Chapter 3 Data Description 3-1 Measures of Central Tendency ( page 3-3) Measures found using data values from the entire population are called: parameter Measures found using data values from samples are called: statistic A parameter is a characteristic or measure obtained using data values from a specific population. A statistic is a characteristic or measure obtained using data values from a specific sample. The Measures of Central Tendency are: • The Mean • The
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Mohammed Al Bittar - 1006091 1. What is meant by big data? A term used for complex sets of data which becomes very difficult to process‚ manage‚ or capture by commonly-used software. 2. What is meaning of data-driven culture? A culture where decisions made upon analyzing real statistical information. Like how Wal-Mart checks on the weather in order to provide more products to the customers; because their statistical information shows that whenever there is a storm‚ customers by
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Plants and animals already existed before hand which led to the secondary succession. The soil is richer than the primary succession. The effects of a natural disaster common in the Great Plains to cause secondary succession is hailstorms and droughts. Hailstorms have big balls of frozen ice that can kill the plants‚ but leaves the seeds behind. Droughts are when everything is dried out
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Summary of data gathering There are more cars and trucks going through the intersection of Spring Street and Route 27 in the morning than the afternoon. How to determine cars and trucks Cars are usually used for taking passengers to the destination; in general‚ they are smaller than trucks. As for trucks‚ there are larger spaces to carry items‚ for example‚ gasoline‚ foods‚ and other goods. In addition‚ school buses are important transportations on campus‚ but they are counted as neither cars
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The data protection principles There are eight data protection principles that are central to the Act. The Company and all its employees must comply with these principles at all times in its information-handling practices. In brief‚ the principles say that personal data must be: 1. Processed fairly and lawfully and must not be processed unless certain conditions are met in relation to personal data and additional conditions are met in relation to sensitive personal data. The conditions are
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Data Anomalies Normalization is the process of splitting relations into well-structured relations that allow users to inset‚ delete‚ and update tuples without introducing database inconsistencies. Without normalization many problems can occur when trying to load an integrated conceptual model into the DBMS. These problems arise from relations that are generated directly from user views are called anomalies. There are three types of anomalies: update‚ deletion and insertion anomalies. An update anomaly
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Be Data Literate – Know What to Know by Peter F. Drucker Executives have become computer literate. The younger ones‚ especially‚ know more about the way the computer works than they know about the mechanics of the automobile or the telephone. But not many executives are information-literate. They know how to get data. But most still have to learn how to use data. Few executives yet know how to ask: What information do I need to do my job? When do I need it? In what
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into the Workers’ Compensation (WC) actuarial model workbook. Payroll data for the WC model should contain “only the actual hours worked” for specific Rate Schedule Codes (RSC) groups‚ including executives. The WC payroll data should exclude all paid leave types. A comparison of work hours from the NPHRS mainframe report to the summary in EDW reveals very small differences. We hope to align the NPHRS and EDW work hour data. Also‚ we (Technical Analysis‚ Accounting and Finance) need to understand
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Table of Contents 1. VARIABLES- QUALITATIVE AND QUANTITATIVE......................3 1.1 Qualitative Data (Categorical Variables or Attributes) ........................... 3 1.2 Quantitative Data............................................................................................... 4 DESCRIPTIVE STATISTICS.................................................6 2.1 Sample Data versus Population Data ................................................................... 6 2.2 Parameters and Statistics
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Americans leave long electronic trails of private information wherever they go. But too often‚ that data is compromised. When they shop—whether online or at brick and mortar stores—retailers gain access to their credit card numbers. Medical institutions maintain patient records‚ which are increasingly electronic. Corporations store copious customer lists and employee Social Security numbers. These types of data frequently get loose. Hackers gain entry to improperly protected networks‚ thieves steal employee
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