Multidimensional Analysis of Snow Cover Data in South Dakota Diversity of Landscapes

Snow distribution in SD was studied with Factor Analysis (FA) of monthly total snowfall [in]. The long-term data obtained from the High Plains Regional Climate Center were used for the territory of South Dakota. The perspective for creating an Atlas of Climate and Water Resources for SD directed this study of total monthly snowfall with connection to landscape diversity.

The initial matrix {Xn*p} where n is number of stations and p is number of variables of monthly average for period of observations. The maximum number of stations n with mutual interval of observations for SD is equal 93 (n=93). These stations have mutual time interval of 18 years observations (1952-53 – 1969-70). Total monthly snowfall has data for p=10 is number of months with observation and p=11 is number of months with observations with total snowfall for the winter season. The second matrix contains proportions of total monthly snowfall to total annual (proportion is the monthly total snowfall divided on total seasonal snowfall); the number of rows n and the number of variable are the same as in the first case: n=93 and p=10, 11.

The average annual sum of total monthly snowfall (September-June) for SD 34.64 in obtained on 93 stations for 1952-1970, the median is 31.84 from the same data; ranged from 10.21 to 152.27 from the same data. The most variable month is April with average
4.89 [in], median 3.67, min 0.75 and max 33.18; the average proportion for April is 0.13, min 0.03 and max 0.24. The averages for November to April grow as sequence: 3.60, 5.70, 4.88, 7.25, 7.46 and 4.89 the variability of those months has sequence: 2.49 2.52 2.25 3.33 3.53 and 4.87. The Pearson coefficient of correlation for the monthly snowfall averages from September to June with annual sum has sequence: 0.83, 0.92, 0.96, 0.92, 0.93, 0.93, 0.94, 0.92, 0.90, and 0.82; the correlation for the monthly proportions from September to June with annual sum has sequence: 0.38, 0.45, -0.01, -0.32, -0.33, -0.21, -0.15, 0.27, 0.36, and 0.35.

The FA of both initial matrixes had extracted two factors for monthly observations with incorporation of 93 % of total variability of the data in the model; and three factors model with 70% variability of data for monthly proportions. The two factor groups in model of observed snowfall contain winter months (Dec – Mar) and all other. The model of monthly proportions with tree factor groups include winter months in two group and combination of spring and fall months in third. The factor scores presented as maps for SD allow trace distribution of factor groups of monthly snowfall patterns for two models through the territory of SD. The combination of factor scores distribution for two models is in a good agreement with main landscape regions and subregions of SD.
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Precipitation in Aberdeen, SD: data analysis approach

The daily, monthly and annual sum of precipitation was analyzed for station Aberdeen-Airport, South Dakota (NOAA COOP #390020) with traditional normative statistical descriptions of precipitation variability and with methods of data analysis. The goal for research was formulated as finding a way to forecast extremes, such as the flooding of Aberdeen in May 2007 after a 30-hour, 8.42 inch rain event. Daily data were available, analyzed and cannot be considered as having any use for the forecast. The observations of 1423 monthly sums of precipitation have the following characteristics: mean = 1.802 in.; geometric mean = 0.856 in.; median = 1.26 in.; minimum = 0.0; maximum = 12.39 in. Simplified Fourier analysis was performed for monthly data, and 31 cyclic components were determined and used in a model to represent the data with periods from 4 to 379 months. The biggest amplitude from those components belongs to period of 12 months (1.396), and the next largest amplitude (0.261) had a period length of 6 months. The lowest amplitude (0.033) has a period of 75 months; the longest period 379 months has an amplitude of approximately twice that height (0.066). Fourteen periods have amplitudes higher that 0.1. The same analysis was performed on data obtained from the High Plains Regional Climate Center that was collected between January 1932 and April 2006. The model has mean equal 1.623 in. and the same two periods: 12 and 6 months, - with the biggest amplitudes: 1.315 and 0.258, and the model showed the increase in precipitation during May 2007 (3.494 in) that bigger that in April (2.965 in), June (3.115 in) 2007, and May (2.246 in) 2006. Mean annual precipitation for the time interval 1891- 2007 was 21.627 in. Simplified Fourier analysis presented a model of annual variability with eight cyclic components. The biggest amplitude (2.26) has the longest period of 46 years; the smallest (0.336) has a period of 31 years. Five components (46, 3, 10, 24, and 8 years) have amplitudes larger that 1.0 The same analysis for annual sum for time interval 1932-2006 presents mean = 19.514, and a model with five components of 3, 7, 14, 18, and 26 years with sequential altitudes 1.525, 1.451, 1.084, 1.066, and 1.451. The model shows that 2007 annual precipitation is moderately greater than the long-term mean value. During 118 hydrological years, the mean of annual sum for hydrologic year is 21.617 in., the minimum is equal 9.07 and maximum is equal 41.44. The highest mean of 3.704 in. for monthly sum during this time interval has June, the second of 2.948 in. has May; but May has the highest maximum equal 12.39 and June only has 10.91. The factor analysis on the matrix of monthly sum of precipitations {P118*12,13} shows that the annual sum mostly associated with May and August; the seasonal variability may be described by five factors (cumulative variance reflected by the model is equal 57.4%). The seasonal regime may be well represented by the factor scores; four factors charts (for factors 1, 3, 4, and 5) show different degrees of decrease in sum of precipitations for analyzed interval. In contrast, for factor 2 slightly increased. The assumption of possible forecast for precipitation and connected flooding was considered based on the obtained results. The highest daily sum of precipitation may be expected in month with biggest monthly sums in May and June. The time series of annual sum and monthly sums of precipitations have cycles with periods of 24, 31 and 46 years, and 286 and 379 months; traditional tools of characterization variability have to be applied to the longest as it is possible time series

