...data to find whether CRM project is worth pursuing given the strength of firm’s project management capability along with market evaluation of CRM implementation. Market analysis shows that 47% of the company finds that inadaptability of the end-user with CRM applications put the project in jeopardy(Coltman and Devinney, 2007). Data is analyzed for implementation of CRM through different vendors for companies of all range from less than $750K to over $10M. It consists of implementation statistics over the past 10 years. To analyze research data, Bayes’ theorem is selected as the probability model that was close to implementation of CRM project. Statistics and Probability Tutorial(n.d.) states that Bayes’ theorem looks appropriate in the context as it provides logical inference to calculate the degree of confidence based on already gathered evidence. Statistical result of data reflects that the probability of project being failed by a project management methodology is 47%. Conditional probability calculation shows that if there is established project management methodology in the firm there was a 16% chance the project would fail. The CRM research analyst additionally stated that even though a project management is not adequate, failure is not always imminent. Failure also...
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...Unit 2 – Probability and Distributions Kimberly Reed American InterContinental University Abstract This week’s paper focuses on an email that will be written to AUI the email will contain information from the data set key and explain why this information is important to the company. Memo To: HR Department From: Senior Manager Date: 20 Sept, 2011 Subject: Data Set Dear Department Heads: The following memo will contain information that contains vital and confidential information. This information will need to be studied by all department heads. Overview of the data set This data set of information contains information on the breakdown of the survey that was conducted on the company Use of statistics and probability in the real world Companies use statistics in the real world to get and have an advantage. They can be used for things such as knowing the latest stats on a sports figure or what items a consumer will likely buy from the local hardware store Distributions Distribution table contains the information that gives the breakdown of how the study was conducted and who the participants were in the study. This information is important to AIU for the company will be able to better prepare for the future when they know how to better manage their work force Then complete the following distribution tables. Please pay attention to whether you should present the results in terms of percentages or simple counts. Gender |Gender |Percentage...
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...Chapter 6 Statistical Process Control 6.0 Introduction One of the axioms or truisms in law of nature is “No two items of any category at any instant in the universe are the same”. Manufacturing process is no exception to it. It means that variability is part of life and is an inherent property of any process. Measuring, monitoring and managing are rather engineers’ primary job in the global competition. A typical manufacturing scenario can be viewed as shown in the Figure 6.1. That is if one measures the quality characteristic of the output, he will come to know that no two measured characteristics assume same value. This way the variablility conforms one of the axioms or truisms of law of nature; no two items in the universe under any category at any instant will be exactly the same. In maunufacturing scenario, this variability is due to the factors (Random variables) acting upon the input during the process of adding value. Thus the process which is nothing but value adding activity is bound ot experience variability as it is inherent and integral part of the process. Quality had been defined in many ways. Quality is fitness for use is the most common way of looking at it. This fitness for use is governed by the variability. In a maufacturing scenario, despite the fact that a machine operator uses the same precision methods and machines and endeavours to produce identical parts, but the finished products will show a definite variablity. The variability of a product...
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...Teaching Statistics and Probability is great for promoting problem solving and critical thinking, enhancing communication, developing number sense, and applying computation. As it applies to every day situations and appeals to our sense of fairness, it is very close in nature to inquiry based learning. Children encounter ideas of statistics and probability outside of school every day. The data students see are often represented graphically, statistically, or probabilistically. Weather reports are just one example of probability data we hear on the news. Begin teaching probability by formulating questions. “How many children in this class prefer to eat apples?” Children are familiar with line plots, which they learned earlier, review and build on that knowledge. Next step in teaching probability is to teach to collect data: observations, survey and questionnaires, experiments, interviews, simulations, poles, examining records, and searching info sources. It is important to teach kids to use appropriate methods of collecting data. Next step is to analyze data, represent it graphically. Representing data is done in a concrete way first (laying objects on the graph), and moving towards pictorial representation (drawing a chart with pictures of items being compared), and then symbolic (line plot, pie chart). Help students understand graphic representations by asking questions about the chart. Different ways to represent graphically: line plots, stem and leaf plots, box plots, picture...
