Sample Selection. To test the developed model we have chosen to input data from two separate cases which consists of a 45-day-time period on both. To incorporate regular working day restriction explained in „laws and regulations‟ we found the job with the longest duration to incorporate this restriction. Looking at the data file for Case 1 in Appendix C, Gazflot3 has duration of 31 days and consequently setting the time horizon to be 45 days. By using the numbers 1-45 the model became a lot more manageable rather than applying the use of actual dates. Case 1 contains historical data from 1st of May 2010 till mid-June, while case 2 starts at the 1st of September and ends 45 days later in mid-October. These months are one of the most challenging with regards to the number of completed jobs to schedule during the year of 2010, and therefore considered good candidates for testing the model. Given the 45-day time horizon some jobs are “cut off” as they have a start date before day 1 or end later than day 45. Yet the data set contains parts of these jobs during the time they are within the time horizon. No historical data other than this set is included in the model.
Sample Selection. The target list consists of all AGN’s observable from Cerro Paranal (δ < 22) and have an estimated total N-band flux density brighter than N = 5 or SN = 400mJy, which is the limiting magnitude for observations with MIDI, estimated from previous N-band measurements available in the literature. This list of 21 objects (without NGC 1068) was taken from the catalogue of Seyfert galaxies by Xxxxxxxxxx et al. (1988) to which we added additional sources from the compilations of Xxxxxxx et al. (1997) and Xxxxxxxx et al. (1995), plus the IR luminous galaxy M83, the starburst galaxy NGC 253 and the famous quasar 3C 273. See Table 2.1 for a complete list. Table 2.1. Observation list and fluxes for all objectsa. Name RA J2000 DEC J2000 D Mpc 11.9µm Flux mJy 8.9µm flux mJy N 2531 00 47 33.1 -25 17 17.2 3.3 0000-0000 0000 N 2532 1160 695 N 13651 03 33 36.4 -36 08 25.5 20.7 606 - N 13652 157 - N 13653 152 - IRS 05189-2524 05 21 01.4 -25 21 44.9 172.6 545 - N 2377 07 24 56.8 -09 39 36.9 31.3 < 60 - MCG-5-23-16 09 47 40.2 -30 56 54.2 31.9 648 - Mrk 1239 09 52 19.1 -01 36 43.5 79.7 638 - N 3256 10 27 51.8 -43 54 08.7 35.4 553 - N 3281 10 31 52.0 -34 51 13.3 40.9 625 - N 3758 11 36 29.0 +21 35 47.8 122 < 60 - N 3783 11 39 01.8 -37 44 18.7 37.2 590 - 3C 273 12 29 06.7 +02 03 08.5 649 345 - N 4594 12 39 59.4 -11 37 23.0 12.4 < 60 - MCG-3-34-6 13 10 23.7 -21 41 09.0 95.4 < 60 - N 5128 13 25 27.6 -43 01 08.8 5.3 1220 635 M 83 13 37 00.8 -29 51 58.6 5.2 232 - ESO 445-G50 13 49 19.3 -30 18 34.4 64.2 352 - Mrk 463 13 56 02.9 +18 22 19.5 20.7 338 - Circinus 14 13 09.3 -65 20 20.6 3.6 9700 4700 N 5506 14 13 15.0 -03 12 27.2 25 908 - N 7469 23 03 15.6 +08 52 26.4 69.2 414 - N 7582 23 18 23.5 -42 22 14.0 21.3 670 - '' aRA and DEC coordinates are coordinates taken directly from the telescope position. Distances are from XXX. RA & DEC error: ±5 . Flux errors are ±15%.
Sample Selection. Customers chosen to be part of a sample population for Hauler Route review inspection shall be selected at random by route.
Sample Selection. Samples will be collected at a designated restroom and time on the same day the student is selected for testing or, if the student is absent on that day, on the day of the student’s return to school. If a student is unable to produce a sample at any particular time, the student will be allowed another opportunity within a reasonable time on that same day to provide a sample. All students providing samples will be given the option of doing so alone in an individual stall with the door closed. *NOTE: Any student not able to produce a sample within a reasonable time on that test day/date will be verified as a positive result for that test.
Sample Selection. The literature review focuses mostly on supply chain management in manufacturing companies. Therefore, I targeted manufacturing companies for the empirical study. After having performed interviews in an SME and some big enterprises, I noticed that the bigger companies have more formal management practices and thus have a higher likelihood to measure and document their management processes. They also have a higher tendency to go beyond their own boundaries than smaller companies, which are often more internally focussed. This is why I decided to focus on big companies executing manufacturing activities for the further research. The SME will be excluded from the cross- case analysis because it does not have formal cost management models and also because its performance management is not fully embedded in the company but lives only in the minds of the management. Additionally, all other participants are big, multinational, stock-listed companies while Gondella is an SME. Excluding Gondella is thus also favourable for comparability reasons. I will not exclude the company from the within-case analysis because it remains interesting to observe how an SME approaches the topics, even though not everything is formally recorded. At ArcelorMittal, contact with the suppliers and clients is conducted at a higher level. The same applies to eventual cost and performance management in the supply chain. Therefore, I could not interview any members of staff who could provide me with the necessary information for this research. All studied companies seem to have particularly internal cost management models, but could provide me with a lot of information on how they involve supply chain partners in cost management while ArcelorMittal could not do so. The company is excluded from this dissertation because the gathered information would be too much out of scope for this study. Managers in the business world are very busy and it is not evident to obtain their e-mail addresses using the Internet. That is why I relied on my personal network of acquaintances to get in touch with people working in big or midsized companies located in Belgium. Once I had a contact person within a company, this person or multiple people helped me to find the right person(s) to interview. An e-mail drafted to request participation can be found in attachment 10.2. Twelve different companies were contacted to participate in the study. Eight of them gave me the opportunity to interview one or multiple per...