The spatial temporal regime of stream flow of the conterminous U.S. in connection with indices of global atmospheric circulation

Long-term stream flow records (1929-1988) from seventy one U.S. Geological Survey gauging stations with drainage area in range 1000-10000 sq mi were analyzed using multivariate statistics. Factor analysis of average annual flow revealed seven patterns of river runoff within seven distinct regions of the territory. This factor model reflected 69% variance of the initial matrix. The second set of stream flow records (1939-1972) from ninety-seven gauging stations was used as control. This set contains all seventy one from first one and additional stations with shorter observation period. Factor analysis of this expended set again yielded seven factors (69% variance of the initial matrix) with very similar spatial distribution of gauging stations.

Every group of watersheds obtained as a factor was presented by one gauging station with time series of annual discharges (1- 05474000, 2- 14321000, 3- 07019000, 4- 0815000, 5- 11186001, 6- 01666000, 7- 06800500) as the most typical for group. For the same time interval, streams represented by all patterns have increasing values (i. e. the positive difference between two time subintervals); but only the positive linear trend for patterns 1 and 7 are statistically significant. 

For the seven typical flow records, monthly average values were obtained from three to five seasons composed from different ensembles of months. 

For each annual time series of the typical seven stream flow patterns, regression equations were obtained from indices of global atmospheric circulation (AO, NAO, NPO and AAO). The equations contain from one to five variables (predictors) and have coefficients of correlation from 32% to 73%. 
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Single-Editor Editions from Manuscript: \u3ci\u3eThe Journals of Theodore Parker\u3c/i\u3e

Anyone who undertakes to edit a text must necessarily make some basic decisions about the nature of that text and the purpose of the final edition. The editorial plan on which this edition is based was derived from a series of premises on the nature of the Parker manuscripts and on my purpose in editing them. The journal manuscripts are massive, encyclopedic documents rendering Parker\u27s thought coherently, if not always according to strict grammatical usage. They are private, unpublished documents written in a hand that is cramped and difficult to read, and the manuscript pages are complicated by unformed words, slurred endings, and an extensive use of personal abbreviation. In editing this document, my primary concern is to make the Parker journals available as rapidly as is consistent with the ml)~t elementary requirements for a scholarly edition-accuracy and completeness. My second concern is to present the text in a manner that will retain rather than obscure the inevitable nuance of the rough texture of the journal. The final product will be an un-modernized, critical, genetic-text edition. It will be un-modernized in the sense that spelling and punctuation will not be altered to conform to recent usage, critical in the sense that it will incorporate certain kinds of editorial emendations dictated by the editor\u27s judgment, and genetic in the sense that cancellations and insertions will be noted directly in the text

An Analysis of the Questions on University Teaching Surveys and the Universities that Use Them: The Australian Experience

This paper is the first attempt to perform an analysis of the internal Quality of Teaching Surveys (QTS) used in all Australian Universities by investigating how they compare across Universities. We categorize the questions on each university’s QTS into one of 18 types and then define a proximity measure between the surveys. We then use an agglomerative cluster analysis to establish groupings of these institutions on the basis of the similarity of their QTSs as well as groupings of question types by their frequency of use. In addition, we also determine if the form of the survey is related to the responses recorded by the Course Evaluation Questionnaire (CEQ) that is administered to all graduates of Australian Universities. This was done by the use of regression analysis to establish if the form of the questionnaire is related to the overall good teaching scores earned by the universities from the CEQ.Tertiary Education; University Rankings; CEQ