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...What is “Statistics”? Data collection Chapter 1 of text “a way to get information from data” A framework for dealing with variability A way to make decisions under uncertainty Statistical inference: the problem of determining the behaviour of a large population by studying a small sample from that population Why is statistics important in business? Financial management (capital budgeting) Marketing management (pricing) Marketing research (consumer behaviour) Operations management (inventory) Accounting (forecasting sales) Human resources management (performance appraisal) Information systems Economics (summarising, predicting) See http://www.youtube.com/watch?v=D4FQsYTbLoI What is a population? What is a sample? Population: a collection of the whole of something – e.g. all female students of ANU; all people who live in Tuggeranong; all people who play the flute. Sample: a set of individuals drawn from a population e.g. the female students in STAT1008 are a sample of all female students at ANU. If we have a population…. We can get parameters – true values for things like the centre and spread of the population We know the answers – what proportion are this tall? We look at the population and get the answer. If we have a sample… We can get statistics – these are values that estimate the parameters e.g. sample centre and sample spread used to estimate population centre and population spread We have to use inference to do this estimation – what proportion are this...
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...Management Science Total Cost: Total Revenue: Total Profit: Break-even Point: Where: Example: Probability and Statistics: P(A or B) = P(A) + P(B) – P(AB) Where P(A or B) = probability event A or B or both will occur, P(A) = probability event A will occur, P(B) = probability event B will occur, P(AB) = probability that both event A and B will occur. Example: P(Negative Test Result or Not Pregnant) = P(Negative Test Result) + P(Not Pregnant) – P(Negative Test Result and Not Pregnant) = 16/99 + 14/99 – 11/99 = 19/99 Independent Events: A succession of events that do not affect each other. If A and B are independent, P(AB) = P(A)•P(B) Example: Flip a coin twice, the probability of a head followed by a tail is: P(HT) = P(H)•P(T) = (.5)(.5) = .25 Binomial Distribution: Example: Assume n= 20, p=0.1 P(r ≥ 5) – rejected. P(r < 5) – accepted. P(r < 5) = P(r = 0) + P(r = 1) + P(r = 2) + P(r = 3) + P(r = 4) P(r < 5) = P(r < 5) = 0.1216 + 0.2702 + 0.2852 + 0.1901 + 0.0898 = 0.9569 (accepted) P(r ≥ 5) = 1 – P(r < 5) = 1 – 0.9569 = 0.0431 (rejected) Expected Value: E(x) = Example: x P(x) x•P(x) 0 .10 0 1 .20 .20 2 .30 .60 3 .25 .75 4 .15 .60 ∑x•P(x) = 2.15 Normal Distribution: Example: Note: x = 400 is to the left of the mean in following example. Note: x = 19 is to the right of the mean in the following example. Decision Analysis: Maximax: First select the...
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...Probability 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...
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...a decision on a daily basis. A decision to buy rental car insurance will be answered by using the concept of probability. This paper will focus on the application of various probabilities to formulate the decision under uncertainty. Discreet outcome from statistical analysis as well as trade-offs between accuracy and precision obtained by different probabilities concepts shall be evaluated. According to car accident statistic stats, auto, fatal, and drunk driving, the estimation of having an accident is of one in 16 cars. It has provided useful information to make important decision. There are a number of probability concepts that can be used in determining the results from the research data that was given. Probability is used to limit the uncertainty of the decision on whether to buy the rental car insurance. The probability concept that works the best and meets all of the criteria from the information that was gathered is the Bayes’ Theorem. The application of Bayes' theorem helps to interpret the data because it is most relevant to the itinerancy of the whole trip. The Bayes' theorems follow the method of logical inference by establishing the degree of assurance in every decision (Lind, Marchal & Wathen, 2008). Consequently, Bayes' theorem is best utilized for the purpose of predicting confidence levels for purchasing rental car insurance based on the probability of...
