The document presents a mathematical model called "Tai's model" for determining the total area under curves from metabolic studies more accurately than other existing formulas. Tai's model divides the total area under a curve into individual segments like rectangles and triangles, whose areas can be precisely calculated using geometric formulas. The individual areas are then summed to obtain the total area. The model was tested on five data sets and found to determine total areas with over 99.8% accuracy compared to the true value obtained from graphical methods, while other formulas underestimated or overestimated areas by a large margin. Tai's model allows flexibility in experimental conditions and precise determination of total areas even with unequal time intervals between measurements.
This document discusses surfaces in hyperbolic 3-manifolds and the effect of Dehn filling on incompressible surfaces. It provides examples of hyperbolic 3-manifolds, defines Dehn filling, and describes types of surfaces like totally geodesic, incompressible, and acylindrical surfaces. The main results are that incompressible surfaces can become compressible after finitely many Dehn fillings, but there is no universal upper bound on the number of fillings. The document also outlines the strategy to prove that for any integer n, there exist n slopes where infinitely many incompressible surfaces in the figure eight knot complement compress.
Shape analysis using conformal mapping is a technique that analyzes geometric shapes by mapping their connections and distributions into computer programs. It is useful in fields like archaeology, architecture, medical imaging, and computer design. Conformal mapping preserves angles between shapes and can map 2D and 3D shapes onto each other. For brain mapping specifically, spherical harmonics equations are used to compress brain surface data and compare different brain scans. Conformal mapping proves useful for medical applications due to its ability to uniformly map points on brain surfaces without distortion.
Statistical Measures of Location: Mathematical Formulas versus Geometric Appr...BRNSS Publication Hub
This paper illustrates with an example of the comparison of the geometrical and the numerical approaches
of measures of location. A geometrical derivation of the most popular measure of location (mean) was
derived from a histogram by determining the centroid of a histogram. The numerical or mathematical
expression of the other measures of location, median and mode were derived from ogive and histogram,
respectively. Finally, the research establishes that the two approaches produce the same results.
THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...ICARDA
The document discusses symmetrizing and variance stabilizing transformations for normalizing statistical distributions. It examines some standard transformations like the arcsine transformation for binomial proportions and the square root transformation for Poisson variables. While variance stabilizing transformations stabilize variance, symmetrizing transformations directly reduce skewness for better normality. The document derives differential equations to obtain symmetrizing transformations and applies this to examples like the correlation coefficient, binomial proportion, Poisson variable, and chi-square distribution. It finds that in some cases, the symmetrizing transformation provides better normality than the variance stabilizing transformation. The document also discusses an application of these transformations.
This document contains a table with geometry vocabulary terms from Chapter 3 including definitions and examples. It lists terms such as alternate angles, parallel lines, perpendicular lines, slope, and the point-slope form of a linear equation. The table is intended to be filled in as readers work through Chapter 3. There are also review questions testing understanding of lines, angles, parallel and perpendicular lines, slopes of lines, and representing lines in the coordinate plane.
Model of robust regression with parametric and nonparametric methodsAlexander Decker
This document summarizes and compares several parametric and nonparametric methods for estimating the parameters in a simple linear regression model when outliers are present in the data. It introduces ordinary least squares regression as the classical parametric method and discusses its limitations when outliers are present. It then summarizes several nonparametric and robust regression methods that are less influenced by outliers, including Theil's method, least absolute deviations regression, M-estimation, and trimmed least squares regression. The document presents the models and algorithms for these various methods. It concludes by describing a simulation study that evaluates and compares the performance of these different estimation techniques under various types and amounts of outliers.
