...Qualitative Data Analysis 1. Data reduction Data reduction is a term that applies to the business practice of accumulating, analyzing and ultimately transforming massive amounts of data into a series of summarized reports. The idea behind the data reduction process is to provide a complete though somewhat simplified format that can be utilized with relative ease in business settings. Several different approaches to the process may be used, with the selection of data reduction techniques and systems depending on the nature of the data and how those summary reports need to be structured in order to provide a full and comprehensive representation of that data. 2. Data Display Data display refers to computer output of data to a user, and assimilation of information from such outputs. Some kind of display output is needed for all information handling tasks. Data display is particularly critical in monitoring and control tasks. Data may be output on electronic displays, or hardcopy printouts, or other auxiliary displays and signaling devices including voice output, which may alert users to unusual conditions. 3. Reliability The ability of an apparatus, machine, or system to consistently perform its intended or required function or mission, on demand and without degradation or failure. 4. Validity Validity concerns the degree to which an account is accurate or truthful. In a broad sense, pertains to the relationship between an account and something external to it...
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...factors that affect the student results and to prove the most efficient causes for high marks amongst the 705 students in BSB123. The variables are gender, whether the student is studying a double or single degree, quiz, report and exam results. The scope of the report is the population of university students studying BSB123, which will be examined. 2.0 – Outliers (Refer to the appendix) An outlier is an extreme value that differs greatly from other values that does not follow the overall data pattern. Outliers are beneficial when analysing data, as it helps gather additional information, displays trends and patterns and helps define the scope of the population being investigated. In the provided data, there were no outliers found. Figure 13.0 displays a minimum Z-score of -1.8970567 and a maximum of 2.459087745, meaning no z-scores fell outside the range. This shows that the chosen population is more controlled, less susceptible to variation and that exceptional cases do not vary the data results. 3.0 – Distribution of Exam Results This section of the report will investigate the performance of the students in regards to the final examination. Figure 1.0 – Histogram based on the distribution of the Exam Results amongst the students Figure 2.0 – Histogram based on the distribution of the Exam Results amongst the students The shape of the graph is right-skewed with a...
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... Enerdecido, Tania Mae R. Ronario, Ma. Francesca S. Data Analysis Qualitative Data Analysis “Analysis is a breaking up, separating, or disassembling of research materials into pieces, parts, elements, or units. With facts broken down into manageable pieces, the researcher sorts and sifts them, searching for types, classes, sequences, processes, patterns or wholes. The aim of this process is to assemble or reconstruct the data in a meaningful or comprehensible fashion” (Jorgensen, 1989: 107). The researchers will analyze the data by means of using Powell and Renner’s analysis process. The analysis process is made up of getting to know the data, focusing the analysis, categorizing information, identifying patterns and connections within and between categories, and interpretation (Powell and Renner, 2003). This process will be used in analyzing all of the transcripts of parents of students from Mater Dei Academy. The identified consistencies and differences from responses of the interviewees (Powell and Renner, 2003) will be open-coded (Merriam, 2009). Identified consistencies and differences from the parents’ statements will be generalized. The researchers will then come up with patterns or themes that could be formed through the coded answers of parents (Merriam, 2009). Furthermore, these will all be interpreted. Specifically, the researchers followed the subsequent steps in doing their qualitative data analysis of parents’ of students from Mater Dei Academy observations...
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...DATA ANALYSIS TAKE-HOME EXAM Introduction This report provides an insight into the investment behavior of 50 couples. Using different statistical methods and observing the trend followed by the effect of various independent variables on a single independent variable , a conclusion will be reached. The following are the main tools used for analysis of investment behavior of 50 couples who are selected from a sample size of 194 couples. 1. Descriptive statistics 2.Histograms 3.Pivot tables 4.Multiple Regression In this model , a scrutiny of the above statistical data will give the tendency to invest in retirement plans and the type of couples who invest and take advantage of the attractive investments in order to avail tax exemption. This report also elaborates on how the different independent variables - Number of children, Salary, Mortgage and Debt- have an effect on the dependent variable, i.e. the percentage of salary invested. Consequently the below tasks will be fulfilled. Step 1-Extracting a sample of 50 couples Step 2-Constructing histograms and point estimates with given confidence intervals Step 3-Inference from pivot tables to explain the preferences of different couples on investments based on independent variables Step 4-Performing multivariate regression and conducting significance tests on beta coefficients R^2 and F-tests and hence establish a correlation of different variables and ensuing effect on investments made. Dataset We have a sample...
