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Machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature extraction by neighborhood components analysis (NCA). Our dataset comprises 287 features from both process data indicators (i.e., derived from log data of an online statistics learning platform) and self-report data from high school students enrolled in Advanced Placement (AP) Statistics (N=733). As a label for prediction, we use students' scores on the AP Statistics exam. We evaluated the performance of machine learning classifiers with a given feature extraction method by evaluation criteria including F1 scores, the area under the receiver operating characteristic curve (AUC), and Cohen's Kappas. We find that NCA effectively reduces the dimensionality of training datasets, stabilizes machine learning predictions, and produces interpretable scores. However, interpreting the NCA weights of features, while feasible, is not very straightforward compared to linear regression. Future research should consider developing guidelines to interpret NCA weights.

The purpose of this study was to examine the interaction effects of gender and programs of study on attitudes of postgraduate diploma in teaching (PGDT) students towards the teaching profession. The main aim of the study was to find out whether there are any significant interaction effects of gender and programs of study of PGDT students' attitude towards the teaching profession. A descriptive survey was carried out among PGDT students during the year 2016/2017 regular session with a population of 371 students. The survey was conducted between science and non-science students in the department of teacher education at Mekelle University, Ethiopia. Using a stratified random sampling method, 130 student-teachers were selected as sample for the study. A scale entitled "attitude towards teaching profession" was developed and validated by the researchers based on established procedures in the literature (Aliyu Musa and Ado Abdu Bichi, 2015). A scale contained 15 items to be scored on the five-point Likert scale as Strongly Agree (SA), Agree (A), Neutral (N), Disagree (D) and Strongly Disagree (SD) was used. The data analysis of descriptive statistics through quantitative analysis by computing SPSS 16.0 (Statistical Package for the Social Sciences) for Windows 10 was employed. Statistical techniques which include percentage analysis for levels, mean, standard deviation and two-way analysis of variance (ANOVA) were also used for data analysis and interpretation. The findings of the two-way ANOVA demonstrated that there are no significant interaction effects of gender and programs of study on attitude towards the teaching profession of PGDT students. Correlation analysis of gender and programs of a study indicated that the student teachers' attitude towards the teaching profession was positively related to gender and programs of study.

The purpose of this study was to examine the interaction effects of gender and programs of study on attitudes of postgraduate diploma in teaching (PGDT) students towards the teaching profession. The main aim of the study was to find out whether there are any significant interaction effects of gender and programs of study of PGDT students' attitude towards the teaching profession. A descriptive survey was carried out among PGDT students during the year 2016/2017 regular session with a population of 371 students. The survey was conducted between science and non-science students in the department of teacher education at Mekelle University, Ethiopia. Using a stratified random sampling method, 130 student-teachers were selected as sample for the study. A scale entitled "attitude towards teaching profession" was developed and validated by the researchers based on established procedures in the literature (Aliyu Musa and Ado Abdu Bichi, 2015). A scale contained 15 items to be scored on the five-point Likert scale as Strongly Agree (SA), Agree (A), Neutral (N), Disagree (D) and Strongly Disagree (SD) was used. The data analysis of descriptive statistics through quantitative analysis by computing SPSS 16.0 (Statistical Package for the Social Sciences) for Windows 10 was employed. Statistical techniques which include percentage analysis for levels, mean, standard deviation and two-way analysis of variance (ANOVA) were also used for data analysis and interpretation. The findings of the two-way ANOVA demonstrated that there are no significant interaction effects of gender and programs of study on attitude towards the teaching profession of PGDT students. Correlation analysis of gender and programs of a study indicated that the student teachers' attitude towards the teaching profession was positively related to gender and programs of study.

xviii, 397 p. : 26 cm

xviii, 397 p. : 26 cm

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

209 pages : illustrations ; 24 cm +

209 pages : illustrations ; 24 cm +

A method of fitting vapor pressure temperature data to the Antoine equation is described along with a detailed statistical analysis of the fits and calculations of vapor pressures, enthalpies of vaporization, volatilities (concentration of saturated vapors), and boiling points with the Antoine constants. It is shown that the number of digits required to accurately perform vapor pressure calculations is greater than previously believed. The system permits calculation of the limits of error for any level of confidence. A program is used to analyze and plot the available vapor pressure data for diethyl malonate. Vapor pressure, Antoine equation, Volatility, Temperature, Statistics, Plotting, Enthalpy of Vaporization, Concentration of saturated vapor, Experimental methods, Diethyl malonate, Confidence limits, Confidence level.