The concept of teaching is the fundamental study of student's cognitive action. In this paper a Trapezoidal Fuzzy Assessment model (TFAM) is developed for teaching assessment. The TFAM is a new variation of a special form, which is used in Fuzzy mathematics centre of gravity(COG) defuzzification technique. The TFAM's new idea is the replacement of the rectangles appearing in the graph of the COG method by isosceles trapezoids sharing common parts, thus covering the ambiguous cases of teachers' scores being at the limits between two successive grades (e.g between A and B). A classroom application is also presented in which the outcomes of the COG and TFAM methods are compared with those of other traditional assessment methods (calculation of means and GPA index) and explanations are provided for the differences appeared among these outcomes .
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.
This document discusses surfaces in hyperbolic 3-manifolds and the effect of Dehn filling on incompressible surfaces. It provides examples of hyperbolic 3-manifolds, defines Dehn filling, and describes types of surfaces like totally geodesic, incompressible, and acylindrical surfaces. The main results are that incompressible surfaces can become compressible after finitely many Dehn fillings, but there is no universal upper bound on the number of fillings. The document also outlines the strategy to prove that for any integer n, there exist n slopes where infinitely many incompressible surfaces in the figure eight knot complement compress.
Shape analysis using conformal mapping is a technique that analyzes geometric shapes by mapping their connections and distributions into computer programs. It is useful in fields like archaeology, architecture, medical imaging, and computer design. Conformal mapping preserves angles between shapes and can map 2D and 3D shapes onto each other. For brain mapping specifically, spherical harmonics equations are used to compress brain surface data and compare different brain scans. Conformal mapping proves useful for medical applications due to its ability to uniformly map points on brain surfaces without distortion.
Statistical Measures of Location: Mathematical Formulas versus Geometric Appr...BRNSS Publication Hub
This paper illustrates with an example of the comparison of the geometrical and the numerical approaches
of measures of location. A geometrical derivation of the most popular measure of location (mean) was
derived from a histogram by determining the centroid of a histogram. The numerical or mathematical
expression of the other measures of location, median and mode were derived from ogive and histogram,
respectively. Finally, the research establishes that the two approaches produce the same results.
THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...ICARDA
The document discusses symmetrizing and variance stabilizing transformations for normalizing statistical distributions. It examines some standard transformations like the arcsine transformation for binomial proportions and the square root transformation for Poisson variables. While variance stabilizing transformations stabilize variance, symmetrizing transformations directly reduce skewness for better normality. The document derives differential equations to obtain symmetrizing transformations and applies this to examples like the correlation coefficient, binomial proportion, Poisson variable, and chi-square distribution. It finds that in some cases, the symmetrizing transformation provides better normality than the variance stabilizing transformation. The document also discusses an application of these transformations.
This document contains a table with geometry vocabulary terms from Chapter 3 including definitions and examples. It lists terms such as alternate angles, parallel lines, perpendicular lines, slope, and the point-slope form of a linear equation. The table is intended to be filled in as readers work through Chapter 3. There are also review questions testing understanding of lines, angles, parallel and perpendicular lines, slopes of lines, and representing lines in the coordinate plane.
Model of robust regression with parametric and nonparametric methodsAlexander Decker
This document summarizes and compares several parametric and nonparametric methods for estimating the parameters in a simple linear regression model when outliers are present in the data. It introduces ordinary least squares regression as the classical parametric method and discusses its limitations when outliers are present. It then summarizes several nonparametric and robust regression methods that are less influenced by outliers, including Theil's method, least absolute deviations regression, M-estimation, and trimmed least squares regression. The document presents the models and algorithms for these various methods. It concludes by describing a simulation study that evaluates and compares the performance of these different estimation techniques under various types and amounts of outliers.
The concept of teaching is the fundamental study of student's cognitive action. In this paper a Trapezoidal Fuzzy Assessment model (TFAM) is developed for teaching assessment. The TFAM is a new variation of a special form, which is used in Fuzzy mathematics centre of gravity(COG) defuzzification technique. The TFAM's new idea is the replacement of the rectangles appearing in the graph of the COG method by isosceles trapezoids sharing common parts, thus covering the ambiguous cases of teachers' scores being at the limits between two successive grades (e.g between A and B). A classroom application is also presented in which the outcomes of the COG and TFAM methods are compared with those of other traditional assessment methods (calculation of means and GPA index) and explanations are provided for the differences appeared among these outcomes .