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...Exploratory Data Analysis Course Project Part A Subject: Applied Managerial Statistics Faculty: Curtis Allen Brown Submitted by: Christian Oji Introduction Exploratory Data Analysis (EDA): Exploratory data analysis is an approach to analyze statistical data using a variety of techniques out of which many are graphical analyses. EDA is used to dissect the data and look for the hidden patterns and correlations. Some of the graphical methods include pie charts, bar graphs, histograms, frequency and relative frequency tables, box plot, scatter graph, stem-leaf diagram etc. There are also quantitative measures of data which include central tendencies like mean and median, measure of dispersion like standard deviation, minimum and maximum...
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...evenly generated from both bubblers | 6 | Medium sized | Present | Evenly distributed in the bulk with a lot present near baffles | Present at top | Turbulent | 7 | Large | No vortex seen | | No Foam | Bubbles were evenly formed from both bubblers | 5.3 Analysis of Variance in parameters based on pooled data Variance in data was observed in each set of experiments due to a number of factors. These factors include stirrer speed, oxygen content, and the different sizes in air stones. From the ANOVA illustrated in Figure 1, the sources X1, X2, X3, X4 are oxygen flow rate, stone size, stirrer speed, and groups respectively. It is seen that the largest variance occurs at the stirrer speed parameter, with a F value of 24.71 as compared to oxygen flow rate at 1.82, stone size at 1.98 and groups at 0.15. This shows that the stirrer speed plays the most significant role in the variation between the sets of data. One of the reasons that might have caused this outcome was the formation of a vortex at high stirrer speeds. Although a high stirrer speed is needed for even mixing, the vortex formed creates a vacuum around an area this disrupts the mixing of the oxygen. Another factor that might have caused a variation in data is...
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...Data Collection Analysis: ITT Technical Institute Keller Graduate School of Management Training and Development HR592 Professor Robert Graver February 23, 2014 Table of Contents Overview of the Organization…..………………………………………………………………3 Executive Summary …………..………………………………………………………...…...…3,4 Analysis of Data Collected & Identified Training Need…………………………………..…4,5,6 Recommendations………………………………………………………………………………...7 Training or Intervention Strategy to Address the Needs…………………………………………8 Cost benefit analysis……………………………………………………………………………8,9 Conclusion……………………………………………………………………………………….10 References………………………………………………………………………………………..11 The ITT Technical Institutes is a leading private college system focused on technology-oriented programs of study. The seven schools of study at the ITT Technical Institutes [i.e., the School of Information Technology, the School of Drafting and Design, the School of Electronics Technology, the School of Business, the School of Criminal Justice, the School of Health Sciences and the Breckinridge School of Nursing] teach skills and knowledge that can be used to pursue employment opportunities in today’s world. There are over 140 ITT Technical Institutes in 38 states. ITT Technical Institutes predominantly offer career-focused, degree programs to over 70,000 students. The ITT Technical Institutes have been actively involved in the higher education community in the United States since 1969. Executive Summary ITT Technical Institute...
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...Data Table Analysis University of Phoenix Accounting Information Systems ACC/542 Anita Rodriguez April 2, 2012 Data Table Analysis Kudler Fine Foods was founded by Kathy Kudler in La Jolla in 1998. In the beginning, Kudler Fine Foods had only one store, causing Kathy Kudler to make the decision to use the Microsoft Access database to monitor and maintain sales, employees, inventory, customers, and orders. Because Kudler Fine Foods inventory was perishable, it was imperative to track properly these items. Possessing too much inventory was always a concern of Kudler Fine Foods, if the inventory information was inaccurate, outdated, or incomplete the information would be of no use to the company. This paper will present suggestions for enhancement, evaluate database tables from an accounting point of view, and generate a pivot table to assist the management of Kudler Fine Foods with preparing improved reports in accounting and enhance the decision-making process. Evaluate the design elements of the data tables from an accounting perspective The inventory tables allow the users to establish when to order additional inventory and what inventory is on hand. The use of data table analysis is of the essence for this process. The data table is intended to categorize the data into departments, financial codes, items, and it adds the total amount for the transaction codes. It also adds the amount of items in the inventory during a specified period. The data table design...
