...University of Phoenix Material Data Set Worksheet In 150 to 350 words, define and describe structured data sets. Use at least one outside source in your response. Structured data sets are information or data that is organized in a uniform manner so that a computer can identify it and process it. Structured data is commonly used for things like templates, drop down lists, medical vocabularies (LOINC or SNOMED CT), and boxes that can be checked (Futrell, 2013). Information that is organized in structured data sets can be easily located and enables the full capability of an EHR with things like trend analysis and decision support. A doctor’s office uses structured data sets by using templates that record a patient’s information like demographics, vitals, etc. Data that is coded and organized allows for interoperability (Futrell, 2013). Having information in structured data sets means that information can be shared between different systems and even different providers. Having all of the patient’s information in structured data sets is important when point of care is delivered. If a physician has all of a patient’s information, I believe that they can reach a more accurate diagnosis and plan of treatment. Using the following table, identify and list at least five benefits and five challenges of structured data sets. Explain each benefit or challenge in 50 to 100 words. |Benefits |Challenges ...
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...Littletown Café Data Analysis QNT/275 January 25, 2016 The Littletown Café has a different schedule it’s busy season and off season. Careful analysis of statistical data will aid in future decisions for the business. By using qualitative and qualitative data, information gathered can determine the number of staff that is needed for the busy season. Graphs and charts are used when presenting this type of data to show variations between seasons. As with any data, there can be several factors that can influence the validity of the data if it isn’t consistent, such as if there is an abundance of discrepancies in the data sets that could potentially compromise its validity. Some factors that would affect the validity of the set would be weather, temperature, and construction. The continuous changes in these factors could determine whether business will be good or bad. Reliability of the data set can convey the genuine, dependable, constant, and the consistency. Misplaced documents of employee’s record could also affect the reliability of the set. Another problem is no-shows and illnesses could offset the information trying to be analyzed. The steps taken for my conclusion about the validity and reliability was to check and make sure if the data was consistent and if the process was collected was valid for the study. This gave me a chance to check the data to make sure that everything was good enough to be included in the study.Central tendency and variability give us a more...
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...Paper 258-2013 Data Set Compression using COMPRESS= ABSTRACT DUE TO AN INCREASED AWARENESS ABOUT DATA MINING, TEXT MINING AND BIG DATA APPLICATIONS ACROSS ALL DOMAINS THE VALUE OF DATA HAS BEEN REALIZED AND IS RESULTING IN DATA SETS WITH A LARGE NUMBER OF VARIABLES AND INCREASED OBSERVATION SIZE. OFTEN IT TAKES A LOT OF TIME TO PROCESS THESE DATASETS WHICH CAN HAVE AN IMPACT ON DELIVERY TIMELINES. WHEN THERE IS LIMITED PERMANENT STORAGE SPACE, STORING SUCH LARGE DATASETS MAY CAUSE SERIOUS PROBLEMS. BEST WAY TO HANDLE SOME OF THESE CONSTRAINTS IS BY MAKING A LARGE DATASET SMALLER, BY REDUCING THE NUMBER OF OBSERVATIONS AND/OR VARIABLES OR BY REDUCING THE SIZE OF THE VARIABLES, WITHOUT LOSING VALUABLE INFORMATION. IN THIS PAPER WE WILL SEE HOW A SAS DATA SET CAN BE COMPRESSED USING THE COMPRESS= SYSTEM OPTION AND ALSO SOME TECHNIQUES TO MAKE THIS OPTION MORE EFFECTIVE. Introduction THE PROCESS OF REDUCING THE NUMBER OF BYTES REQUIRED TO REPRESENT EACH OBSERVATION IS KNOWN AS COMPRESSION. SOME OF THE ADVANTAGES OF COMPRESSING A DATASET INCLUDE BUT ARE NOT LIMITED TO, A COMPRESSED FILE REDUCES THE STORAGE REQUIREMENTS AND ALSO REDUCES THE NUMBER OF I/O OPERATIONS NECESSARY TO READ OR WRITE THE DATA DURING PROCESSING. IN A COMPRESSED FILE, THE DELETED OBSERVATION SPACE CAN BE REUSED USING REUSE= OPTION, WHEREAS IN AN UNCOMPRESSED DATA SET THE DELETED OBSERVATION SPACE IS NEVER REUSED. To create a compressed data set we use the COMPRESS= output data set option or system...
