...Statistics-the language of data Statistic is to data as French is to language of the French. A solid understanding of statistics provides you with tools to react intelligently to information that you read or hear. Finally, an important ingredient of a well-executed statistical analysis is to clearly communicate numerical information into written language. Descriptive statistics refers to the summary of important aspects of a data set. This includes collecting data, organizing the data, and then presenting the data in the forms of charts and tables. In addition, we often calculate numerical measures that summarize, for instance, the data’s typical value and the data’s variability. Descriptive statistics is summary/describing data. Inferential statistics is drawing conclusions/assumptions. The phenomenal growth in statistics is mainly in the field called inferential statistics. Generally, inferential statistics refers to drawing conclusions about a large set of data— called a population —based on a smaller set of sample data. A population is defined as all members of a specified group (not necessarily people), whereas a sample is a subset of that particular population. In most statistical applications we must rely on sample data in order to make inferences about various characteristics of the population. For example, a 2010 survey of 1,208 registered voters by a USA TODAY/Gallup Poll found that President Obama’s job performance was viewed favorably by only 41%...
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...Statistics in Business Statistics in Business Statistics is considered art and science of collecting, analyzing, presenting, and interpreting data (McClave, Benson, & Sincich, 2011). Four scales of measurement are available for obtaining data on a exacting variable: nominal, ordinal, interval, and ratio (Lind, Marchal, & Wathen, 2011). The scale of measurement for a variable is nominal when the data are labels or names used to identify an attribute of an element (McClave, Benson, & Sincich, 2011). The scale is ordinal if the data have the properties of nominal data the order or rank of the data is meaningful. The scale is interval if the data have the properties of ordinal data the interval between observations is expressed in terms of fixed unit of measure (Lind, Marchal, & Wathen, 2011). Finally, the scale of measurement is ratio if the data have all the properties of interval data and the ratio of two values is meaningful. For propose of statistical analysis, data can be classified as qualitative or quantitative (Lind, Marchal, & Wathen, 2011). Qualitative data are labels or names used to identify an attribute of each element. Qualitative use either the nominal or ordinal scale of measurement and may be non-numeric or numeric. Quantitative data are numeric values that indicate how much or how many. Quantitative data use either the interval or ratios scales of measurement (Lind, Marchal, & Wathen, 2011). Ordinary arithmetic operations are meaningful...
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...and statistics are two related but separate academic disciplines. Statistical analysis often uses probability distributions, and the two topics are often studied together. However, probability theory contains much that is of mostly of mathematical interest and not directly relevant to statistics. Moreover, many topics in statistics are independent of probability theory. Probability (or likelihood) is a measure or estimation of how likely it is that something will happen or that a statement is true. Probabilities are given a value between 0 (0% chance or will not happen) and 1 (100% chance or will happen). The higher the degree of probability, the more likely the event is to happen, or, in a longer series of samples, the greater the number of times such event is expected to happen. These concepts have been given an axiomatic mathematical derivation in probability theory (see probability axioms), which is used widely in such areas of study as mathematics, statistics, finance, gambling, science, artificial intelligence/machine learning and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems. Statistics is the study of the collection, organization, analysis, interpretation and presentation of data. It deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments. The word statistics, when...
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...Statistics Business Jason Garrett Qnt/351 YOHANNES MARIAM 5/4/2015 After review of the text and online research I have come to the understanding of Statistics in business it’s the most widely used quantitative method in business. This method has a lot to do with the accuracy the business collects of the information to review and help make the best decision based on these findings. It is concerned with extracting the best possible information from data in order to aid decision making. As Define statistics a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data Identify different types and levels of statistics. Nominal Measurement There are handful of types and levels of statistics, Nominal measurements, “In social research, variables measured at a nominal level include gender, race, religious affiliation, college major, and birthplace. Ordinal measurement, in this classification, the numbers assigned to objects represent the rank order (1st, 2nd, 3rd etc.) of the entities measured. The numbers are called ordinals. The variables are called ordinal variables or rank variables. Comparisons of greater and less can be made, in addition to equality and inequality” ("Level Of Measurements," 2015). Interval measurements, “the numbers assigned to objects have all the features of ordinal measurements, and in addition equal differences between measurements represent equivalent intervals. That is, differences between...