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...in the Study In the survey that Debbie Horner, the Human Resource manager, distributed to the company, there are different levels of data available for analysis. The question seeking information about the respondent’s gender is a nominal-level question. “The nominal level of measurement observations of a qualitative variable can only be classified and counted” (Lind, Marchal, & Wathen, p. 10, 2011). For analytical purposes the order in which the data is displayed makes no difference. The question regarding the division of work for the respondent, and the question regarding if the respondent is a member of management or supervision is also nominal. Also, the question pertaining to length is service is nominal because it only seeks one answer. The ten questions relating to how each respondent feels is interval-level data. The ten questions ask each respondent to rate his or her individual feelings on a scale of one to five. One is considered very negative and five is considered very positive. This is known as a Likert Scale. The Likert Scale is the most popular form of survey data collection because it is easy to assemble, the scale is more reliable, and produce more interval-level data (Cooper & Schindler, 2011). Data Coding After collecting the data, Sally, one the office support staff, began the task of coding the data. While some values were pre-determined other values needed coding. In addition to the questions there were also filter question asked...
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...spectrometric data analysis By XIAO Jiali, Jenny ( 0830300038) A Final Year Project thesis (STAT 4121; 3 Credits) submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Statistics at BNU-HKBU UNITED INTERNATIONAL COLLEGE December, 2011 DECLARATION I hereby declare that all the work done in this Project is of my independent effort. I also certify that I have never submitted the idea and product of this Project for academic or employment credits. XIAO Jiali, Jenny (0830300038) Date: ii Application of Bootstrap method in spectrometric data analysis XIAO Jiali, Jenny Science and Technology Division Abstract In this project the bootstrap methodology for spectrometric data is considered. The bootstrap can also compare two populations, without the normality condition and without the restriction to comparison of means. The most important new idea is that bootstrap resampling must mimic the separate samples design that produced the original data. Bootstrap in mean, bootstrap in median, and bootstrap in confidence interval are three kinds of effective way to handle mass spectrometric data. Then,we need to reduce dimension based on bootstrap method. It may allow the data to be more easily visualized. Afterwards, using results obtained by bootstrap, we use data mining method to predict a patient has ovarian cancer or not. Decision tree induction and neural network are usual way to classify it. Keywords: Bootstrap, data mining...
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...EDU 671 Week 4 DQ 1 Data Analysis Practice To Buy This material Click below link http://www.uoptutors.com/edu-671-ash/edu-671-week-4-dq-1-data-analysis-practice Mills (2014) shares in Chapter 6, “the interpretation of qualitative data is the researcher’s attempt to find meaning, to answer the ‘So what?’ in terms of the implications of the study’s findings” (p. 133). He adds that data analysis and interpretation is “. . . a process of digesting the contents of your qualitative data and finding related threads in it” (133). Analyze the middle school scenario, Flipped Math Class. Explain your process for coding and categorizing the qualitative data. What patterns and/or themes did you discover? Answer the “So What” for your team of teacher-researchers based on your findings. What steps does your team need to take to address these issues before implementing the innovation of a flipped classroom? Pages 138-139 in Action Research: A Guide for the Teacher Researcher provide an example of coding from a transcript. Guided Response: Consider the analysis and interpretation of at least two of your classmates. Did you find similar themes or patterns? Examine their interpretation of the data. Did they discover something you didn’t? Is there something you think is lacking in their interpretation? Provide specific feedback by asking a probing question and/or providing your interpretation of their analysis and next steps. *It is expected you follow-up by the last day of...
