Data Analysis Sample Clauses

Data Analysis. In the meeting, the analysis that has led the College President to conclude that a reduction- in-force in the FSA at that College may be necessary will be shared. The analysis will include but is not limited to the following: ● Relationship of the FSA to the mission, vision, values, and strategic plan of the College and district ● External requirement for the services provided by the FSA such as accreditation or intergovernmental agreements ● Annual instructional load (as applicable) ● Percentage of annual instructional load taught by Residential Faculty (as applicable) ● Fall 45th-day FTSE inclusive of dual enrollment ● Number of Residential Faculty teaching/working in the FSA ● Number of Residential Faculty whose primary FSA is the FSA being analyzed ● Revenue trends over five years for the FSA including but not limited to tuition and fees ● Expenditure trends over five years for the FSA including but not limited to personnel and capital ● Account balances for any fees accounts within the FSA ● Cost/benefit analysis of reducing all non-Residential Faculty plus one Residential Faculty within the FSA ● An explanation of the problem that reducing the number of faculty in the FSA would solve ● The list of potential Residential Faculty that are at risk of layoff as determined by the Vice Chancellor of Human Resources ● Other relevant information, as requested

Data Analysis. Alabama Power will ensure appropriate data analysis techniques are used in the collection of the data required for this study.

Data Analysis. The Client assumes the possible editorial responsibility for the use of the Application Services. Both the Client and the Service Provider is responsible for the quality, legality and relevance of the Data and content transmitted for the use of the Application Services. The Client further guarantees to be the owner of the intellectual property rights allowing the use of the Data and content. Consequently, the Service Provider disclaims all liability in the event of non-compliance of the Data and / or the content with the laws and regulations, with the public order or with the needs of the Client. The Client guarantees the Service Provider at first request against any damage that may result from his being called into question by a third party for a breach of this guarantee. More generally, the Client is solely responsible for the content and messages broadcasted and / or downloaded via the Application Services. The Client remains the sole owner of the Data constituting the content of the Solutions.

Data Analysis. The survey will comprise Yes and No questions and Likert scales. For the first type of questions, the analysis will provide frequencies of responses. For Likert scales, the analysis will provide mean average of results as well as frequencies of responses for each item in the scale. Depending on final number of interviews, the answers to the introductory session describing respondents would allow to segment results and identify which factors drive and inhibit acceptance by gender, age, family status, etc. The final model will be instrumental for the analysis and assessment of MAtchUP social acceptance in the 3 Lighthouse cities but can be also a very powerful tool after project’s end. Indeed, the analysis of results can be leveraged for several activities, also in the post-project exploitation phase: 1. To monitor the evolution of social acceptance over time and verify which variables drive change (those variables represent factors that need attention when deciding on next steps in the exploitation phase). 2. To identify areas of improvement for MAtchUP. As an example, if awareness is a key problem, then recommendations can be made on how to better promote the existence of the solution. If citizens using electric vehicles disliked the support, then actions should be taken to improve it. Etc. 3. To personalize the value proposition according to citizens’ needs and perceptions. The segmentation of results by age bands can for example drive the ideation of different value propositions for young people versus elderly and decide on ways to promote the project and overall go-to-market strategy.

Data Analysis. The three curriculum representations of Xxx xxx Xxxxx (1998) were used to analyse the different data. We used Atlas.ti (Scientific Software Development GmbH, Berlin, Germany) for the analysis of the interviews. The derived analytic scheme was used by the first two authors to code all interviews during several rounds until full agreement was reached. The other data sources were analysed separately by the two first authors. To determine the aim of the intended curriculum, we reviewed the study guides and analysed the data from the interviews with the teacher educators and the heads of department. In the study guides, we scrutinised all texts to search for references to (the development of) community competence. We included all sentences referring to the acquisition of community competence in the mission/vision statement, the learning aims, the course descriptions, and the assessment procedure. From the interviews, we used those parts in which the interviewees described what they considered to be the ideal way to educate student teachers in community competence. A distinction was made between their views on the importance of community competence for the profession and their views on the role of teacher education institutes. The implemented curriculum was analysed on the basis of interviews with teacher educators, group observations, and the logs of the electronic learning environments used by groups. As mentioned before, we may expect teacher educators not only to recognise the importance of community competence, but we also expect them to stimulate community competence development by organising collaborative activities, including activities focusing on reflection on and assessment of community competence development. Therefore, during our analysis we searched within the interviews for teacher educators’ comments about the way they stimulate community competence, and categorised these statements into the three main categories: collaborative activities, reflection and assessment. The collaborative activities are configured within different group arrangements: mentor groups, subject matter groups, reflection groups, and research groups. The activities within these types of groups, together with reflection and assessment, have an important role in the curriculum. Student teachers present their reflections in electronic portfolios, which are used by the teacher educators as a basis for assessment. Comments about the electronic learning environment were also consi...