A Systematic Analysis of Quality of Teaching Surveys

All tertiary institutions in Australia use the same Course Evaluation Questionnaire (CEQ) however for the internal evaluation of teaching they use their own surveys. This paper performs an analysis of the internal Quality of Teaching Surveys (QTS) used in Australian Universities. We classify the questions within the QTS surveys. This classification is used to explore how different universities’ surveys are similar to each other. We find that some universities use a QTS that is quite distinct from other universities. We also investigate whether there is a particular pattern to the types of questions used in the surveys. We find that there are some question types that are employed widely in a typical survey and others that are All tertiary institutions in Australia use the same Course Evaluation Questionnaire (CEQ) however for the internal evaluation of teaching they use their own surveys. This paper performs an analysis of the internal Quality of Teaching Surveys (QTS) used in Australian Universities. We classify the questions within the QTS surveys. This classification is used to explore how different universities’ surveys are similar to each other. We find that some universities use a QTS that is quite distinct from other universities. We also investigate whether there is a particular pattern to the types of questions used in the surveys. We find that there are some question types that are employed widely in a typical survey and others that are All tertiary institutions in Australia use the same Course Evaluation Questionnaire (CEQ) however for the internal evaluation of teaching they use their own surveys. This paper performs an analysis of the internal Quality of Teaching Surveys (QTS) used in Australian Universities. We classify the questions within the QTS surveys. This classification is used to explore how different universities’ surveys are similar to each other. We find that some universities use a QTS that is quite distinct from other universities. We also investigate whether there is a particular pattern to the types of questions used in the surveys. We find that there are some question types that are employed widely in a typical survey and others that are not. This analysis can be used by universities to determine how their surveys compare to their peer institutions and other institutions across Australia.

Child Mental Health and Educational Attainment: Multiple Observers and the Measurement Error Problem

We examine the effect of survey measurement error on the empirical relationship between child mental health and personal and family characteristics, and between child mental health and educational progress. Our contribution is to use unique UK survey data that contains (potentially biased) assessments of each child's mental state from three observers (parent, teacher and child), together with expert (quasi-) diagnoses, using an assumption of optimal diagnostic behaviour to adjust for reporting bias. We use three alternative restrictions to identify the effect of mental disorders on educational progress. Maternal education and mental health, family income, and major adverse life events, are all significant in explaining child mental health, and child mental health is found to have a large influence on educational progress. Our preferred estimate is that a 1-standard deviation reduction in ‘true' latent child mental health leads to a 2-5 months loss in educational progress. We also and a strong tendency for observers to understate the problems of older children and adolescents compared to expert diagnosis.Child mental health; Education; Strengths and Difficulties Questionnaire; Measurement error

Child mental health and educational attainment: multiple observers and the measurement error problem

We examine the effect of survey measurement error on the empirical relationship between child mental health and personal and family characteristics, and between child mental health and educational progress. Our contribution is to use unique UK survey data that contains (potentially biased) assessments of each child's mental state from three observers (parent, teacher and child), together with expert (quasi-) diagnoses, using an assumption of optimal diagnostic behaviour to adjust for reporting bias. We use three alternative restrictions to identify the effect of mental disorders on educational progress. Maternal education and mental health, family income, and major adverse life events, are all significant in explaining child mental health, and child mental health is found to have a large influence on educational progress. Our preferred estimate is that a 1-standard deviation reduction in 'true' latent child mental health leads to a 2-5 months loss in educational progress. We also find a strong tendency for observers to understate the problems of older children and adolescents compared to expert diagnosis.

Snow Cover in South Dakota: Statistical analysis of spatiotemporal diversity

Snow distribution and accumulation influence many human activities and the dynamic sustainability of ecological systems. Snow cover distribution analysis is a second research step towards creating a Atlas of Climate and Water Resources for SD (temperature analysis was presented last year). We analyzed the regional diversity of monthly total snowfall based on long-term data obtained from the High Plains Regional Climate Center for South Dakota. Multidimensional statistical methods were used, and the results presented for the State of South Dakota. 

The sets of initial matrixes were compiled with snow observations for the state. The first type of initial matrix of time series {Xt*n}, where t = number of years (67) and n = number of meteorological stations (25), contains 25 stations with mutual observational interval (1931-1998) of 67 winters. The second type of initial matrix {Xt*m}, where t = number of years (67) and m = number of months in a year, included seven initial matrixes.

Statistical analysis allowed us to differentiate weather stations by temporal trends and spatial distribution for the time intervals 1931-1998. The average annual sum of total monthly snowfall (October-May) ranged from 25.5 to 53.5 inches for 25 stations trough this time interval. The most variable stations (Bowman Court House, Alexandria, Colony, Gordon, Clark, hot Springs and Eureka) were determined; their seasonality was described (the most variable months and correlation among months during period of snowfall) and their seasonal regime determined. 
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