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...project in jeopardy (Coltman and Devinney, 2007). If the project is cancelled, it results in loss of million dollars. As a business analyst of the XYZ Global Pvt. Ltd, I have to provide statistical interpretation to the senior management whether CRM project is worth pursuing given the strength of firm’s project management capability along with market evaluation of CRM implementation. Risk is huge if project goes on wrong note (Stone, Woodcock, and Wilson, 1996). Research To reach to right conclusion; I researched market analysis regarding implementation of CRM through different vendors for companies of all range from less than $750K to over $10M. My researched data set was from the CRM LANDMARK and consisted of implementation statistics over the past 10 years. More importantly, my research specified the success and failure of implementation along with factors associated to the result of success. Since my assessment was to get the prediction of CRM project in the firm in next 2 year, my research primarily concentrated on this time frame. Once the research was completed, I focused on accurately interpreting market data to predict a precise decision....
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...Inferential Psychology: Hypothesis testing and the search for truth Asking questions of nature has been a part of science from the beginning. In psychology we generally move towards a model of natural science that makes use of inferential hypothesis testing as a central tenet or dogma. As indicated earlier, with respect to sampling from populations, we use a collection of probability distributions and inferential statistics to help us in making decisions about our hypotheses. Essentially statistics are a tool that we use in making decisions. Sometimes we use these tools thoughtfully, considering their strengths and weaknesses, sometimes we use them blindly allowing them to dominate our thoughts and remove our interpretations from the role they must play. Remember that there are numerous assumptions behind the use of statistics in guiding our decision-making, they do not make science and truth for us. Looking back to the Rebirth of Positivism: Psychology and The Golden Age of Behaviorism Edward Chace Tolman (1886-1959): Purposive behaviorism Influenced by neo-realism and gestalt psychology Purpose and hunger could be objectively observed Operational behaviorism examines the functional relationships between independent and dependent variables Intervening variables: Theoretical constructs representing hypothetical processes) which enable the prediction of dependent variables. Theoretically they are the link between stimulus and response, sometimes...
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...investigation of relationships between variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon another—the effect of a price increase upon demand, for example, or the effect of changes in the money supply upon the inflation rate. To explore such issues, the investigator assembles data on the underlying variables of interest and employs regression to estimate the quantitative effect of the causal variables upon the variable that they influence. The investigator also typically assesses the “statistical significance” of the estimated relationships, that is, the degree of confidence that the true relationship is close to the estimated relationship. Regression techniques have long been central to the field of economic statistics (“econometrics”). Increasingly, they have become important to lawyers and legal policy makers as well. Regression has been offered as evidence of liability under Title VII of the Civil Rights Act of , as evidence of racial bias in death penalty litigation, as evidence of damages in contract actions, as evidence of violations under the Voting Rights Act, and as evidence of damages in antitrust litigation, among other things. In this lecture, I will provide an overview of the most basic techniques of regression analysis—how they work, what they assume, Professor of Law, University of Chicago, The Law School. I thank Donna Cote for helpful research assistance. See, e.g, Bazemore v. Friday, U.S. , (). See, e.g....
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...systems engineering. Reliability is theoretically defined as the probability of success (Reliability=1-Probability of Failure), as the frequency of failures; or in terms of availability, as a probability derived from reliability and maintainability. Maintainability and maintenance are often defined as a part of "reliability engineering" in Reliability Programs. Reliability plays a key role in the cost-effectiveness of systems. Reliability engineering deals with the estimation and management of high levels of "lifetime" engineering uncertainty and risks of failure. Although stochastic parameters define and affect reliability, according to some expert authors on Reliability Engineering (e.g. P. O'Conner, J. Moubray[2] and A. Barnard,[3]), reliability is not (solely) achieved by mathematics and statistics. "Nearly all teaching and literature on the subject emphasize these aspects, and ignore the reality that the ranges of uncertainty involved largely invalidate quantitative methods for prediction and measurement." [4] Reliability engineering relates closely to safety engineering and to system safety, in that they use common methods for their analysis and may require input from each other. Reliability engineering focuses on costs of failure caused by system downtime, cost of spares, repair equipment, personnel, and cost of warranty claims. Safety engineering normally emphasizes not cost, but preserving life and nature, and therefore deals only with particular dangerous system-failure...
