Concepts 1. What is Inferential statistics? Inferential statistics uses observations of past occurrences or available data i.e. descriptive statistics to make decisions about future possibilities and/or the nature of the entire body of data. Inferential statistics draws conclusions or makes interpretations‚ predictions and inferences about a population based upon an analysis of a sample. 2. Give 2 different techniques which are used in descriptive statistics to represent the data. Tables or
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distribution function for 100 normal random numbers. The K-S test is based on the maximum distance between these two curves. Characteristics and Limitations of the K-S TestAn attractive feature of this test is that the distribution of the K-S test statistic itself does not depend on the underlying cumulative distribution function being tested. Another advantage is that it is an exact test (the chi-square goodness-of-fit test depends on an adequate sample size for the approximations to be valid). Despite
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Chapter 1 The Probability in Everyday Life In This Chapter Recognizing the prevalence and impact of probability in your everyday life Taking different approaches to finding probabilities Steering clear of common probability misconceptions You’ve heard it‚ thought it‚ and said it before: “What are the odds of that happening?” Someone wins the lottery not once‚ but twice. You accidentally run into a friend you haven’t seen since high school during a vacation in Florida. A cop pulls you over the
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Week Four Discussion 2 1. In your own words‚ describe two main differences between classical and empirical probabilities. The differences between classical and empirical probabilities are that classical assumes that all outcomes are likely to occur‚ while empirical involves actually physically observing and collecting the information. 2. Gather coins you find around your home or in your pocket or purse. You will need an even number of coins (any denomination) between 16 and 30. You do not
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Probability Games Walter J Mahoney MTH 157 1/20/2013 Andrea Hayes Probability is a fascinating math concept. It can be applied in many aspects of our students’ daily lives. As the world of technology continues to grow‚ teaching of many math
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Normal(88.532‚5.07943) Normal(97.1033‚5.04488) Normal(107.15‚5.15367) Summary Statistics Mean 88.532 Std Dev 5.0794269 Std Err Mean 0.2271589 Upper 95% Mean 88.978306 Lower 95% Mean 88.085694 N 500 Sum 44266 Summary Statistics Mean 97.103333 Std Dev 5.0448811 Std Err Mean 0.2912663 Upper 95% Mean 97.676525 Lower 95% Mean 96.530142 N 300 Sum 29131 Summary Statistics Mean 107.15 Std Dev 5.1536687 Std Err Mean
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Area of Research The research seeks to investigate the entrepreneurial success in electronic commerce. Previous research has identified several drivers and impediments to success and the study will mitigate those drivers and impediments to assist an entrepreneur in achieving success as a reseller of tangible goods. Types of Measurement In my research‚ the variables are categorized into two groups: success drivers and impediments to success. The one thing that is common in these items being
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1. Introduction This report is about the case study of PAR‚ INC. From the following book: Statistics for Business an Economics‚ 8th edition by D.R. Anderson‚ D.J. Sweeney and Th.A. Williams‚ publisher: Dave Shaut. The case is described at page 416‚ chapter 10. 2. Problem statement Par‚ Inc. has produced a new type of golf ball. The company wants to know if this new type of golf ball is comparable to the old ones. Therefore they did a test‚ which consists out of 40 trials with the current
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MGT 601: Statistical Inference Lecture 03 Dr. MUMTAZ AHMED Objectives of Current Lecture In the current lecture: Introduction to Probability Definition and Basic concepts of probability Some basic questions related to probability Laws of probability Conditional probability Independent and Dependent Events Related Examples 2 Probability Probability (or likelihood) is a measure or estimation of how likely it is that something will happen or that a statement is true. For example
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Chapter 6 Continuous Probability Distributions Case Problem: Specialty Toys 1. Information provided by the forecaster At x = 30‚000‚ [pic] [pic] Normal distribution [pic] [pic] 2. @ 15‚000 [pic] P(stockout) = 1 - .1635 = .8365 @ 18‚000 [pic] P(stockout) = 1 - .3483 = .6517 @ 24‚000 [pic] P(stockout) = 1 - .7823 = .2177 @ 28‚000 [pic]
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