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.
Dimensional analysis is a technique for simplifying physical problems by reducing the number of variables. It is useful for presenting experimental data, solving problems without exact theories, checking equations, and establishing relative importance of phenomena. Dimensional analysis refers to the physical nature and units of quantities. It can be used to develop equations for fluid phenomena, convert between unit systems, and reduce variables in experiments. Two common methods are Rayleigh's method, which determines expressions involving a maximum of four variables, and Buckingham's π-method, which arranges variables into dimensionless groups. Dimensional analysis provides a partial solution and requires understanding the relevant phenomena.
The document discusses various methods for constructing confidence intervals for estimating multinomial proportions. It aims to analyze the propensity for aberrations (i.e. unrealistic bounds like negative values) in the interval estimates across different classical and Bayesian methods. Specifically, it provides the mathematical conditions under which each method may produce aberrant interval limits, such as zero-width intervals or bounds exceeding 0 and 1, especially for small sample counts. The document also develops an R program to facilitate computational implementation of the various methods for applied analysis of multinomial data.
Siegel-Tukey test named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to the data measured at least on an ordinal scale. It tests for the differences in scale between two groups.
The test is used to determine if one of two groups of data tends to have more widely dispersed values than the other.
The test was published in 1980 by Sidney Siegel and John Wilder Tukey in the journal of the American Statistical Association in the article “A Non-parametric Sum Of Ranks Procedure For Relative Spread in Unpaired Samples “.
This document provides an overview of different types of charts for visualizing data, including pie charts, bar charts, histograms, step charts, and ogives. It discusses when each chart is most appropriate based on the type of data (nominal, ordinal, discrete metric, continuous metric). For each chart type, it provides examples and discusses how to properly construct and interpret the charts. It also covers concepts like bimodal distributions and the normal distribution as they relate to interpreting chart shapes.
The document presents a new method for forecasting enrollments at the University of Alabama using fuzzy time series. The method first partitions the historical enrollment data universe of discourse into intervals based on frequency density. It then defines fuzzy sets for each interval and fuzzifies the enrollment data. Fuzzy logical relationships are established based on the fuzzified enrollments. The method is then used to forecast enrollments from 1972 to 1992 and results are compared to existing fuzzy time series forecasting methods using average forecasting error rate and mean square error. The proposed method aims to improve forecasting accuracy over existing approaches.
The tooth factor effect on the harmonics of large electrical machinesjournalBEEI
In the current study, the general mathematical model for the calculation and analysis of asynchronous systems and transient cases in asynchronous synchronous large electrical machines was developed. The theory of magnetic fields in the teeth's circuits with a smooth surface of the rotor was used and at the same time, high harmonics of magnetic fields and its effect on the transient cases was also calculated. Performance curves were investigated using Matlab codes and evaluated under different values of factor. The results confirmed the possibility of improving the noise harmonics on the sinusoidal wave form, which is reflected on the machines starting.
This document contains a geometry lesson on calculating the perimeters and areas of polygons in the coordinate plane. It includes examples of estimating areas of irregular shapes using grids, finding perimeters and areas of parallelograms, and subtracting areas of additional shapes from a bounding rectangle. One example shows two composite figures that appear to have different areas but are proved to have the same area through calculation. The lesson concludes with a quiz to assess understanding of finding perimeters and areas of polygons in the coordinate plane.
1. A model is constructed using plywood, nails, and wires to represent two skew lines in three-dimensional space.
2. The shortest distance between the two skew lines is measured directly using a ruler.
3. The coordinates of points on the two lines are used to calculate the shortest distance analytically via a formula.
4. The distances obtained through measurement and calculation are found to be approximately the same, verifying the analytical method.