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...California, Berkeley, Berkeley, CA 94720, USA 4 Center for Neuropharmacology & Neuroscience, Albany Medical College, Albany, NY 12208, USA ABSTRACT An algorithmic information theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG), whose connected subgraphs are compared using an adaptive information distance measure, aided by a closed-form multi-dimensional quantization. The summary is the clustering result and feature subset that maximize the gap statistic. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. When applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain. When analyzing intra-cellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification. Finally, it was able to differentiate wild type from genetically modified thymocyte cells. Index Terms: Algorithmic information theory, Algorithmic statistics, Information distance, Gap statistic, Clustering. Various portions of this research were supported by the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821), and...
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...Write a brief paper on Statistics and Operations Research in the context of Analytics. Analytics is defined as the scientific process of transforming data into insight for making better decisions. It is the combination of skills, technologies, applications and processes used by data scientist to gain insight in to their business based on data and statistics to drive business planning. It typically use’s data, statistical and quantitative analysis to measure the performance of the subject (Organization/ website etc.) on which a study is to be conducted. Analytics can be used in various fields such as market research, for studying user web pattern behavior & in many other applications to derive some meaningful information out of the complex world around us. But we will first touch upon the area of operations research & how Analytics with the help of various statistical tools can help to solve the operation related problem in an organization. Operations research overlaps with other disciplines, such as industrial engineering and operations management. It is often concerned with determining an optimal solution out of a business problem. It may either to maximize your profit, performance, or yield or minimize your losses, risk, or cost. In the fields of production, logistics, or sales where managers are facing a problems so as how to allocate resources, develop production schedules, manage the supply chain, and set prices. For example, it many help to decide how to organize...
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...Descriptive Statistics Pain Study “The kappa opioid nalbuphine produces gender-and dose-dependent analgesia and antianalgesia in patients with postoperative pain” was a study that was performed to observe gender-specific patient response to varied doses of nalbuphine, an opioid pain medication, following oral surgery (Gear, Miaskowski, Gordon, Paul, Heller, & Levine, 1999). In this study, the researchers asked participants to rate their pain on a 10 cm visual analog scale (VAS) just before drug administration to obtain a baseline measurement, and again at 20 minute intervals thereafter (Gear et al., 1999). The demographic characteristics and descriptive statistics of the 131 participants are provided in Table 1 of the study (Gear et al., 1999). To aid in interpretation of the data collected in the research experiment, the researchers provide the reader with information using both ratio and ordinal data measurements. The weight of the participants is given as a mean, or average, and is considered ratio measurement. This is important data because weight is a variable that is considered when calculating dosage requirements. For each dose of pain medication given, as well as the placebo, the weight in kilograms for both men and women is averaged in the table. The data appropriate and meaningful since the average weight of participants in each dose category is similar save for the weight differences between men and women. Ratio measurement is considered the highest...
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...population? A sample is a subset of a population. 3. What is the difference between a parameter and a statistic? A parameter is a numerical description of a population; a statistic is a numerical description of a sample. True or False? In Exercises 5–10, determine whether the statement is true or false. If it is false, rewrite it as a true statement. 5. A statistic is a measure that describes a population characteristic. False; a statistic is a measure that describes a sample characteristic 7. It is impossible for the Census Bureau to obtain all the census data about the population of the United States. True 9. A population is the collection of some outcomes, responses, measurements, or counts that are of interest. False, t is a collection of ALL outcomes, etc Classifying a Data Set In Exercises 11–16, determine whether the data set is a population or a sample. Explain your reasoning. 11. The age of each member of the House of Representatives Population; collection of ages of all members of the House 13. A survey of 500 spectators from a stadium with 42,000 spectators Sample; collection of 500 spectators is a subset within population of 42,000 spectators 15. The cholesterol levels of 20 patients in a hospital with 100 patients Sample; collection of 20 patients is a subset within the population of 100 patients Graphical Analysis In Exercises 17–20, use the Venn diagram to identify the population and the sample. 17. Population: Party of registered...
