Maple-yellow birch associations were more affected than fir associations although no significant difference was found. Fir mortality was delayed by aerial spraying of insecticides but this advantage disappeared as soon as the spray operations came to an end.

The Effect of Knowledge Management Strategies and Enablers on Knowledge Creation Process and Organizational Performance by Using Partial Least Squares Regression Method

2012 ◽  
Vol 3 (4) ◽  
pp. 38-52 ◽  
Author(s):  
Cheng-Ping Shih ◽  
Hsin-Fu Chou
Keyword(s):  
Knowledge Management ◽  
Organizational Performance ◽  
Least Squares ◽  
Partial Least Squares ◽  
Knowledge Creation ◽  
Least Squares Method ◽  
Management Strategies ◽  
Integrative Model ◽  
Least Squares Regression ◽  
Creation Process

Under Knowledge-based economy, knowledge has been recognized as a form of capital for organizations and provides sustainable competitive advantages. knowledge is not only one of the few recyclable assets that continuously lends itself to new intellectual capital but also be integrated in many different ways in order to maximize its value. This paper has three research objectives. Firstly, measure the effect of Knowledge Management (KM) Strategies on KM Enablers; secondly, measure the effect of KM Enablers on the Knowledge Creation Process (KCP); thirdly, to measure the effect of KCP on the three aspects of Organizational Performance. A knowledge integrative model was built by using Partial Least Squares method, and the findings indicate that KM Strategies do have a significant effect on KM enablers, which in turn does have a significant effect on the KCP. KCP also has a significant effect on innovation, customer’s satisfaction and financial performance for Taiwan multinational company in Thailand.

scholarly journals Analysis of Methanol Gasoline by ATR-FT-IR Spectroscopy

2019 ◽  
Vol 9 (24) ◽  
pp. 5336 ◽  
Author(s):  
Qi XIA ◽  
Lei-ming YUAN ◽  
Xiaojing CHEN ◽  
Liuwei MENG ◽  
Guangzao HUANG
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Internal Combustion Engines ◽  
Quantitative Model ◽  
Least Square ◽  
Support Vector ◽  
Least Squares Regression ◽  
Transportation Industry ◽  
Successive Projections Algorithm

Methanol gasoline blends are a more economical, and environmentally friendly fuels than gasoline alone, and are widely used in the transportation industry. The content of methanol in methanol gasoline plays an important role in ensuring the quality of gasoline. In some solutions, due to the shortage of energy and illegal profits, the problem of gasoline adulteration and its fineness, has received more and more attention, which would seriously affect the operating condition and service life of internal combustion engines. Therefore, it is very important to identify the correct level of gasoline. However, the traditional detection method is complex and time-consuming. To this end, the feasibility of using attenuated total reflectance Fourier transform infrared (ATR-FTIR) methods coupled with chemometrics methods were investigated to quantitatively and qualitatively analyze methanol gasoline. The qualitative analysis result of partial least squares discriminant analysis (PLS-DA) obtained 100% and 98.66% accuracy in the calibration set and the prediction set, respectively. As for quantitative analysis; two regression algorithms of partial least squares regression (PLSR) and the least square support vector machine (LS-SVM), as well as two variables selection methods of the successive projections algorithm (UVE) competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were combined to establish the quantitative model. By comparing the performance of the optimal models; the UVE-PLSR model performed best with a residual predictive deviation (RPD) value of 6.420. The qualitative and quantitative analysis results demonstrate the feasibility of using ATR-FTIR spectra to detect the methanol in methanol gasoline. It is believed that the promising IR spectra will be widely used in gasoline energy quality control in the further.

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