A method of fitting vapor pressure temperature data to the Antoine equation is described along with a detailed statistical analysis of the fits and calculations of vapor pressures, enthalpies of vaporization, volatilities (concentration of saturated vapors), and boiling points with the Antoine constants. It is shown that the number of digits required to accurately perform vapor pressure calculations is greater than previously believed. The system permits calculation of the limits of error for any level of confidence. A program is used to analyze and plot the available vapor pressure data for diethyl malonate. Vapor pressure, Antoine equation, Volatility, Temperature, Statistics, Plotting, Enthalpy of Vaporization, Concentration of saturated vapor, Experimental methods, Diethyl malonate, Confidence limits, Confidence level.

The Higher Education Funding Council for England (HEFCE) asked the Higher Education Statistics Agency (HESA) and the Higher Education Academy (HEA) to undertake research to explore the current issues around academic teaching qualifications in the HESA Staff record and to offer recommendations to improve data quality and coverage in future collections. The teaching qualifications data-collection cycle has been running for three years since the 2012/13 academic year. However, low levels of confidence in data robustness have prevented full publication of the data. In the first year of the data return in England, 51% of relevant staff were returned with "unknown" teaching qualifications. In the subsequent two years, the percentage of staff returned with unknown qualifications has fallen to 37%, and most recently to 26%. Whilst the improvement in the return is welcome, HEFCE was keen to understand the reasons for the still significant number of staff returned in this category. Accordingly, HESA (the sector data-collection agency) and the HEA (which accredits much of the training and continuing professional development in higher education (HE) and is responsible for overseeing the UK Professional Standards) were asked to investigate the potential reasons for this. In particular they were asked to explore trends and variability in the data, to identify and explore issues which might be affecting data quality and make recommendations for improvements in data collection and in the provision of information about the teaching qualifications return. This report analyses the proportion of "unknowns", broken down by a variety of factors, including mode of employment, length of service, nationality and discipline. The following are appended: (1) Glossary and Acronym List; (2) Interview Schedule for Teaching Qualifications Research with PVCs and HR Directors; and (3) How the HEA Keeps Records Up to Date.

The Higher Education Funding Council for England (HEFCE) asked the Higher Education Statistics Agency (HESA) and the Higher Education Academy (HEA) to undertake research to explore the current issues around academic teaching qualifications in the HESA Staff record and to offer recommendations to improve data quality and coverage in future collections. The teaching qualifications data-collection cycle has been running for three years since the 2012/13 academic year. However, low levels of confidence in data robustness have prevented full publication of the data. In the first year of the data return in England, 51% of relevant staff were returned with "unknown" teaching qualifications. In the subsequent two years, the percentage of staff returned with unknown qualifications has fallen to 37%, and most recently to 26%. Whilst the improvement in the return is welcome, HEFCE was keen to understand the reasons for the still significant number of staff returned in this category. Accordingly, HESA (the sector data-collection agency) and the HEA (which accredits much of the training and continuing professional development in higher education (HE) and is responsible for overseeing the UK Professional Standards) were asked to investigate the potential reasons for this. In particular they were asked to explore trends and variability in the data, to identify and explore issues which might be affecting data quality and make recommendations for improvements in data collection and in the provision of information about the teaching qualifications return. This report analyses the proportion of "unknowns", broken down by a variety of factors, including mode of employment, length of service, nationality and discipline. The following are appended: (1) Glossary and Acronym List; (2) Interview Schedule for Teaching Qualifications Research with PVCs and HR Directors; and (3) How the HEA Keeps Records Up to Date.

The Higher Education Funding Council for England (HEFCE) asked the Higher Education Statistics Agency (HESA) and the Higher Education Academy (HEA) to undertake research to explore the current issues around academic teaching qualifications in the HESA Staff record and to offer recommendations to improve data quality and coverage in future collections. The teaching qualifications data-collection cycle has been running for three years since the 2012/13 academic year. However, low levels of confidence in data robustness have prevented full publication of the data. In the first year of the data return in England, 51% of relevant staff were returned with "unknown" teaching qualifications. In the subsequent two years, the percentage of staff returned with unknown qualifications has fallen to 37%, and most recently to 26%. Whilst the improvement in the return is welcome, HEFCE was keen to understand the reasons for the still significant number of staff returned in this category. Accordingly, HESA (the sector data-collection agency) and the HEA (which accredits much of the training and continuing professional development in higher education (HE) and is responsible for overseeing the UK Professional Standards) were asked to investigate the potential reasons for this. In particular they were asked to explore trends and variability in the data, to identify and explore issues which might be affecting data quality and make recommendations for improvements in data collection and in the provision of information about the teaching qualifications return. This report analyses the proportion of "unknowns", broken down by a variety of factors, including mode of employment, length of service, nationality and discipline. The following are appended: (1) Glossary and Acronym List; (2) Interview Schedule for Teaching Qualifications Research with PVCs and HR Directors; and (3) How the HEA Keeps Records Up to Date.