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...Mat 205-01 Statistics: Course summary December 3, 2014 Emmanuel Johnson Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data. Descriptive statistics are very important because if we simply presented our raw data it would be hard to visulize what the data was showing, especially if there was a lot of it. Descriptive statistics therefore enables us to present the data in a more meaningful way, which allows simpler interpretation of the data. For example, if we had the results of 100 pieces of students' coursework, we may be interested in the overall performance of those students. We would also be interested in the distribution or spread of the marks. Descriptive statistics allow us to do this. How to properly describe data through statistics and graphs is an important topic and discussed in other Laerd Statistics guides. In simple linear regression, we predict scores on one variable from the scores on a second variable. The variable we are predicting is called the criterion variableand is referred to as Y. The variable we are basing our predictions on is called thepredictor variable and is referred to as X. When there...
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...Statistics in Business Tania Correia QNT 295 April 6, 2015 Jeff Jung Statistics in Business Statistics can be defined as the way we look at, understand, and interpret “data” using math. By collecting the data, or information, we can analyze certain portions of this information to allow us to come to a conclusion or guesstimate of what may happen next or even to get an answer to a specific question. It all depends on the purpose of gathering the data. There are four different levels that we can measure statistics. The first would be the nominal level of measurement. It is the lowest of the four ways to characterize data. Nominal data deals with names, categories, or labels, leaving it to be a qualitative type of data. The second level would be, ordinal level of measurement. Data at this level can be ordered, but no differences between the data can be taken that are meaningful. As with the nominal level, data at the ordinal level should not be used in calculations. The third level would be the interval level. This level of measurement deals with data that can be ordered, and in which differences between the data does make sense. Data at this level does not have a starting point. The fourth and highest level of measurement is the ratio level. Data at the ratio level possess all of the features of the interval level, in addition to a zero value. At the ratio level of measurement, not only can sums and differences be calculated, but also ratios. One measurement can be divided...
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...------------------------------------------------- Ch 1 Introduction 1.1 Why Learn Statistics? * Statistics is the branch of mathematics that transforms numbers into useful information for decision makers. Statistics lets you know about the risks associated with making a business decision and allows you to understand and reduce the variation in the decision-making process. * Statistics provides you with methods for making better sense of the numbers used every day to describe or analyze the world we live in. * Statistical methods help you understand the information contained in “the numbers” and determine whether differences in “the numbers are meaningful or just due to chance. * Why learn statistics? First and foremost, statistics helps you make better sense of the world. Second, statistics helps you make better business decisions. 1.2 Statistics in Business * In the business world, statistics has these important specific uses: 1. To summarize business data 2. To draw conclusions from those data 3. To make reliable forecasts about business activities 4. To improve business processes * The statistical methods you use for these tasks come from one of the two branches of statistics: descriptive statistics and inferential statistics. * Descriptive statistics are the methods that help collect, summarize, present, and analyze a set of data. * Inferential statistics are the methods that use the data collected from a small group to draw...
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...Statistics in Business Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data. Some experts prefer to call statistics data science, a trilogy of tasks involving data modeling, analysis, and decision making. In contrast, a statistic is a single measure, reported as a number, used to summarize a sample data set. Knowing statistics will make you a better consumer of other people’s data. You should know enough to handle everyday data problems, to feel confident that others cannot deceive you with spurious arguments, and to know when you’ve reached the limits of your expertise. Statistical knowledge gives your company a competitive advantage against organizations that cannot understand their internal or external market data. And mastery of basic statistics gives you, the individual manager, a competitive advantage as you work your way through the promotion process, or when you move to a new employer. Nominal Level of Measurement The nominal level of measurement is the lowest of the four ways to characterize data. Nominal means "in name only" and that should help to remember what this level is all about. Nominal data deals with names, categories, or labels. Data at the nominal level is qualitative. Colors of eyes, yes or no responses to a survey, and favorite breakfast cereal all deal with the nominal level of measurement. Even some things with numbers associated with them, such as a number on the back of a football jersey, are nominal since...