This chapter discusses chi-square tests and nonparametric tests. It covers performing chi-square tests to compare two or more proportions, test independence between categorical variables using contingency tables, and introduce nonparametric tests including the Wilcoxon rank sum test and Kruskal-Wallis test. Examples are provided to demonstrate chi-square tests of equality of proportions, independence, and expected versus observed frequencies in contingency tables.
1. Miller indices arise from finding the plane intercepts with the crystal axes, taking their reciprocals, and reducing to integers.
2. Miller indices represent the components of the plane equation and each set of indices corresponds to a set of parallel planes.
3. Important crystal planes and forms are often given special names based on their symmetry and Miller indices. The reciprocal lattice is used to relate planes and directions in a crystal lattice.
1) Analysis of variance (ANOVA) is a statistical technique developed by R.A. Fisher in 1920 to analyze the differences between group means and their associated procedures.
2) ANOVA divides the total variation into different parts that can be attributed to various sources of variation - between groups, within groups, etc.
3) There are two main classifications of ANOVA - one-way ANOVA, which looks at the effect of one factor on the dependent variable, and two-way ANOVA, which analyzes the effects of two factors.
4) ANOVA has many applications in fields like pharmacy, biology, agriculture, and business research to study the effects of different treatments, products, or interventions.
The document discusses dimensional analysis, similitude, and model analysis. It provides background on how dimensional analysis and model testing are used to study fluid mechanics problems. Dimensional analysis uses the dimensions of physical quantities to determine which parameters influence a phenomenon. Model testing in a laboratory allows measurements to be applied to larger scale systems using similitude. Buckingham's π-theorem is introduced as a way to non-dimensionalize variables when there are more variables than fundamental dimensions. Rayleigh's and Buckingham's methods are demonstrated on an example of determining the resisting force on an aircraft.
This document provides information about the chi-square test. It begins with an introduction to chi-square tests and their use in testing differences between observed and expected frequencies. It then discusses degrees of freedom, assumptions of chi-square tests, and applications like testing goodness of fit. Examples are provided to demonstrate chi-square calculations and comparing results to critical values. The document concludes that chi-square tests are useful statistical tools for analyzing group differences in non-parametric data.
A study on the ANOVA ANALYSIS OF VARIANCE.pptxjibinjohn140
ANOVA (analysis of variance) is a statistical method used to test if the means of three or more samples or groups are equal. It divides the total variation in a data set into variation between groups and variation within groups. An F-test is used to compare the ratio of between-group variation and within-group variation. If the F-calculated value is less than the F-critical value, the null hypothesis that the sample means are equal is accepted. ANOVA can test for differences between more than two groups which makes it more efficient than multiple t-tests.
This document provides an overview of analysis of variance (ANOVA) including:
- ANOVA was developed by R.A. Fisher in 1920 to analyze differences between multiple sample means.
- It compares variance between groups to variance within groups using an F-statistic ratio.
- ANOVA can be one-way to analyze one variable or two-way to analyze effects of two variables.
- Applications of ANOVA include pharmaceutical research, clinical trials, agriculture, marketing, and more.
This document provides an overview of Chapter 4: Statistics from the Analytical Chemistry I course. It discusses key topics from the chapter including the Gaussian distribution, confidence intervals, comparisons of means and standard deviations using t-tests and F-tests, identifying outliers, calibration curves, and the method of least squares for fitting data to lines. The chapter establishes the importance of statistics in analyzing experimental measurements and accounting for variability.
The document discusses the chi-square test, a non-parametric statistical test used to compare observed data with expected frequencies. It can be used to test independence and association between two categorical variables. The chi-square test involves calculating a quantity called chi-square from a contingency table of observed and expected frequencies and comparing it to critical values from a chi-square distribution table. If the calculated value exceeds the critical value, the null hypothesis of no relationship can be rejected.