The Higher Education Funding Council for England (HEFCE) asked the Higher Education Statistics Agency (HESA) and the Higher Education Academy (HEA) to undertake research to explore the current issues around academic teaching qualifications in the HESA Staff record and to offer recommendations to improve data quality and coverage in future collections. The teaching qualifications data-collection cycle has been running for three years since the 2012/13 academic year. However, low levels of confidence in data robustness have prevented full publication of the data. In the first year of the data return in England, 51% of relevant staff were returned with "unknown" teaching qualifications. In the subsequent two years, the percentage of staff returned with unknown qualifications has fallen to 37%, and most recently to 26%. Whilst the improvement in the return is welcome, HEFCE was keen to understand the reasons for the still significant number of staff returned in this category. Accordingly, HESA (the sector data-collection agency) and the HEA (which accredits much of the training and continuing professional development in higher education (HE) and is responsible for overseeing the UK Professional Standards) were asked to investigate the potential reasons for this. In particular they were asked to explore trends and variability in the data, to identify and explore issues which might be affecting data quality and make recommendations for improvements in data collection and in the provision of information about the teaching qualifications return. This report analyses the proportion of "unknowns", broken down by a variety of factors, including mode of employment, length of service, nationality and discipline. The following are appended: (1) Glossary and Acronym List; (2) Interview Schedule for Teaching Qualifications Research with PVCs and HR Directors; and (3) How the HEA Keeps Records Up to Date.

This report compares several methods for expressing vapor pressure as a function of temperature (also referred to as correlation in the traditional literature) using the Antoine equation and discusses statistical analyses of the resulting correlations. Vapor pressure varies nonlinearly with temperature and is an important property of materials for applications ranging from estimates of their behavior in the environment to design of test equipment. Vapor pressure and temperature measurements over wide dynamic ranges are difficult to obtain, and prediction of vapor pressure based on limited data may be required in certain cases, necessitating reliable relationships between pressure and temperature. While the integrated form of the Clausius--Clapeyron equation has sound theoretical basis for correlating pressure and temperature, assumptions required for the temperature dependence of enthalpy may not be valid, particularly over wide temperature ranges. To correct for those approximations, a modified correlation equation may be implemented to enable accurate extrapolation. One variation of the Clausius-Clapeyron equation is the Antoine equation, which incorporates a third fit parameter to more accurately describe the nonlinearity of vapor pressure data. The current results support the use of the procedure proposed by Penski and Latour as the best method for correlating vapor pressure data.

This report compares several methods for expressing vapor pressure as a function of temperature (also referred to as correlation in the traditional literature) using the Antoine equation and discusses statistical analyses of the resulting correlations. Vapor pressure varies nonlinearly with temperature and is an important property of materials for applications ranging from estimates of their behavior in the environment to design of test equipment. Vapor pressure and temperature measurements over wide dynamic ranges are difficult to obtain, and prediction of vapor pressure based on limited data may be required in certain cases, necessitating reliable relationships between pressure and temperature. While the integrated form of the Clausius--Clapeyron equation has sound theoretical basis for correlating pressure and temperature, assumptions required for the temperature dependence of enthalpy may not be valid, particularly over wide temperature ranges. To correct for those approximations, a modified correlation equation may be implemented to enable accurate extrapolation. One variation of the Clausius-Clapeyron equation is the Antoine equation, which incorporates a third fit parameter to more accurately describe the nonlinearity of vapor pressure data. The current results support the use of the procedure proposed by Penski and Latour as the best method for correlating vapor pressure data.