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...Trends in Information Analysis and Data Management Trends in Information Analysis and Data Management Over the last decade, advancements in digital technology have enabled companies to collect huge amounts of new information. This data is so large in scope, it has traditionally been difficult to process and analyze this information using standard database management systems such as SQL. The commoditization of computer technology has created a new paradigm in which data can be analyzed more efficiently and effectively than ever before. This report analyzes the some of the most important changes that are currently taking place within this new paradigm. The first part of this report covers trends in database analysis by analyzing the field of data mining. The report covers the topic of data mining by providing an explanation of it, and then by providing examples of real-world examples of data mining technology. Benefits and challenges of data mining are then provided. The second part of the report outlines an even more recent trend in data science, which is the increasing usage of noSQL databases to analyze “big data,” also referred to web-scale datasets. The most recent and major technological developments in the industry are then provided and described. Data Mining Background & Definition Data mining involves the process of discovering and extracting new knowledge from the analysis of large data sets. This is most often done through the use of data mining software, which...
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...these outcomes a success and the other, a failure. * The sample includes at least 10 successes and 10 failures. (Some texts say that 5 successes and 5 failures are enough.) * The population size is at least 10 times as big as the sample size. This approach consists of four steps: (1) state the hypotheses, (2) formulate an analysis plan, (3) analyze sample data, and (4) interpret results. State the Hypotheses Every hypothesis test requires the analyst to state a null hypothesis and an alternative hypothesis. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false; and vice versa. Formulate an Analysis Plan The analysis plan describes how to use sample data to accept or reject the null hypothesis. It should specify the following elements. * Significance level. Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used. * Test method. Use the one-sample z-test to determine whether the hypothesized population proportion differs significantly from the observed sample proportion. Analyze Sample Data Using sample data, find the test statistic and its associated P-Value. * Standard deviation. Compute the standard deviation (σ) of the sampling distribution. σ = sqrt[ P * ( 1 - P ) / n ] where P is the hypothesized value of population proportion in the null hypothesis, and n is the sample size. * Test statistic. The test statistic is...
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...Final Project: Statistics II Descriptive analysis of statistical data INTRODUCTION There have always been crimes, from a treachery to an assassination. Happens in every country you can think of, and every government has to deal with it. It is really stressful to try to understand the nature of the crimes: why are they done and where could they happen next. Out of this preoccupation is that we found studies gathering data from communities; we focused on one specific crime: murders. In several communities, it is thought that the murder rate is somehow related to several factors. For instance, it is common to hear that the murder rate depends on poverty and unemployment. Starting from this hypothesis, the database found to make this analysis relates the number of murders per year per 1,000,000 inhabitants with the number of inhabitants, the percentage of families’ incomes below $5000, and the percentage unemployed. OBJECTIVE OF THE STUDY Trying to estimate how many murders will happen in a year in a specific place is difficult, but not impossible. This is why we are using the dataset found with the variables mentioned above, with which we’ll be able to find a formula. So, after this project, if we want to know how many murders will be on a city, for example Monterrey, we’d just plug in the data from that city (the inhabitants, the percentage of families income below $5000, and the percentage unemployed) and we’ll get a number, which would be the predicted number of murders...
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...Methods of Collecting Job Analysis Data A variety of methods are used to collect information about jobs. None of them, however, is perfect. In actual practice, therefore, a combination of several methods is used for obtaining job analysis data. These are discussed below. Job performance In this method the job analyst actually performs the job in question. The analyst, thus, receives first hand experience of contextual factors on the job including physical hazards, social demands, emotional pressures and mental requirements. This method is useful for jobs that can be easily learned. It is not suitable for jobs that are hazardous (e.g., fire fighters) or for jobs that require extensive training (e.g., doctors, pharmacists). Personal observation The analyst observes the worker(s) doing the job. The tasks performed, the pace at which activities are done, the working conditions, etc., are observed during a complete work cycle. During observation, certain precautions should be taken The analyst must observe average workers during average conditions. The analyst should observe without getting directly involved in the job. The analyst must make note of the specific job needs and not the behaviors specific to particular workers. The analyst must make sure that he obtains a proper sample for generalization. This method allows for a deep understanding of job duties. It is appropriate for manual, short period job activities. On the negative side, the methods fail to take note of the mental...
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