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...Abstract The head of the American Intellectual Union (AIU) is looking for research data about Gender, Probability factors and several other data sets that they need to use for reporting purposes. This data will help the AIU make sound and responsible decisions in regards to the data that they are looking to collect. Memo To: Director, American Intellectual Union From: John C. Carter Date: 8/2/2014 Subject: Distribution and Probability of data collected Dear Sir: As we discussed in earlier meetings, the AIU is looking for research data to help provide clear and concise numbers that you will be able to use in your business. These numbers that will be included in this document will allow the AIU to make sound and responsible business decisions about the future of your company. Overview of the Data Set The data that was used in this report came from information collected by AIU. These data sets include: Gender, Age, Department, Position, Tenure, Job Satisfaction, Intrinsic, Extrinsic and Benefits. With this data we will be looking at job satisfaction and how it relates to the different data sets that are included. These data sets include some Quantitive data sets like; Gender, Age and Tenure and Intrinsic. They also include a set of Qualitative categories like Department, Position, Extrinsic and Benefits. Use of Statistics and Probability in the Real World Statistics and Probability are used in a wide variety of ways in the modern workplace. Being...
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...are two types of statistics that are often referred to when making a statistical decision or working on a statistical problem. Descriptive statistics utilize numerical and graphical methods to look for patterns in data set, to summarize the information revealed in a data set and to present information in a convenient form that individuals can use to make decisions (Singpurwalla, 2013).Descriptive statistics main goal is to describe a data set, hence the class of descriptive statistics include both numerical measures and graphical displays of data. Inferential statistics utilizes sample data to make estimates, decisions, predictions, or other generalizations about a larger set of data (Singpurwalla, 2013).The main goal of inferential statistics is to make a conclusion about a population based off a sample of data from the population. A2. The population is the whole set of values, or individuals, that the information collector is interested in. The sample is a subset of the population, and is the set of values that are actually use in estimation. For example, if a person wants to know the average height of the residents of India, that will be the population, i.e., the population of India. As India population is quite large a number, and we wouldn't be able to get data for everyone there. So the collector draw a sample, to get some observations, or the height of some of the people in India (a subset of the population, the sample) and do the inference based on that. A3. A)...
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... QNT/351 ON14BSB02 7/14/15 Dr. JYOTIRMAY DEB Statistics in Business The Random House College Dictionary defines statistics as “the science that deals with the collection, classification, analysis, and interpretation of information or data.” Thus, a statistician isn’t just someone who calculates batting averages at baseball games or tabulates the results of a Gallup poll. Professional statisticians are trained in statistical science—that is, they are trained in collecting numerical information in the form of data, evaluating it, and drawing conclusions from it. Furthermore, statisticians determine what information is relevant in a given problem and whether the conclusions drawn from a study are to be trusted. Statistics is the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information. (Sincich, Benson,, & McClave, 2011, p. ). Many companies use statistics to make major business decisions, this can be financial, personnel, acquisitions, marketing and others. Another entity that benefits from statistics are government entities such as defense, financial (IRS), political and cultural policy departments. A good example of a company using statistics is a Bayer...
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...QNT/351 May 4, 2015 Sara Skowronski Statistics in Business According to dictionary definition it is “the science that deals with the collection, classification, analysis, and interpretation of numerical facts or data, and that, by use of mathematical theories of probability, imposes order and regularity on aggregates of more or less disparate elements (2015).” Identify different types and levels of statistics Types and levels of statistics include: Descriptive statistics: numerical and graphical methods that observe patterns in data set to give a summary of the information so it can be presented in an understandable format. Inferential statistics: is sample data that comes up with estimates, decisions, predictions, or other general particulars about a larger set of data. Experimental unit: is a fundamental element – an object (person, thing, transaction, or event). Another fundamental is Population: which is a set of units including people, objects, transactions, or events used in a study. A sample is the subject of the units of a population. Variable: describes the character or property of an individual experimental unit. It is known as univariate data which is a study the looks at only one variable, and bivariate data that deals with the study or relationship of two variables. Measurement: is an important factor of studying variables, as is qualitative and quantitative variables. Describe the role of statistics in business decision making ...
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...appropriate nearest neighbor search algorithm makes k-NN computationally tractable even for larger data sets, dealing with as high as 60000 data sets in our case. * KNN algorithm is analytically tractable. * KNN algorithm is highly adaptive to local information. We have used Euclidean distance metrics in our KNN algorithm to estimate the closest data points. Thus the algorithm is able to take full advantage of the local information in the training datasets to form a highly nonlinear, highly adaptive decision boundaries for each data point. * KNN algorithm is easily implemented in parallel. For each data set to be evaluated, it checks against the training data-set for the k nearest neighbors around the data set to be evaluated. Since each of these data set is independent of the others, the search and validation can be implemented in parallel. * Choosing higher value of K will yield smoother decision regions and provide probabilistic information. The disadvantages of KNN algorithm are: * KNN algorithm is computationally very intensive as it needs to search through training datasets (of size 60000) to find the nearest neighbors and determine the closest ones for each of the testing data sets which needs to be classified. * Since the datasets is huge (as we are dealing with 60000 training sets), storage will be a concern as we need to store all the data sets in training sample and use when we need to validate the test datasets. * As dimensionality...