This report compares several methods for expressing vapor pressure as a function of temperature (also referred to as correlation in the traditional literature) using the Antoine equation and discusses statistical analyses of the resulting correlations. Vapor pressure varies nonlinearly with temperature and is an important property of materials for applications ranging from estimates of their behavior in the environment to design of test equipment. Vapor pressure and temperature measurements over wide dynamic ranges are difficult to obtain, and prediction of vapor pressure based on limited data may be required in certain cases, necessitating reliable relationships between pressure and temperature. While the integrated form of the Clausius--Clapeyron equation has sound theoretical basis for correlating pressure and temperature, assumptions required for the temperature dependence of enthalpy may not be valid, particularly over wide temperature ranges. To correct for those approximations, a modified correlation equation may be implemented to enable accurate extrapolation. One variation of the Clausius-Clapeyron equation is the Antoine equation, which incorporates a third fit parameter to more accurately describe the nonlinearity of vapor pressure data. The current results support the use of the procedure proposed by Penski and Latour as the best method for correlating vapor pressure data.

This report compares several methods for expressing vapor pressure as a function of temperature (also referred to as correlation in the traditional literature) using the Antoine equation and discusses statistical analyses of the resulting correlations. Vapor pressure varies nonlinearly with temperature and is an important property of materials for applications ranging from estimates of their behavior in the environment to design of test equipment. Vapor pressure and temperature measurements over wide dynamic ranges are difficult to obtain, and prediction of vapor pressure based on limited data may be required in certain cases, necessitating reliable relationships between pressure and temperature. While the integrated form of the Clausius--Clapeyron equation has sound theoretical basis for correlating pressure and temperature, assumptions required for the temperature dependence of enthalpy may not be valid, particularly over wide temperature ranges. To correct for those approximations, a modified correlation equation may be implemented to enable accurate extrapolation. One variation of the Clausius-Clapeyron equation is the Antoine equation, which incorporates a third fit parameter to more accurately describe the nonlinearity of vapor pressure data. The current results support the use of the procedure proposed by Penski and Latour as the best method for correlating vapor pressure data.

To her surprise, Ms. Logan had just conducted a statistical analysis of her 10th grade biology students' quiz scores. The results indicated that she needed to reinforce mitosis before the students took the high-school proficiency test in three weeks, as required by the state. "Oh! That's easy!" She exclaimed. Teachers like Ms. Logan are everywhere. You may be a lot like her yourself--you have lots of data on your students: quiz scores, homework grades, attendance records, and so on--but aside from reporting such information to your school, you don?t know what else to do with it.

To her surprise, Ms. Logan had just conducted a statistical analysis of her 10th grade biology students' quiz scores. The results indicated that she needed to reinforce mitosis before the students took the high-school proficiency test in three weeks, as required by the state. "Oh! That's easy!" She exclaimed. Teachers like Ms. Logan are everywhere. You may be a lot like her yourself--you have lots of data on your students: quiz scores, homework grades, attendance records, and so on--but aside from reporting such information to your school, you don?t know what else to do with it.

To her surprise, Ms. Logan had just conducted a statistical analysis of her 10th grade biology students' quiz scores. The results indicated that she needed to reinforce mitosis before the students took the high-school proficiency test in three weeks, as required by the state. "Oh! That's easy!" She exclaimed. Teachers like Ms. Logan are everywhere. You may be a lot like her yourself--you have lots of data on your students: quiz scores, homework grades, attendance records, and so on--but aside from reporting such information to your school, you don?t know what else to do with it.

To her surprise, Ms. Logan had just conducted a statistical analysis of her 10th grade biology students' quiz scores. The results indicated that she needed to reinforce mitosis before the students took the high-school proficiency test in three weeks, as required by the state. "Oh! That's easy!" She exclaimed. Teachers like Ms. Logan are everywhere. You may be a lot like her yourself--you have lots of data on your students: quiz scores, homework grades, attendance records, and so on--but aside from reporting such information to your school, you don?t know what else to do with it.

DCIEM has been involved in decompression research for over 30 years, and has successfully developed and validated a number of decompression tables for use by the Canadian Forces. These include decompression for air diving, surface-supported helium-oxygen diving, and self-contained semi-closed circuit rebreather diving for mine countermeasures. This article is a review of the traditional deterministic approach to decompression modelling and the newer probabilistic approach. The new approach to decompression modelling has the potential for developing safer and more efficient diving procedures and tables for CF diving applications. Since the development of the first decompression tables in 1906 by J.S. Haldane, considerable research and effort have been expended in the development of safer and more rapid decompression procedures and tables. Most models of decompression that have been used to generate decompression tables have taken a deterministic approach where the boundary between 'safe' and 'unsafe' dives is governed by a fixed set of rules, depending on the gas exchange model and 'safe ascent' criterion that are selected. These models are essentially empirical and not physiological models, providing 'safe' decompression only over a limited range of depth and bottom times. Because decompression illness (DCI) is considered a binary event, it becomes logistically and financially impossible to conduct enough dives to show that a given dive profile is safe within statistical significance. DCI should actually be considered as a probabilistic event. Decompression profiles are not just a case of being 'safe' or "unsafe" but should be considered as a time-depth dosage.