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...Abstract: Statistics represents that body of methods by which characteristics of a population are inferred through observations made in a representative sample from that population. Since scientists rarely observe entire populations, sampling and statistical inference are essential. This article first discusses some general principles for the planning of experiments and data visualization. Then, a strong emphasis is put on the choice of appropriate standard statistical models and methods of statistical inference. (1) Standard models (binomial, Poisson, normal) are described. Application of these models to confidence interval estimation and parametric hypothesis testing are also described, including two-sample situations when the purpose is to compare two (or more) populations with respect to their means or variances. (2) Non-parametric inference tests are also described in cases where the data sample distribution is not compatible with standard parametric distributions. (3) Resampling methods using many randomly computer-generated samples are finally introduced for estimating characteristics of a distribution and for statistical inference. The following section deals with methods for processing multivariate data. Methods for dealing with clinical trials are also briefly reviewed. Finally, a last section discusses statistical computer software and guides the reader through a collection of bibliographic references adapted to different levels of expertise and topics. Statistics can be...
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...(Prerequisite: MAT 104) COURSE DESCRIPTION This course examines the principles of probability and of descriptive and inferential statistics. Topics include probability concepts, measures of central tendency, normal distributions, and sampling techniques. The application of these principles to simple hypothesis testing methods and to confidence intervals is also covered. The application of these topics in solving problems encountered in personal and professional settings is also discussed. INSTRUCTIONAL MATERIALS Required Resources ALEKS Access Code (bundled with course text when purchased from the Strayer Bookstore) Bluman, A. G. (2013). Elementary statistics: a brief version (6th ed.). New York, NY: McGraw-Hill. Note: Course materials for this class must be purchased from the Strayer Bookstore at http://www.strayerbookstore.com Supplemental Resources Hand, D. J. (2008). Statistics: a very short introduction. Oxford, UK: Oxford University Press. Rumsey, D. (2011). Statistics for dummies (2nd ed.). Hoboken, NJ: Wiley Publishing. Standard Normal Distribution Table. (2012). Retrieved from http://www.mathsisfun.com/data/standard-normal-distribution-table.html Statistics Calculator Free App for your Smartphone, created by Christian Gollner. Retrieved from https://play.google.com/store/apps/details?id=com.cgollner&hl=en COURSE LEARNING OUTCOMES 1. Describe the differences between the various types of data. 2. Apply various descriptive graphical techniques...
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...Jim Albert and Ruud H. Koning (eds.) Statistical Thinking in Sports CRC PRESS Boca Raton Ann Arbor London Tokyo Contents 1 Introduction Jim Albert and Ruud H. Koning 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Patterns of world records in sports (2 articles) . . . . . . . 1.1.2 Competition, rankings and betting in soccer (3 articles) . . 1.1.3 An investigation into some popular baseball myths (3 articles) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 Uncertainty of attendance at sports events (2 articles) . . . 1.1.5 Home advantage, myths in tennis, drafting in hockey pools, American football . . . . . . . . . . . . . . . . . . . . . 1.2 Website . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modelling the development of world records in running Gerard H. Kuper and Elmer Sterken 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . 2.2 Modelling world records . . . . . . . . . . . . . . 2.2.1 Cross-sectional approach . . . . . . . . . . 2.2.2 Fitting the individual curves . . . . . . . . 2.3 Selection of the functional form . . . . . . . . . . 2.3.1 Candidate functions . . . . . . . . . . . . . 2.3.2 Theoretical selection of curves . . . . . . . 2.3.3 Fitting the models . . . . . . . . . . . . . . 2.3.4 The Gompertz curve in more detail...
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