RESIDUALS AND INFLUENCE IN NONLINEAR REGRESSION FOR REPEATED MEASUREMENT DATAorajjournal
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.
The document discusses statistical tests such as the t-test and F-test. The t-test is used to compare means of two samples, such as comparing sample means before and after treatment. There are different types of t-tests, including paired samples and independent samples t-tests. The F-test, also called the F-ratio, compares variances between samples and is used in analysis of variance (ANOVA) to test differences between two or more groups. Examples are provided to demonstrate how to perform t-tests and F-tests to analyze data and test hypotheses.
The chi-square test is used to determine if an observed distribution of data differs from the theoretical distribution. It compares observed frequencies to expected frequencies based on a hypothesis. The chi-square value is calculated by summing the squared differences between observed and expected frequencies divided by the expected frequency. The chi-square value is then compared to a critical value from the chi-square distribution table based on the degrees of freedom. If the chi-square value is greater than the critical value, the null hypothesis that the distributions are the same can be rejected.
Dimensional analysis is a technique for simplifying physical problems by reducing the number of variables. It is useful for presenting experimental data, solving problems without exact theories, checking equations, and establishing relative importance of phenomena. Dimensional analysis refers to the physical nature and units of quantities. It can be used to develop equations for fluid phenomena, convert between unit systems, and reduce variables in experiments. Two common methods are Rayleigh's method, which determines expressions involving a maximum of four variables, and Buckingham's π-method, which arranges variables into dimensionless groups. Dimensional analysis provides a partial solution and requires understanding the relevant phenomena.
The document discusses various methods for constructing confidence intervals for estimating multinomial proportions. It aims to analyze the propensity for aberrations (i.e. unrealistic bounds like negative values) in the interval estimates across different classical and Bayesian methods. Specifically, it provides the mathematical conditions under which each method may produce aberrant interval limits, such as zero-width intervals or bounds exceeding 0 and 1, especially for small sample counts. The document also develops an R program to facilitate computational implementation of the various methods for applied analysis of multinomial data.
Siegel-Tukey test named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to the data measured at least on an ordinal scale. It tests for the differences in scale between two groups.
The test is used to determine if one of two groups of data tends to have more widely dispersed values than the other.
The test was published in 1980 by Sidney Siegel and John Wilder Tukey in the journal of the American Statistical Association in the article “A Non-parametric Sum Of Ranks Procedure For Relative Spread in Unpaired Samples “.
This document provides an overview of different types of charts for visualizing data, including pie charts, bar charts, histograms, step charts, and ogives. It discusses when each chart is most appropriate based on the type of data (nominal, ordinal, discrete metric, continuous metric). For each chart type, it provides examples and discusses how to properly construct and interpret the charts. It also covers concepts like bimodal distributions and the normal distribution as they relate to interpreting chart shapes.
The document presents a new method for forecasting enrollments at the University of Alabama using fuzzy time series. The method first partitions the historical enrollment data universe of discourse into intervals based on frequency density. It then defines fuzzy sets for each interval and fuzzifies the enrollment data. Fuzzy logical relationships are established based on the fuzzified enrollments. The method is then used to forecast enrollments from 1972 to 1992 and results are compared to existing fuzzy time series forecasting methods using average forecasting error rate and mean square error. The proposed method aims to improve forecasting accuracy over existing approaches.
The tooth factor effect on the harmonics of large electrical machinesjournalBEEI
In the current study, the general mathematical model for the calculation and analysis of asynchronous systems and transient cases in asynchronous synchronous large electrical machines was developed. The theory of magnetic fields in the teeth's circuits with a smooth surface of the rotor was used and at the same time, high harmonics of magnetic fields and its effect on the transient cases was also calculated. Performance curves were investigated using Matlab codes and evaluated under different values of factor. The results confirmed the possibility of improving the noise harmonics on the sinusoidal wave form, which is reflected on the machines starting.
