Does asymptotic mean that the normal curve gets closer and closer to the X-axis but never actually touches it? Yes‚ asymptotic means that the curve of a line will approach 0 (the x-axis)‚ but it will not touch 0 and instead will extend to infinity. In this class‚ this applies to the normal continuous distribution and is one of the 4 key characteristics of a normal continuous distribution that our text book discusses. This means that the curve of the line will extend infinitely in both the negative
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Objectives Paragraph Uncertainty in the business environment is a major threat at each and every level of the supply chain. Every day new challenges and opportunities arise – rising cost of fue‚ implications of an organization’s carbon footprint‚ outsourcing regulations‚ tax incentives‚ and political fluctuation. Proactively monitoring the implications of such events at frequent intervals is crucial for an organization. By using a variety of Supply Chain modeling and mathematical tools‚ an organization
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ECONOMICS 140 Professor Glenn Woroch 2/3/09 Lecture 5 ASUC Lecture Notes Online is the only authorized note-taking service at UC Berkeley. Do not share‚ copy or illegally distribute (electronically or otherwise) these notes. Our student-run program depends on your individual subscription for its continued existence. These notes are copyrighted by the University of California and are for your personal use only. ANNOUNCEMENTS First problem set is due this Thursday Feb. 5th in lecture
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Chong Chun Wie Ext: 2768 ChongChunWie@imu.edu.my Content • Sampling distribution of sample means (SDSM) • Normality Test • Estimating a population mean: σ known • Estimating a population mean: σ unknown • Standard deviation of proportion • Confidence interval of proportion • Hypothesis testing with proportion Population and Sample Samples Populations Sampling distribution of sample means (SDSM) Sampling distribution • Example – Select randomly from the following number (1‚ 1‚ 2‚ 5‚ 5‚ 6
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Starts Here / Chapter 2: Data Stats: Modeling the World - Bock‚ Velleman‚ & DeVeaux Chapter 3: Displaying and Describing Categorical Data Key Vocabulary: frequency table relative frequency table distribution bar chart pie chart contingency table marginal distribution conditional distribution independent segmented bar chart Simpson’s Paradox 1. According to the authors‚ what are the three rules of data analysis? 2. Explain the difference between a frequency table and a relative frequency
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between probability distributions and frequency distributions? Provide an example that demonstrates the difference between the two. A probability distribution directly corresponds to a frequency distribution‚ except that it is based on theory (probability theory)‚ rather than on what is observed in the real world (empirical data). A frequency distribution is based on actual observations. An example would be observing a coin be flipped twenty times. A probability distribution is theoretical or ideal;
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PYC3704-- 2011 PYC3704 (2011) Psychological Research Study Notes IMPORTANT: Read through your UNISA study guide first! Get an overview of the module and then study each Topic individually Use this guide in conjunction with the UNISA guide- it is NOT a substitute If you have previous question papers- PLEASE DON‟T RELY ON THESE‚ you need to UNDERSTAND the content of this module if you want to pass and carry on to complete your Honours! 1 PYC3704-- 2011 Table of Contents
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statistical model and collect data • Compare and observe against expected outcomes and test model; • Refine model if necessary. A sample space A list of all possible outcomes of an experiment Event Sub-set of possible outcomes of an experiment. Normal Distribution Bell shaped curve symmetrical about mean; mean = mode = median 95% of data lies within 2 standard deviations of mean 2 conditions for skewness Positive skew if ( Q3 − Q2 ) − ( Q2 − Q1 ) > 0 and if Mean − Median > 0 . Negative skew if ( Q3 −
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Parameter b. Quantitative estimates ‚ sample mean ‚ population i. Discrete mean s‚ sample standard estimates ‚ population ii. Continuous deviation standard deviation Random Variable estimates P‚ population ˆ p ‚ sample Sampling Distributions proportion proportion Parameter (Defines a population) Statistic (calculated from sample to estimate a parameter) Central Limit Theorem Law of Large Numbers Confidence Level (1- )*100 Type I error (rejecting the null hypothesis when in
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------o0o------ CHAPTER 14: CHI-SQUARE TESTING STATISTICS FOR BUSINESS TAs: Vo Vuong Van Anh‚ Le Phuoc Thien Thanh‚ and Le Nhat Ho December 21‚ 2013 TABLE OF CONTENTS • PART I: CHI-SQUARE TESTING FOR GOODNESS-OF-FIT. • PART II: CHI-SQUARE TESTING FOR NORMAL DISTRIBUTION. • PART III: CHI-SQUARE TESTING FOR INDEPENDENCE. December 2013 Powered by Vo Vuong Van Anh 2 1 12/21/2013 Hypothesis Testing Procedure for Chi-Square Testing 5 Steps to Perform an Chi-Square Testing STEP 01 State the null and alternative
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