Data Description. 3.1.1.1 Task(s)/group(s) responsible for generating data
Data Description. The following CMS data file(s) is/are covered under this Agreement.
Data Description. The following CMS data file(s) is/are covered under this Agreement. (note for form creator - change the header column for System of Record to “Charge Per Year*” and add a “Total” on bottom line) (the file will be prefilled in with “MEDPAR File Extract – Provider # ”) “Medicare Provider Analysis and Review (MEDPAR), HHS/CMS/OIS, 09-70-0514” Privacy Act System of Records, published at 71 Fed. Reg. 17470 (April 06, 2006) *For pre-FFY 1996, the charge is $1,200 per year (FFY or provider cost year), per provider; For FFY 1996 thru December 7, 2004, the charge is $900 per year (FFY or provider cost year), per provider; Where the cost year includes part of both FFY 1995 and FFY 1996, the charge is $900.
Data Description. 3.1.1.1 Task / group responsible for generating data T1.1.2, Xxxxx XXXXXXX, UOXF
Data Description. 3.9.1.1 Task(s)/group(s) responsible for generating data T1.2.6 (UPM)
Data Description. Eden Development Options • Southern New South Wales Mill Survey • ABS Sociodemographic Data • Composite Mineral Potential CRA Eden Region • Cumulative Mineral Potential CRA Eden Region • Eden 100 metres ESOCLIM climate surfaces • Eden Region CRA Mineral Potential Tracts (12 maps) • Eucalyptus nitens Plantation Potential • Land Use Dataset - Eden CRA Region • Pinus radiata Plantation Potential • Private plantations • Weighted Composite Mineral Potential CRA Eden Region • Eden area input-output table - forestry based industry profiles and multipliers • Geochemical Fertility Index for the Bega 1:250,000 geology sheet • Substrate Lithology Classification of the Bega 1:250,000 geology sheet • Substrate Stability Index for the Bega 1:250,000 geology sheet • EPA Erosion Hazard Classification • Eden Region CRA Geological Coverage • Eden Region CRA Industrial Mineral Occurrences • Eden Region CRA Metallic Mineral Occurrences • Exploration Licences - Eden Region CRA • Mining Titles - Eden Region CRA • Community Attitudes to Forests • Forest User Survey Data • Eden CRA - 1994 Landsat Thematic Mapper Classification of Cleared / Non-cleared Land • Eden CRA - 1997 Landsat Thematic Mapper Classification of Cleared / Non-cleared Land • Eden CRA - Conservation Assessment Database • Eden CRA - Interim Logging History 1972 - 1997 • Eden CRA - Landsat Thematic Mapper Difference Classification 1994-1997 • Eden CRA - Landsat TM Image of Eden NSW, 1994 • Eden CRA - Landsat TM Image of Eden NSW, 1997 • Eden CRA - National Estate - Aesthetics Values Context Layer • Eden CRA - National Estate - Centres of Endemism (Flora) • Eden CRA - National Estate - Delineated Natural Landscapes • Eden CRA - National Estate - Disjunct Species • Eden CRA - National Estate - Ecosystems • Eden CRA - National Estate - High Biodiversity • Eden CRA - National Estate - Refugia • Eden CRA - National Estate - Relictual Species • Eden CRA - National Estate - Undisturbed Catchments • Eden CRA - National Wilderness Inventory (NWI) Delineated Boundary • Eden CRA - National Wilderness Inventory Database (NWI) • Eden CRA - Register of the National Estate (RNE) - Continental Database • Aboriginal Land Claims • API Floristics Layer • Broad Forest Disturbance History - Eden CRA Region • Clearing 1k • Clearing 2k • Contextual GIS layer of Non Indigenous Historic Sites within the Eden CRA Region • Coupes / logging history on declared NPWS after IAP • Danindex • Decorticating Bark Index • Digital Elevation Model (DE...
Data Description. Analyses involved two samples (A498, a tumor line, and hREF, a pool of healthy tissues), each with three technical replicates for all platforms considered (Affymetrix GeneChip⃝c xxXXX Array, Agilent Human xxXXX Microarray (V1) and Illumina humanMI_V2). XxXXX selection described in the Experimental Section, resulted in a total of 813 human miRNAs considered for analysis, which account for 95.99% of human miRNAs on Affymetrix platforms, 95.53% on Agilent and 94.76% on Illumina (see Figure 1). Pairwise intersections of human xxXXX lists revealed that the larger overlap occurred when Affymetrix and Agilent were considered (830 miRNAs, 97.99% of Affymetrix hsaand 97.53% of Agilent hsa), whereas Illumina showed a slightly poorer degree of overlap with both Affymetrix (817 miRNAs, 96.46% of Affymetrix and 95.22% of Illumina) and Agilent (815 miRNAs, 95.77% of Agilent and 94.99% of Illumina).