This document contains a geometry lesson on calculating the perimeters and areas of polygons in the coordinate plane. It includes examples of estimating areas of irregular shapes using grids, finding perimeters and areas of parallelograms, and subtracting areas of additional shapes from a bounding rectangle. One example shows two composite figures that appear to have different areas but are proved to have the same area through calculation. The lesson concludes with a quiz to assess understanding of finding perimeters and areas of polygons in the coordinate plane.
1. A model is constructed using plywood, nails, and wires to represent two skew lines in three-dimensional space.
2. The shortest distance between the two skew lines is measured directly using a ruler.
3. The coordinates of points on the two lines are used to calculate the shortest distance analytically via a formula.
4. The distances obtained through measurement and calculation are found to be approximately the same, verifying the analytical method.
This chapter discusses chi-square tests and nonparametric tests. It covers performing chi-square tests to compare two or more proportions, test independence between categorical variables using contingency tables, and introduce nonparametric tests including the Wilcoxon rank sum test and Kruskal-Wallis test. Examples are provided to demonstrate chi-square tests of equality of proportions, independence, and expected versus observed frequencies in contingency tables.
1. Miller indices arise from finding the plane intercepts with the crystal axes, taking their reciprocals, and reducing to integers.
2. Miller indices represent the components of the plane equation and each set of indices corresponds to a set of parallel planes.
3. Important crystal planes and forms are often given special names based on their symmetry and Miller indices. The reciprocal lattice is used to relate planes and directions in a crystal lattice.
1) Analysis of variance (ANOVA) is a statistical technique developed by R.A. Fisher in 1920 to analyze the differences between group means and their associated procedures.
2) ANOVA divides the total variation into different parts that can be attributed to various sources of variation - between groups, within groups, etc.
3) There are two main classifications of ANOVA - one-way ANOVA, which looks at the effect of one factor on the dependent variable, and two-way ANOVA, which analyzes the effects of two factors.
4) ANOVA has many applications in fields like pharmacy, biology, agriculture, and business research to study the effects of different treatments, products, or interventions.
The document discusses dimensional analysis, similitude, and model analysis. It provides background on how dimensional analysis and model testing are used to study fluid mechanics problems. Dimensional analysis uses the dimensions of physical quantities to determine which parameters influence a phenomenon. Model testing in a laboratory allows measurements to be applied to larger scale systems using similitude. Buckingham's π-theorem is introduced as a way to non-dimensionalize variables when there are more variables than fundamental dimensions. Rayleigh's and Buckingham's methods are demonstrated on an example of determining the resisting force on an aircraft.
This document provides information about the chi-square test. It begins with an introduction to chi-square tests and their use in testing differences between observed and expected frequencies. It then discusses degrees of freedom, assumptions of chi-square tests, and applications like testing goodness of fit. Examples are provided to demonstrate chi-square calculations and comparing results to critical values. The document concludes that chi-square tests are useful statistical tools for analyzing group differences in non-parametric data.
A study on the ANOVA ANALYSIS OF VARIANCE.pptxjibinjohn140
ANOVA (analysis of variance) is a statistical method used to test if the means of three or more samples or groups are equal. It divides the total variation in a data set into variation between groups and variation within groups. An F-test is used to compare the ratio of between-group variation and within-group variation. If the F-calculated value is less than the F-critical value, the null hypothesis that the sample means are equal is accepted. ANOVA can test for differences between more than two groups which makes it more efficient than multiple t-tests.
This document provides an overview of analysis of variance (ANOVA) including:
- ANOVA was developed by R.A. Fisher in 1920 to analyze differences between multiple sample means.
- It compares variance between groups to variance within groups using an F-statistic ratio.
- ANOVA can be one-way to analyze one variable or two-way to analyze effects of two variables.
- Applications of ANOVA include pharmaceutical research, clinical trials, agriculture, marketing, and more.
