Statistics & the Single Girl: Incorporating Statistical Literacy into Information Literacy Instruction

This article will address how subject specialists and general instruction librarians can integrate numeric information (in the form of statistics) into library instruction sessions and information literacy programs. In this context, data refers to numeric datasets which typically require analysis using statistical or spreadsheet software and statistics refers to compiled or summarized data

Visualising category recoding and numeric redistributions

This paper proposes graphical representations of data and rationale provenance in workflows that convert both category labels and associated numeric data between distinct but semantically related taxonomies. We motivate the graphical representations with a new task abstraction, the cross-taxonomy transformation, and associated graph-based information structure, the crossmap. The task abstraction supports the separation of category recoding and numeric redistribution decisions from the specifics of data manipulation in ex-post data harmonisation. The crossmap structure is illustrated using an example conversion of numeric statistics from a country-specific taxonomy to an international classification standard. We discuss the opportunities and challenges of using visualisation to audit and communicate cross-taxonomy transformations and present candidate graphical representations.Comment: 6 pages, 3 figures. Accepted to (Vis + Prov) x Domain workshop at IEEE VIS 202

Finding Meaning in the Madness: Unifying Your Library’s Data Collection with LibAnalytics

Libraries have collected data across multiple platforms and areas and developed reports to inform their stakeholders to show the value of the library. As data collection methods have evolved, more assessment platforms have become available in the market. LibAnalytics can help you pull together an all-inclusive, real-time assessment of your library’s services. At the University of Maryland, Priddy Library, we implemented LibAnalytics in 2011 to centralize our data collection points on numerous library services. Learn how this personalized tool has helped the Priddy Library aggregate statistics on library services such as gate counts, circulation and acquisition statistics, interlibrary loan activity, reference statistics and library instruction sessions. The presentation will focus on how we developed a customized instance based on library services and the assessment needs specific to our library. Participants will brainstorm data collection points based on their library services and how to group data points logically. The presentation will discuss how to create a dataset, categorize data entry fields, develop questions, select data field types (i.e. numeric, single or multi option dropdown menus, or text fields), and edit existing datasets. In a few simple steps, learn how to filter your data, generate custom reports, create visualizations, analyze library usage instantly, and produce shareable dashboards to provide a comprehensive overview of your library’s metrics. Through these features, learn how your library can assess trends across multiple years, make data driven decisions to improve services and demonstrate the library’s value

On the perils of categorizing responses

The assumptions underlying the categorization of numeric measurements are examined and it is concluded that some numeric data that are measured by categories might better be obtained by direct estimates. Statistical tests are performed on artificially generated data of normal, triangular and empirically measured distributions, and on various categorizations of these data. It is shown that categorization can markedly affect the outcome of significance tests, in some cases leading to both Type I and Type II errors. When high local densities of values are numerically separated by categorization, test statistics can be substantially inflated from the uncategorized values. It is recommended that response categorization be subjected to the same critical analysis as data transformation techniques like arbitrary dichotomization

Data Displays [6th grade]

This unit was created to cover the data displays portion of statistics and measurement standards. Students will create data displays correctly, and use their knowledge to decide which data displays works best for their data and why. The students will focus to understand that - Not every data display is appropriate for data given. - Each data display has its own purpose. - The way information is displayed can skew a person’s perception of it. They will do this through representing numeric data graphically including dot plots, stem-and-leaf plots, histograms, and box plots, and using the graphical representation of numeric data to describe the center, spread, and shape of the data distribution. The will also analyze the shape and what that tells us about the data

Time Resolved Gain and Excess Noise Properties of InGaAs/InAlAs Avalanche Photodiodes with Cascaded Discrete Gain Layer Multiplication Regions

To predict pulse detection performance when implemented in high speed photoreceivers, temporally resolved measurements of a 10-stage InAlAs/InGaAs single carrier multiplication (SCM) avalanche photodiode (APD)\u27s avalanche response to short multi-photon laser pulses were explained using instantaneous (time resolved) pulse height statistics of the device\u27s impulse response. Numeric models of the junction carrier populations as a function of the time following injection of a primary photo-electron were used to create the probability density functions (pdfs) of the instances of the avalanche buildup process. The numeric pdfs were used to generate low frequency gain and excess noise models, which were in good agreement with analytic models of multiple discrete low-gain-stage APDs and with measured excess noise data. The numeric models were then used to generate the instantaneous and cumulative instantaneous low order statistics of the instances of the impulse response. It is shown that during the early times of the impulse response, the SCM APDs have lower excess noise than the pseudo-DC measurements and the common APDmodels used to describe them. The methods of determining the time resolved low order statistics of APDs are described and the importance of using time-resolved models of APDgain and noise is discussed

Statistics of Impedance, Local Density of States, and Reflection in Quantum Chaotic Systems with Absorption

We are interested in finding the joint distribution function of the real and imaginary parts of the local Green function for a system with chaotic internal wave scattering and a uniform energy loss (absorption). For a microwave cavity attached to a single-mode antenna the same quantity has a meaning of the complex cavity impedance. Using the random matrix approach, we relate its statistics to that of the reflection coefficient and scattering phase and provide exact distributions for systems with beta=2 and beta=4 symmetry class. In the case of beta=1 we provide an interpolation formula which incorporates all known limiting cases and fits excellently available experimental data as well as diverse numeric tests.Comment: 4 pages, 1 figur

The Predictive Approach to Teaching Statistics.

Statistics is commonly taught as a set of techniques to aid in decision making, by extracting information from data. It is argued here that the underlying purpose, often implicit rather than explicit, of every statistical analysis is to establish a set of probability models which can be used to predict values of one or more variables. Such a model constitutes 'information' only in the sense, and to the extent, that it provides predictions of sufficient quality to be useful for decision making. The quality of the decision making is determined by the quality of the predictions, and hence by that of the models used. Using natural criteria, the 'best predictions' for nominal and numeric variables are respectively the mode and mean. For a nominal variable, the quality of a prediction is measured by the probability of error; for a numeric variable, it is specified using a prediction interval. Presenting statistical analysis in this way provides students with a clearer understanding of what a statistical analysis is, and its role in decision making.Statistics, teaching, prediction, probability model, prediction interval.

Measures of Variability for Bayesian Network Graphical Structures

The structure of a Bayesian network includes a great deal of information about the probability distribution of the data, which is uniquely identified given some general distributional assumptions. Therefore it's important to study its variability, which can be used to compare the performance of different learning algorithms and to measure the strength of any arbitrary subset of arcs. In this paper we will introduce some descriptive statistics and the corresponding parametric and Monte Carlo tests on the undirected graph underlying the structure of a Bayesian network, modeled as a multivariate Bernoulli random variable. A simple numeric example and the comparison of the performance of some structure learning algorithm on small samples will then illustrate their use.Comment: 19 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:0909.168