DCIEM has been involved in decompression research for over 30 years, and has successfully developed and validated a number of decompression tables for use by the Canadian Forces. These include decompression for air diving, surface-supported helium-oxygen diving, and self-contained semi-closed circuit rebreather diving for mine countermeasures. This article is a review of the traditional deterministic approach to decompression modelling and the newer probabilistic approach. The new approach to decompression modelling has the potential for developing safer and more efficient diving procedures and tables for CF diving applications. Since the development of the first decompression tables in 1906 by J.S. Haldane, considerable research and effort have been expended in the development of safer and more rapid decompression procedures and tables. Most models of decompression that have been used to generate decompression tables have taken a deterministic approach where the boundary between 'safe' and 'unsafe' dives is governed by a fixed set of rules, depending on the gas exchange model and 'safe ascent' criterion that are selected. These models are essentially empirical and not physiological models, providing 'safe' decompression only over a limited range of depth and bottom times. Because decompression illness (DCI) is considered a binary event, it becomes logistically and financially impossible to conduct enough dives to show that a given dive profile is safe within statistical significance. DCI should actually be considered as a probabilistic event. Decompression profiles are not just a case of being 'safe' or "unsafe" but should be considered as a time-depth dosage.

The Handbook on Experimental Statistics has been prepared as an aid to scientists and engineers engaged in Army research and development programs, and especially as a guide and ready reference for military and civilian personnel who have responsibility for the planning and interpretation of experiments and tests relating to the performance of Army equipment in the design and developmental stages of production. The present document, section 2, provides detailed procedures for the analysis and interpretation of enumerative and classificatory data.

viii, 86 p. 23 cm

viii, 86 p. 23 cm

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

UC Berkeley Vision Science 298, lecture 1. Given Jan 24, 2013. Lecturer is Stan Klein, Professor of Vision Science, Neuroscience and Bioengineering at UC Berkeley.

UC Berkeley Vision Science 298, lecture 1. Given Feb 7, 2013. Lecturer is Stan Klein, Professor of Vision Science, Neuroscience and Bioengineering at UC Berkeley.

The paper discusses research which has been completed in the areas of chi-square testing, weak convergence of empirical distribution functions of random variables subject to perturbation and scale factors, selection procedures in two-way lay outs, some aspects of nonparametric inference, Bahadur efficiencies of some tests for uniformity on the circle, rank tests for some linear hypotheses in paired comparison designs, and nonparametric procedures. The work in progress includes nonparametric procedures for exchangeable processes and other classes of stochastic processes, for incomplete block designs, circular data, and multiple regression.

xxvii, 847, 6 p. : 27 cm

The Bilingual Reading Study employs several measures of oral language proficiency: (1) a standardized oral language proficiency test, the Language Assessment Scales (LAS); (2) teacher ratings on the Student Operational Language Assessment Scale (SOLA); (3) teacher ratings on the Southwest Educational Development Laboratory (SEDL) Oral Language Proficiency Scale (OLPS); and (4) an ethnographic verification of each child's linguistic capability based on audio tapes of the child in the classroom, on the playground, and at home. In this paper, the reliability and distribution of responses for each measure is presented, followed by a discussion of the relations among the various measures. (BW)

1 online resource (478 S

1 online resource (478 S

This is a registration of the simulated data and syntaxes of the manuscript "A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics".

This quantitative study compared two groups of community college students who were studying elementary statistics. The control group consisted of 38 students in two classes during the fall of 1998, and the treatment group contained 40 students in two classes during the spring of 1999. The treatment group participated in ten data collection and analysis activities in lieu of some teacher-centered instruction. The study determined if treatment students would: (1) show different levels of understanding; (2) write more accurate, detailed, and complete explanations on open-ended essay questions; (3) more readily see applications of statistics; and (4) develop different attitudes and beliefs about statistics. Results showed that students in the treatment group had better grades on the first of three tests (p