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...variability and the results from each data set were placed in a chart. (Staff Writer, 2010) CHART 1 – Sample Data DATA | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 1 | 4.92 | 4.65 | 5.77 | 6.25 | 5.27 | 5.22 | 5.47 | 5.71 | 5.24 | 4.42 | 5.14 | 4.92 | 5.79 | 4.92 | 5.68 | 2 | 4.26 | 5.54 | 5.26 | 4.88 | 5.41 | 5.38 | 4.68 | 4.54 | 5.58 | 5.18 | 4.26 | 5.78 | 3.83 | 4.8 | 5.74 | 3 | 4.94 | 5 | 4.76 | 5.66 | 6.02 | 5.08 | 4.56 | 4.17 | 4.72 | 4.79 | 4.71 | 5.5 | 4.3 | 4.75 | 4.65 | 4 | 4.29 | 5.42 | 4.79 | 4.44 | 4.91 | 4.65 | 4.7 | 4.87 | 5.41 | 4.73 | 5.48 | 5.05 | 4.78 | 5.59 | 5.2 | DATA | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 1 | 5.43 | 4.79 | 4.43 | 6.35 | 5.03 | 6.32 | 4.3 | 6.07 | 5.11 | 4.5 | 4.91 | 4.65 | 4.7 | 5.87 | 4.41 | 2 | 4.81 | 6.04 | 5.08 | 5.95 | 4.66 | 6.09 | 5.47 | 4.97 | 4.9 | 5.24 | 4.79 | 4.71 | 5.5 | 5.3 | 4.75 | 3 | 5.27 | 4.47 | 3.69 | 6.29 | 5.25 | 5.57 | 4.27 | 5.51 | 5.91 | 4.86 | 5.74 | 4.81 | 6.04 | 5.78 | 4.95 | 4 | 4.96 | 5.18 | 6.43 | 5.89 | 4.46 | 5.91 | 4.34 | 5.02 | 4.66 | 4.35 | 5.03 | 5.32 | 4.3 | 5.07 | 5.11 | When the data was input into the chart, an X-Bar-Bar chart was made as well as an R-Bar chart. The initial results were an average of 5.1 and a range of 1.083. There were some data points that occurred outside of the control areas. The X-Bar chart shows the upper control limits and the lower control limits of the sample sets taken. The averages are seen...
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...Name Course Instructor Course Date of submission Status of Shark Stocks Protection of all species in the ecosystem, especially the imperiled species, for reasons fundamental among them socioeconomic and environmental is crucial. Debate centering on whether the shark species is imperiled or not, has effects on allocation of funds meant for conservancy efforts. However, the agreement from both sides is on sharks importance and their sustainability as crucial. This expose elucidate positions informing both sides. Further, the expose will identify the most strongly supported side and identify probable lobbies that support either side. Baum and Myers used data on the number of catches to compare the amount of shark species captured in the 1950s and 1990s in addressing their decline in the Gulf of Mexico. They noted that, catches on the Whitetip shark, the most prevalent of the sharks in the 1950s, declined by 99%. Additionally, catches of the Silky and Dusty shark declined by 91% and 79% respectively (142). Thus, this decline historically reflects on the level of humanity’s exploitation, for instance, the disappearance of the Whitetip in the Gulf of Mexico. Effectively, the near disappearance of the species underlines an ecological shift on the pelagic baseline. In another study in the Northwest Atlantic, Baum et al. concluded that there was a 50% decline of all the recorded but one species of shark. This study focused a short period of 15 years between 1986 and 2000 (391)...
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...Webquest Handout Task 1: 1. The research methods knowledge database is a database of generalized topics about performing social research. These vary from how to collect data, who to collect data from, where to collect the data, etc. a. What is the difference between qualitative data and quantitative data? How do you determine what type of data to collect? Can your topic be represented by solid numbers, or is it based on opinion? 2. Quantitative because the data given is concrete and generalizations like mean and mode can easily be identified. 3. Quantitative data is easily compiled into something meaningful because it is based on concrete data. On the other hand, qualitative data is presented in a raw form and needs to be categorized to be meaningful. Quantitative data is better at summarizing large amounts of data, like statistics, whereas qualitative data is better at telling the opinion of the participant, and is richer in details. 4. The three different types of ways to collect qualitative data are in-depth interviews, direct observation and written documents. Interviews can be conducted on an individual basis as well as a group interview and can be recorded in a multitude of ways. In an interview, the participant is being asked questions by the interviewer. This is where direct observation and interviews differ, because in the case of observation, the interviewer does not ask questions from the participant. Instead, the interviewer just stands...