Sample Selection. The IRO shall randomly select and review a sample of 50 Stat Lab Requisitions from the Sampling Frame.
Sample Selection. My primary source of mutual fund data is Morningstar Direct. Morningstar Direct pro- vides not only data on fund return and characteristics, but also short bios of fund managers who are in charge of each fund.2 From each mutual fund’s website inside Morningstar Direct, I extract each fund manager’s specific information. This includes their educational background and their graduation year, prior work history, whether they hold financial cer- tificates, and when they received these certificate. Unfortunately, less than 10% of the fund managers sampled have complete information, and some of the observations are in-
Sample Selection. My primary source of mutual fund data is Morningstar, Inc.1 I focus on actively man- aged U.S. domestic open-end equity mutual funds. Following Elton, Gruber, and Blake (1996), I require funds to satisfy a certain lower bound of total net assets (TNA) to alleviate concerns regarding return outliers. I modify Xxxxx et al.’s criteria as described in the first chapter of this dissertation. If a fund has multiple share classes, I value weighted across share classes. In the final sample, I have 4,465 unique funds for the sample period of 1992 to 2014. Mutual fund holdings data is from Thomson Financial (also known as CDA/Spectrum S12). The main source of Thomson Financial data is periodic filings by mutual funds to the SEC (N-30D form). Prior to 1985, the SEC required each fund to report its portfolio holdings every quarter, but the requirement changed to semiannual starting in 1985. How- ever, the majority of funds continued to report every quarter and the SEC returned to the quarterly reporting requirement in February 2004. Further details on the construction of the Thomson Financial database are available in Xxxxxxx (2002). In order to utilize mutual fund data from Morningstar with holdings data from Thomson Financial, it is necessary that I construct a map between the Morningstar, CRSP and Thom- son databases. I follow the methodology provided in Xxxx and Van Binsbergen (2015) and Xxxxx and Xxxxxxxxxx (2015) in mapping between Morningstar and CRSP MFDB. I indepen- dently mapped CRSP MFDB to Morningstar Principia CDs, and then Morningstar Direct to Morningstar Principia CDs using monthly returns, TNA, CUSIP, Ticker, fund names, and dividends to map the datasets. In the end, I was able to map 90.2% of fund-month 1Patel and Xxxxxxxxxx (2015) find that managerial structure data on Morningstar Direct matches Securities and Exchange Commission (S.E.C.) filings 96% of the time and recommend using Morningstar Direct data for mutual fund manager specific analysis. observations in Morningstar to the CRSP MFDB. Then, I used MFLINKS from Xxxxxxx Research Data Services (WRDS) to map Morningstar Data to the Thomson mutual fund holdings database. I randomly selected funds from my mapping and verified that my map- ping is robust. Lastly, I then mapped each holding with CRSP and Compustat data to obtain returns, size, book-to-market ratios, and other firm specific variables. Given that the S.E.C. regulation is based on the cap size of a fund for U.S. domestic equity funds, ...
Sample Selection. The sampling frame for the household survey was the population of all MWP-K intervention villages as well as non-intervention villages that are anticipating potential coverage, with the exception of Xxxx River district, which was supported by the CRS. Cluster sampling technique was used. Approximately 7-15 surveys were conducted for each cluster (village). A total of 222 clusters and 2,146 surveys were collected. Respondents were both male and female heads of household above age 18, with preference given to female. Questionnaire Design The survey was also designed by Emory Center for Global Safe Water. Table 1 shows the topics, including demographic information - occupations of household heads, household member composition, number and age of dependents, a standard wealth asset index, education, and age; water access conditions – source water, distance to water source, water accessibility in wet and dry seasons, quantity of water use; water treatment; productive use of water – economic activities, waste water management; decision-making power dynamics of the household; typical sanitation habits of each household member; attitudes about sanitation and hygiene; exposure to latrines; level of desire and intention to construct a latrine; the nature of the household’s interaction with the MWP partners; household characteristics – land ownership, community leadership, and wealth estimation. At the end, enumerators conducted structured observations of WASH conditions at the house. Data Entry and Analysis Baseline survey data from 2010 were used for cross-sectional analysis using SAS 9.3 (North Carolina, USA). A list of nine indicators was selected as independent variables, while household latrine ownership was used as the dependent variable. Table 2 shows the definitions of these variables and their research implications. Logistic regression was constructed and odds ratios were used to correspond with interview results.
Sample Selection. The sampling frame for qualitative interviews was the population in 25 CARE intervention villages in Garissa District and in 30 CRS intervention villages in Xxxx River District. Respondents were both male and female heads of household above age 18. Because females were reluctant to share information, more males were selected due to their inclination to provide rich data. A total of eighteen interviews were conducted, equally divided between Garissa and Xxxx River Districts. Among the nine in-depth interviews within each district, they were further divided into three clusters of three: self-financed latrine adopters, organization-supported latrine adopters, and non-adopters.