Data Description. (Xxxxxxx XXXX) After understanding the data properly, next we used Kolmogorov-Smirnov test to determine the distribution of the variables. From the 1-KS test it showed that our variables are normally distributed or Test distribution is normal. Also, our variables are interval scaled. After determining the distribution of the dependent and independent variables we selected Xxxxxxx’x correlation to test our hypotheses or the relation between the independent and dependent variables. Understandability Based on the calculation, the Xxxxxxx’x correlation value showed that there’s high significance and correlation between user TimeSpent and PreferredUserCognizance, r = 0.94, p = 0.00. There was also high significance and correlation between OverallUserUnderstanding and PreferredUserCognizance, r = 0.93, p = 0.00. From the results, we could verify our hypothesis that the more time a user spends on reading the XXXX the more she prefers to know the Terms and Conditions of the software. We could also verify that the more the user is interested in the overall understanding of the User Agreement the more she prefers to know the Terms and Conditions of the software. The result of the calculation validated our third hypothesis. Digest From the results, we saw that there was good significance and correlation between SummarizingOverallEULA and ShorterEULA, r = 0.40, p = 0.03. Based on the result we could verify our hypothesis that a shorter length XXXX is perceived as a summarized XXXX. This means that the best way to represent a xxxxxxx XXXX is to summarize the contents of the XXXX instead of showing the user a huge textual representation of the XXXX. There was less significance and correlation between SummarizingOverallEULA and PreferredUserCognizance, r = 0.22, p = 0.25, which was against our hypothesis expectation that if the Terms and Conditions is summarized then we expect to have more user preferring to read the Terms and Conditions of the software. We suspected that we got poor correlation and significance value due to low data points. We would have required more data to strongly validate our hypothesis. We got poor significance and correlation between InfographicEULA and PreferredUserCognizance, r = -0.09, p = 0.64, which was also against our hypothesis. We expected good correlation and significance between these since if the Terms and Conditions page is in Infographic form then it should have more user preferring to read the Terms and Conditions. This in turn wou...
Data Description. For the empirical example we employ a data set on rating information pro- vided by Oesterreichische Nationalbank, the Austrian central bank. The data contain rating information (one-year PD estimates) from 13 major Austrian banks on 2090 obligors in September 2007 and cover a significant share of the Austrian credit market. For each obligor, at least two PD estimates are available. The number of co-ratings (occurrences of ratings of a single obligor by two different banks) is 5460. In addition to the PD estimates we have cross-sectional information about the obligors, like legal form, industry affiliation and outstanding exposure. Table 3.1 reports descriptive statistics of the data set. Min. Median Max. Mean Number of obligors per bank 70 182 1700 420 Size of banks measured by their total assets (in Euro billions) 1.0 8.7 128.5 11.8 Table 3.1: Descriptive statistics of the characteristics of the rating informa- tion and the 13 Austrian banks in the data set. Note that even the smallest bank has at least 70 obligors in common with one or more of the other institutions. Apart from looking at the number of co-ratings on a bank level, we also compute the number of co-ratings on an obligor level. The median number of these co-ratings is 2, suggesting that most obligors have business relations to only a small number of banks. For a deeper analysis we group all obligors by their industry affiliation and their legal form. Based on the NACE codes (European Commission, 2008) we classify obligors to nine main industries. Table 3.2 shows the distribution of the obligors across the industries. Table 3.2 shows that the total numbers of co-ratings (5460) is not uniformly Label Industry No. of co-ratings No. of co-ratings (%) Manufac Manufacturing 938 17.2 Energy Energy & Environment 180 3.3 Constr Construction 184 3.4 Trading Trading 641 11.7 Finance Financial Intermediation 1737 31.8 RealEst Real Estate & Renting 754 13.8 Public Public Sector 344 6.3 Service Service 435 8.0 Private Private Individuals 247 4.5 Total 5460 100.0 Table 3.2: Distribution of the co-ratings of the 13 Austrian banks across industries. distributed across the nine industries, ranging from 180 co-ratings in Energy & Environment (“Energy”) to 1737 co-ratings in Financial Intermediation (“Finance”). With 13 banks and 9 industries there are 117 possible sub- portfolios to be analyzed. However in 17 of these there are no observations. In addition the obligors can be grouped with aspect to their legal...
Data Description. 3.3.1.1 Task / group responsible for generating data T1.2.4/IEM HAS
