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Status Tracking
Published in Deborah Sater Carstens, Gary L. Richardson, Project Management Tools and Techniques, 2019
Deborah Sater Carstens, Gary L. Richardson
This section will review the role of pivot tables is generating status reports. The example used will be a pivot table of the Resource Work Availability Report. This report was previously displayed in Figure 18.14 using the standard reporting option. Note that the pivot table option opens in Excel and has two view options. The first view, displayed in Figure 18.16, is a graphic of the pivot table menu, and the second view, displayed in Figure 18.17, is of the resulting pivot table. In Figure 18.16, there is a black circle around the second tab at the bottom of the screen, which says, “Resource Usage.” Clicking on this “Resource Usage” tab produces the pivot table, which is displayed in Figure 18.17. The user can then use the pivot table menu shown on the right side of the screen displayed in Figure 18.16 to customize the pivot table output view. Many of the visual reports can be opened up in Excel and will have the pivot table option available there in native Excel view. However, please note that there are sometimes challenges with compatibility in that a visual report is created in MSP and then moved into Excel. These may not be compatible versions.
Microsoft Excel: The Universal Tool of Analysis
Published in Natalie M. Scala, James P. Howard, Handbook of Military and Defense Operations Research, 2020
Joseph M. Lindquist, Charles A. Sulewski
As previously described, a pivot chart is a dynamic visualization of the pivot table. To see this functionality, we introduce another capability of the pivot table – namely filtering and sorting. Suppose that the demographic of 18–49 was particularly important, and further that it should be displayed in descending order. To accomplish this, note that on the pivot table near the header Row Labels resides a pull-down where a dialog box containing several options for sorting and filtering appears. As observed in Figure 2.5, the analyst can now choose from the many options to customize the data – while simultaneously updating the table of data and the visualization.
An Introduction to Business Intelligence
Published in Deepmala Singh, Anurag Singh, Amizan Omar, S.B. Goyal, Business Intelligence and Human Resource Management, 2023
Pivot tables are significant tools that provide views of multidimensional data in a tabular form. They extract substantial data from large, messy data and enable data consolidation, comparison, and summarization. Pivot tables, on the one hand, aid in calculations, such as counting, sorting, and averaging data in one table, and, on the other hand, present summarized reports on the other table. Pivot tables are important tools for examining information and predicting trends.
What is the state of water infrastructure governance research in Nigeria? A review
Published in Water International, 2022
For data analysis, a combination of descriptive and interpretive synthesis approaches, which ‘includes narrative summary and tabulation’ and content and thematic analysis, was used (Evans, 2002, p. 23). The narrative synthesis approach, according to Popay et al. (2006, p. 5), ‘relies primarily on the use of words and texts to summarise and explain the findings of the synthesis’. Combining descriptive and interpretive synthesis safeguards the validity of systematic review results (Evans, 2002). Descriptive synthesis was undertaken using the pivot table in the Excel database, based on specific identifiers. Article characteristics tabulated included: author, year, publication, publication type, study aim, geographical setting, geographical location, water source, study approach, water governance focus and study funder. With the descriptor, water governance focus, ‘institutional’ denotes publications that investigate water infrastructure fully owned and managed by government institutions. ‘Non-institutional’ refers to publications that investigate water infrastructure with a full or shared management arrangement by nongovernment organizations, for example, WaterAid and community groups. Qualitative content analysis was used to identify and organize the documents for relevant themes, narratives and insights (Taylor-Powell & Renner, 2003) through a content search of these articles. For the results, all years where no publication was identified were excluded, specifically 1990–92 and 1994–2006.
Big data analysis of port state control ship detention database
Published in Journal of Marine Engineering & Technology, 2019
The datasets must be cleaned before they are stored in the data warehouse, as the data source truly used for the big data analysis. Since the basis of association rule mining comes from frequent itemsets, a higher frequency of several items emerging at the same time is more likely to have an implicit rule. Therefore, the descriptive statistics of a pivot table and pivot chart were first used for the preliminary analysis, in order to have a preliminary understanding of the data to guide the subsequent big data analysis. The characteristics of detention ship from a single factor include: the ship type, mostly consists of general cargo/multipurpose, bulk carrier, refrigerated cargo, and oil tanker (Figure 4); the ship age, most ships have an age above 15 years, in particular above 25 years (Figure 5); the gross tonnage, most ships are of less than 5,000 tons (Figure 6). The deficiency mainly concentrates on fire safety, life saving appliance, certificate/document, safety of navigation, pollution prevention, and other aspects (Figure 7). Looking the characteristics of detention ship from the multi-factorial pivot analysis, most ships have the combination of characteristics of ship age exceeding 20 years, gross tonnage being less than 5,000 tons, and ship type being general cargo/multipurpose (Figures 8Figure 9–10). The preliminary analysis only provides an understanding of the data, the discovery of deep and interesting rules is still dependent on the application of big data analysis.
Water governance research in Africa: progress, challenges and an agenda for research and action
Published in Water International, 2019
Ayodele Olagunju, Gladman Thondhlana, Jania Said Chilima, Aby Sène-Harper, W.R. Nadège Compaoré, Ehimai Ohiozebau
Descriptive data analysis was carried out using the Pivot Table feature in Microsoft Excel, which helped generate distribution of articles based on basic identifiers such as authors’ names, journal title, year of publication, country of focus, type of article, scale of analysis, and overall conceptual focus. To explore the conceptual insights from the manuscripts, texts were checked for direct answers to the following questions: What are the dominant concepts/theme(s) that the paper discussed (e.g., gender, conflict or sustainability)? Is water governance defined, and if so, how? What water governance problems are identified? Are measures of effective water governance recommended or applied? If so, what are they? Additional details that provide further analytical insights from the articles were also extracted as notes. All textual data were coded thematically to align with the questions just listed.