This quantitative study compared two groups of community college students who were studying elementary statistics. The control group consisted of 38 students in two classes during the fall of 1998, and the treatment group contained 40 students in two classes during the spring of 1999. The treatment group participated in ten data collection and analysis activities in lieu of some teacher-centered instruction. The study determined if treatment students would: (1) show different levels of understanding; (2) write more accurate, detailed, and complete explanations on open-ended essay questions; (3) more readily see applications of statistics; and (4) develop different attitudes and beliefs about statistics. Results showed that students in the treatment group had better grades on the first of three tests (p

This quantitative study compared two groups of community college students who were studying elementary statistics. The control group consisted of 38 students in two classes during the fall of 1998, and the treatment group contained 40 students in two classes during the spring of 1999. The treatment group participated in ten data collection and analysis activities in lieu of some teacher-centered instruction. The study determined if treatment students would: (1) show different levels of understanding; (2) write more accurate, detailed, and complete explanations on open-ended essay questions; (3) more readily see applications of statistics; and (4) develop different attitudes and beliefs about statistics. Results showed that students in the treatment group had better grades on the first of three tests (p

This quantitative study compared two groups of community college students who were studying elementary statistics. The control group consisted of 38 students in two classes during the fall of 1998, and the treatment group contained 40 students in two classes during the spring of 1999. The treatment group participated in ten data collection and analysis activities in lieu of some teacher-centered instruction. The study determined if treatment students would: (1) show different levels of understanding; (2) write more accurate, detailed, and complete explanations on open-ended essay questions; (3) more readily see applications of statistics; and (4) develop different attitudes and beliefs about statistics. Results showed that students in the treatment group had better grades on the first of three tests (p

This article is from Sensors (Basel, Switzerland) , volume 10 . Abstract This paper presents a complete analysis of the positional errors of terrestrial laser scanning (TLS) data based on spherical statistics and 3D graphs. Spherical statistics are preferred because of the 3D vectorial nature of the spatial error. Error vectors have three metric elements (one module and two angles) that were analyzed by spherical statistics. A study case has been presented and discussed in detail. Errors were calculating using 53 check points (CP) and CP coordinates were measured by a digitizer with submillimetre accuracy. The positional accuracy was analyzed by both the conventional method (modular errors analysis) and the proposed method (angular errors analysis) by 3D graphics and numerical spherical statistics. Two packages in R programming language were performed to obtain graphics automatically. The results indicated that the proposed method is advantageous as it offers a more complete analysis of the positional accuracy, such as angular error component, uniformity of the vector distribution, error isotropy, and error, in addition the modular error component by linear statistics.

In this study, it is aimed to gain the ability to transform what nature offers into a work of art by using traditional methods. The study has two main aims. Its first purpose is to present ways to transfer environmental literacy skills through art education, as a value that should be acquired at an early age. The second is to contribute to the development of hand skills, to gain creativity, to acquire individual skills and to develop basic social skills such as self-confidence, through artistic activities to be applied to the target audience. The participants of the research were carried out with 124 students studying in the 7th and 8th grades of Süleyman Demirel Secondary School located in Gonen District of Isparta. The analysis of the data was carried out using the SPSS 25.00 statistical program using descriptive statistics and simple correlation analysis. In the study, the application methods of traditional handicrafts were combined with materials obtained from nature for the purpose of the study. According to the results obtained; It has been observed that awareness about how nature is transformed into an art object through artistic activities. The activities within the scope of the study were planned as a result exhibition and a total of 13 activity titles under two categories, conceptual and artistic. In artistic activities, it is planned to carry out activities for the transformation of materials collected from nature by using the methods and techniques of traditional handicrafts. In addition to materials that can be collected from nature such as mud, stone and cones, art materials such as felt, wool, natural stone and similar art materials obtained from nature were used in the activities. [For the complete proceedings, see ED631021.]

The Handbook on Experimental Statistics has been prepared as an aid to scientists and engineers engaged in Army research and development programs, and especially as a guide and ready reference for military and civilian personnel who have responsibility for the planning and interpretation of experiments and tests relating to the performance of Army equipment in the design and developmental stages of production. The present document, section 1, provides an elementary introduction to basic statistical concepts and furnishes full details on standard statistical techniques for the analysis and interpretation of measurement data.

The Handbook on Experimental Statistics has been prepared as an aid to scientists and engineers engaged in Army research and development programs, and especially as a guide and ready reference for military and civilian personnel who have responsibility for the planning and interpretation of experiments and tests relating to the performance of Army equipment in the design and developmental stages of production. The present document, section 1, provides an elementary introduction to basic statistical concepts and furnishes full details on standard statistical techniques for the analysis and interpretation of measurement data.