Data Analysis The first question of the set of 15 questions was about the age limit of the respondents. We collected all data from the age group starting from 15years. Most of the respondents fall into the age limit of 16-25 years which is 54% of the total respondents. 18of the 50 respondents were 26-35 years of age which is 36%. [pic] [pic] Q1: your most preferable Schemes when you are Thinking about a savings account? This was the question that gives the critical information
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an era of big data‚ this data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives; however‚ there are a number of challenges that must be addressed to allow us to exploit the full potential of big data. This paper focuses on challenges faced by online retailers when making use of big data. With the provided examples of online retailers Amazon and eBay‚ this paper addressed the key challenges of big data analytics including data capture and
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Running head: DATA ANALYSIS USING DESCRIPTIVE STATISTICS Data Analysis Using Descriptive Statistics Marissa Navar University of Phoenix Research and Evaluatiion I RES341 Richard A. Stanley June 28‚ 2009 Data Analysis Using Descriptive Statistics Histogram The Histogram chart shows the measurement of frequency in home buyers. It shows what home buyers are willing to spend in today’s current economy. The histogram explains although the economy is in a bad state that some home
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Section:04 Spring2010‚ Email ID:0630059 I have asked 20 people who live in Mirpur about their choice of soft drink they used to buy. Their answer s(my data set) are given below. Dataset 01: mojo mojo mojo mojo rc coke rc rc sprite coke mojo rc rc lemu lemu sprite lemu mojo sprite 7up ice Qualitative data analysis: from this data set we get to know the name and the number of the brands they choose and 2o people’s frequency of choosing among these brands. class Frequency
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Data Analysis‚ Presentation & Interpretation Prof. Dr. Md. Nazrul Islam Ph.D 1 Data Analysis Plan The appropriate methods of data analysis are determined by your data types and variables of interest‚ the actual distribution of the variables‚ and the number of cases. 2 Data Management 3 Why prepare a plan for processing and analysis of data? All information has been collected in a standardized way Not collected unnecessary data which will never be analyzed A statistical analysis plan should
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Chapter 3 Summarizing Data 0.1 0.2 Introduction...........................................................................Error! Bookmark not defined. A Section Title .......................................................................Error! Bookmark not defined. Demonstration: ............................... Error! Bookmark not defined. Exercises ................................................................................... Error! Bookmark not defined. 0.3 0.4 Chapter Summary .
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STEPS INVOLVED IN PROCESSING OF DATA IN RESEARCH METHODOLOGY Introduction After the collection of the data has been done‚ it has to be then processed and then finally analyzed. The processing of the data involves editing‚ coding‚ classifying‚ tabulating and after all this analyzation of the data takes place. Data Processing The various aspects of the data processing can be studied as follows 1. Editing of data: – This aspect plays a very vital role in the detection of the errors and
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department store chain‚ which has many credit customers and wants to find out more information about these customers. A sample of 50 credit customers is selected with data collected on the following five variables. Location Rural Urban Suburban Income Size Year Credit Balance By analysis the data which is collected on above mentioned variable in StatCrunch (Minitab) we would identify the customer’s income level‚ credit balance and location that they live.
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DataBig Data and Future of Data-Driven Innovation A. A. C. Sandaruwan Faculty of Information Technology University of Moratuwa chanakasan@gmail.com The section 2 of this paper discuss about real world examples of big data application areas. The section 3 introduces the conceptual aspects of Big Data. The section 4 discuss about future and innovations through big data. Abstract: The promise of data-driven decision-making is now being recognized broadly‚ and there is growing enthusiasm
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Data Preprocessing 3 Today’s real-world databases are highly susceptible to noisy‚ missing‚ and inconsistent data due to their typically huge size (often several gigabytes or more) and their likely origin from multiple‚ heterogenous sources. Low-quality data will lead to low-quality mining results. “How can the data be preprocessed in order to help improve the quality of the data and‚ consequently‚ of the mining results? How can the data be preprocessed so as to improve the efficiency and ease
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