Data Analysis. All error values were expressed as their mean ± standard deviation (SD). Statistical analysis of data was performed using Xxxxxx’x homogeneity test and ensured all sample group data was of acceptable distribution (P > 0.05) before statistical significance between the sample groups was assessed by one way analysis of variance (ANOVA) tests with post-hoc Tukey analysis in Origin 2016. Statistically significant differences were assumed when p ≤ 0.05.

Data Analysis. 842. The Parties acknowledge that the Consultant for the ACLU Agreement is preparing a report, in consultation with an independent statistical expert, which assesses data regarding investigatory stops completed by CPD officers for the period between 2018 and 2020 (“Report”). With respect to the disparate impact compliance methodology for this Report, the City has agreed that the Consultant may (1) assume that a prima facie showing under ICRA based on disparate impact on the basis of race has been satisfied, and (2) forego that analysis. The Parties recognize that the methodology for this Report includes, but is not limited to, an analysis of the following: a. Total CPD investigatory stops citywide and by police district, broken down by racial/ethnic identity; b. Comparison of investigatory stop share to population share by race/ethnicity; Case: 1:17-cv-06260 Document #: 1096 Filed: 06/27/23 Page 14 of 26 PageID #:20841 c. Protective xxx xxxxx, searches, and enforcement actions by race/ethnicity; d. Hit-rate analysis for all contraband, firearms/weapons, drugs, and cannabis, including variations in hit rates between police districts; and e. Analysis of the boxes most often checked by officers to document reasonable articulable suspicion, including variations by race/ethnicity and by police district.

Data Analysis. Canopy cover for the population is estimated at 10%, although densitometer readings were taken before the majority of the trees had begun to produce leaves. Soil was very shallow at the site, measuring approximately 5.0 cm in depth. The soil sample collected was not large enough for proper testing. The average number of plants was of 8.5 per m2. The average number of flowering plants was 5.5 per m2. The average number of immature plants was

Data Analysis. This includes a detailed discussion of the method of data evaluation, including appropriate statistical methods that will allow for the effects of the Demonstration to be isolated from other initiatives occurring in the State. The level of analysis may be at the beneficiary, provider, and program level, as appropriate, and shall include population stratifications, for further depth. Sensitivity analyses may be used when appropriate. Qualitative analysis methods may also be described, if applicable.

Data Analysis. Microsoft Excel and SPSS-11 were used to perform the statistical analysis and to assess numeric trends. Intraclass Correlation (ICC) was used to measure the level of agreement among physicians and nurses. There are two approaches to ICC: consistency and absolute agreement. The difference between consistency and absolute agreement measures how the systematic variability due to raters or measures is treated. If that variability is considered irrelevant, it is not included in the denominator of the estimated ICCs, and measures of consistency are produced. If systematic differences among levels of ratings are considered relevant, rater variability contributes to the denominators of the ICC estimates, and measures of absolute agreement are produced. In the current study, we used the consistency approach due to the fact that it is more suitable to Kappa statistic in our later analysis. K statistic was employed to measure the level of agreement among the physicians themselves and among the nurses themselves (quadratic weighting). The K statistic is based on a formula developed by Fleiss [13], which provides a numerical measure of agreement among multiple raters. Xxxxx’x Kappa coefficient was used to test levels of agreement between the two nurses in each unit, Xxxxx’x Kappa is more suitable than Fleiss13 K statistic to examine inter-observer agreement between two raters. The Kappa statistic measures the observed amount of agreement adjusted for the amount of agreement expected by chance alone. A value of −1.00 indicates complete disagreement, a value of 0 indicates that the agreement is no better than chance, and a value of +1.00 indicates a perfect agreement. In addition, Chi square analysis was performed in order to examine the differences between the two units in the staff members’ ratings.