This document provides an overview of Chapter 4: Statistics from the Analytical Chemistry I course. It discusses key topics from the chapter including the Gaussian distribution, confidence intervals, comparisons of means and standard deviations using t-tests and F-tests, identifying outliers, calibration curves, and the method of least squares for fitting data to lines. The chapter establishes the importance of statistics in analyzing experimental measurements and accounting for variability.
The document discusses the chi-square test, a non-parametric statistical test used to compare observed data with expected frequencies. It can be used to test independence and association between two categorical variables. The chi-square test involves calculating a quantity called chi-square from a contingency table of observed and expected frequencies and comparing it to critical values from a chi-square distribution table. If the calculated value exceeds the critical value, the null hypothesis of no relationship can be rejected.
RESIDUALS AND INFLUENCE IN NONLINEAR REGRESSION FOR REPEATED MEASUREMENT DATAorajjournal
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.
The document discusses statistical tests such as the t-test and F-test. The t-test is used to compare means of two samples, such as comparing sample means before and after treatment. There are different types of t-tests, including paired samples and independent samples t-tests. The F-test, also called the F-ratio, compares variances between samples and is used in analysis of variance (ANOVA) to test differences between two or more groups. Examples are provided to demonstrate how to perform t-tests and F-tests to analyze data and test hypotheses.
The chi-square test is used to determine if an observed distribution of data differs from the theoretical distribution. It compares observed frequencies to expected frequencies based on a hypothesis. The chi-square value is calculated by summing the squared differences between observed and expected frequencies divided by the expected frequency. The chi-square value is then compared to a critical value from the chi-square distribution table based on the degrees of freedom. If the chi-square value is greater than the critical value, the null hypothesis that the distributions are the same can be rejected.
The document applies the variational iteration method (VIM) to solve linear and nonlinear ordinary differential equations (ODEs) with variable coefficients. It emphasizes the power of the method by using it to solve a variety of ODE models of different orders and coefficients. The document also uses VIM to solve four scientific models - the hybrid selection model, Thomas-Fermi equation, Kidder equation for unsteady gas flow through porous media, and the Riccati equation. The VIM provides efficient iterative approximations for both analytic solutions and numeric simulations of real-world applications in science and engineering.
The chi-square test is used to determine if an observed frequency distribution differs from an expected theoretical distribution. It can test goodness of fit, independence of attributes, and homogeneity. The test involves calculating chi-square by taking the sum of the squares of the differences between observed and expected frequencies divided by expected frequencies. For the test to be valid, certain conditions must be met regarding sample size, expected frequencies, independence, and randomness. The test has some limitations such as not measuring strength of association and being unreliable with small expected frequencies.
The document discusses analysis of variance (ANOVA), a statistical technique developed by R.A. Fisher in 1920 to analyze the differences between group means and their associated procedures. It can be used when there are two or more samples to study the significance of differences between their mean values. ANOVA works by decomposing the overall variability into different sources and comparing the relative sizes of different variances. It is useful for research in fields like agriculture, biology, pharmacy, and more.
Regularity and complexity in dynamical systemsSpringer
This chapter discusses how variational methods have been used to analyze three classes of snakelike robots: 1) hyper-redundant manipulators guided by backbone curves, 2) flexible steerable needles, and 3) concentric tube continuum robots. Variational methods provide a means to determine optimal backbone curves for manipulators, generate optimal plans for needle steering, and model equilibrium conformations for concentric tube robots based on elastic mechanics principles. The chapter reviews how variational formulations using Euler-Lagrange and Euler-Poincare equations are applied in each case.
This document discusses two statistical models of chromatography - the discrete flow model and continuous flow model. It derives equations to calculate the mean and variance of peaks that are still on the column (position peaks) and peaks leaving the column (exit peaks) for each model. It also discusses how the variance contributions from different sources, such as axial diffusion, can be determined and subtracted from measured peak variances to calculate a plate number that is independent of the capacity factor. This provides a simple but practical way to account for dynamic effects that determine peak variance.