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...Chris Sweeney Liu Liu Sean Arietta Jason Lawrence University of Virginia Images 1...k Cull ... ... images n-k....n Hipi Image Bundle Map 1 Map i Reduce 1 Shuffle ... Result Reduce j Figure 1: A typical MapReduce pipeline using our Hadoop Image Processing Interface with n images, i map nodes, and j reduce nodes Abstract 1 The amount of images being uploaded to the internet is rapidly increasing, with Facebook users uploading over 2.5 billion new photos every month [Facebook 2010], however, applications that make use of this data are severely lacking. Current computer vision applications use a small number of input images because of the difficulty is in acquiring computational resources and storage options for large amounts of data [Guo. . . 2005; White et al. 2010]. As such, development of vision applications that use a large set of images has been limited [Ghemawat and Gobioff. . . 2003]. The Hadoop Mapreduce platform provides a system for large and computationally intensive distributed processing (Dean, 2004), though use of Hadoops system is severely limited by the technical complexities of developing useful applications [Ghemawat and Gobioff. . . 2003; White et al. 2010]. To immediately address this, we propose an open-source Hadoop Image Processing Interface (HIPI) that aims to create an interface for computer vision with MapReduce technology. HIPI abstracts the highly technical details of Hadoop’s system...
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...SAS 认证考试样题 1.A raw data file is listed below. 1---+----10---+----20---+--son Frank 01/31/89 daughter June 12-25-87 brother Samuel 01/17/51 The following program is submitted using this file as input: data work.family; infile 'file-specification'; run; Which INPUT statement correctly reads the values for the variable Birthdate as SAS date values? a. input b. input c. input d. input relation relation relation relation $ $ $ $ first_name first_name first_name first_name $ $ $ $ birthdate birthdate birthdate birthdate date9.; mmddyy8.; : date9.; : mmddyy8.; Correct answer: d An informat is used to translate the calendar date to a SAS date value. The date values are in the form of two-digit values for month-day-year, so the MMDDYY8. informat must be used. When using an informat with list input, the colon-format modifier is required to correctly associate the informat with the variable name. You can learn about • • informats in Reading Date and Time Values the colon-format modifier in Reading Free-Format Data. 2.A raw data file is listed below. 1---+----10---+----20---+--Jose,47,210 Sue,,108 The following SAS program is submitted using the raw data file above as input: data employeestats; input name $ age weight; run; The following output is desired: name age weight Jose 47 210 Sue . 108 Which of the following INFILE statements completes the program and accesses the data correctly? a. infile 'file-specification' pad; b. infile 'file-specification'...
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...done and after downloading your data in its original format to your computer or server space, be able to open your do-file, hit a button, and have a log file generated showing the results for all questions. Homework assignments may be done in groups of up to 4 people, but each group member must turn in their own individual copy of their do-file and log-file. Please list the members of your group at the top of your do-file. Part 1 Using the two provided data sets “CEO_Salary.dta” and “CEO_Sex.dta” located on Moodle, complete this part of the task. 1. What is the 95th percentile of salary? 2. What are the 5 lowest salaries in the data set? 3. Create a new variable, the natural log of salary, and find its mean 4. Create and present a tab of a new variable (named industrytype) that indicates what industry the CEO is from. This variable will be of a string type because the values will be text and equal to either “ind”, “fin”, “cons”, or “util” depending on what industry the CEO is from. 5. What is the mean of salary of males? Of females? Part 2 In this part, you will download publicly available data from the American Consumer Survey. Go to IPUMS at https://www.ipums.org/ and choose IPUMS-USA. Click on IPUMS Registration under the Data section, and apply for access. It may take up to 24 hours to receive account verification. Once you are registered and logged into IPUMS, get back to the IPUMS-USA page. Choose the “Select Data” link which may be on the top banner...
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...Running head: Statistics in Business Statistics in Business Define Statistics Statistics is "the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information" (). Different Types and Levels of Statistics The types of statistics are descriptive and inferential. Descriptive statistics looks for patterns in data sets and it does so by using numerical and graphical methods. It is also used to summarize the information found in a data set and allows the information to be shown in a more convenient form that is easier to read and understand. Inferential statistics' goal is to make estimates, decisions, predictions or other generalizations about a larger set of data. It does so by using sample data. There are four levels to statistics. Those are nominal level, ordinal level, interval level and ratio level. Nominal level is information that cannot be arranged in any particular order but that is classified into categories. Ordinal level is similar to nominal however, the data can be arranged into some type of order however, the differences between the values cannot be determined or is seen as meaningless. Interval level is similar to the ordinal level however, there are intervals between each set of data and the measurement can be defined and is obvious and there is no natural zero point. Ratio level is the same as interval level with the only difference being that there is a natural zero. Role Statistics...
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