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...Statistics in Business Meg Armen QNT/275 01/14/2016 James Malachowski Statistics in Business Statistics plays a big role in business. Businesses use statistics to improve their different business related problems. Statistics measures probability, hence without profit a business is almost pointless. Because statistics is important in business, we first understand the two branches of statistics, the four levels of data measurement, the role of statistics in decision making, and last, the implementation of statistics is problem solving. Statistics According to Jaggia and Kelly, the definition of statistics is a method of the collection, organization, and interpretation of data. In a broad sense, statistics is the communication of numerical information into written form. The authors discuss the breakdown of statistics; first, finding the right data, second, using the right tools to display the data, and finally, to communicate the numerical data into a written language (2014). Qualitative data Qualitative data such as, labels or names are used to identifying different characteristics of variables on a set of people, objects, or events (Jaggia & Kelly, 2014). Qualitative derives from the word quality. As an example, if we examine a latte, the qualitative description/data would be; the robust aroma, the frothy appearance, the strong taste and the beige ceramic cup it is poured in. Quantitative data Quantitative variables are described with numerical values (Jaggia...
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...1.1 Intro to the Practice of Statistics Statistics is the science of collecting, organizing, summarizing, and analyzing information to draw conclusions or answer questions. In addition, statistics is about providing a measure of confidence in any conclusions. Process of statistics p.6 1. Identify the research objective 2. Collect the data needed to answer the question #1 3. Describe the data 4. Perform inference Parameter & Statistic Population - the entire group of individuals to be studied Parameter (p) - A numerical summary of a population Sample – a subset (part) of the population to be studied Statistic (s) – a numerical summary of a sample Descriptive Statistics – data through numerical summaries, tables, and graphs Inferential Statistics – take the results from the sample, extend it to the population; measure the reliability of the result Qualitative & Quantitative Variables Qualitative Variable – attribute or characteristic Ex: religious affiliation, gender, number on a jersey Quantitative Variable – numerical Discrete & continuous Variables (Two types of Quantitative Variables) Discrete Variable – “countable” Ex: points scored at a game, number of children at a playground Continuous Variable – “measured” (has units of measurement) Ex: temperature, weight Level of Measurement of a Variable Nominal “to name” – data that consists of names. Labels, or categories only; no arrangement of order (Qualitative) ...
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...Chapter 1: Introduction – Defining the Role of Statistics in Business • Statistical Analysis: helps extract information from data and provides an indication of the quality of that information • Data mining: combines statistical methods with computer science & optimization in order to help businesses make the best use of the information contained in large data sets • Probability: helps you understand risky and random events and provides a way of evaluating the likelihood of various potential outcomes 1.1 - Why should you learn statistics? o Advertising. Effective? Which Commercial? Which markets? o Quality control. Defect rate? Cost? Are improvements working? o Finance. Risk – how high? How to control? At what cost. o Accounting. Audit to check financial statements. Is error material? o Other – economic forecasting, measuring and controlling productivity 1.2 – What is statistics? • Statistics: the art and science of collecting and understanding data o A complete and careful statistical analysis will summarize the general facts that apply to everyone and will also alert you to any exceptions. 1.3 – The Five Basic Activities of Statistics 1. Design Phase: will resolve these issues so that useful data will result a. Designing the Study involves planning the details of data gathering. Can avoid the costs & disappointment of find out – too late – that the data collected are not adequate to answer the important questions...
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