Median: 57 Range 4 Second set of numbers: Mean: 56.14286 Median: 54 Range 23 B. Find the standard deviation using the range rule of thumb for each of the data sets. Please show your work. (Please see Chapter 4, Section 4.3, page 173 of the text).Standard deviation = 61 – 57 = 4 4 / 4 = 1 1 is the standard deviation using the range rule of thumb. Standard deviation = 69 – 46 = 23 23 / 4 = 5.75 5.75 is the standard deviation using the range rule of thumb. C. Compare the two sets and describe what you
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Case Study#2 The XYZ Company Katharine Rally is the vice president of operations for the XYZ Company. She oversees operations at a plant that manufactures components for hydraulic systems. Katharine is concerned about the plant’s present production capability. She has reduced the decision situation to three alternatives. The first alternative, which is fully automation, would result in significant changes in present operations. The second alternative, which is semi-automation, involves fewer
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http://www.stat.tamu.edu/~west/ph/sampledist.html. Use a skewed distribution. Take 1000 samples of sizes 2, 10 and 100. Construct a mean for each sample and look at the distribution of the sample means (third row). Record the mean and SD in the population for the original random variable. Make a table and record the mean of the means, and the standard deviation of the mean for each sample size (2, 10 and 100). For a bell-shaped distribution we showed that the mean of the sample means is very similar
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calculated using the error from the same block * For t-test * Standard errors: * Error (b)n for interaction 9.78583 * 2 × Error (b)n for Factor M 2 × 9.78586 * 2 × Error (a)n for Factor PP 2 × 4.96756 * 2 critical-t values → t at 2 and t at 4 df i.e. 4.303 and 2.776 * Could ask: do ANOVA and t-test, or ANOVA and interpret results from F; Standard error for the difference (a or b); Conclusion: levels differ/do not differ
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Continuous Probability Distributions Business Statistics With Canadian Applications Hummelbrunner Rak Gray Third Edition Week6 Pages 261-263 chapter 8 Pages 288-314, 320-325 chapter 9 Arranged by: Neiloufar Aminneia Probability distribution A probability distribution is a list of all events of an experiment
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revision using tabular approach Read ASW Chapter 4 or LR Chapter 4 | 4 | Probability Distribution - Meaning of Probability Distribution, Type of Probability Distribution, Need (Application) for Probability DistributionRead ASW Chapter 5 or LR Chapter 5 | 5 | Discrete Probability Distribution - Binomial Distribution – Applications, Numerical Problems, Excel & SPSS functions, Poisson Distribution
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Sampling and Sampling Distributions Practice Exam - Solution Instructors: Dr. Samir Safi Mr. Ibrahim Abed SECTION I: MULTIPLE-CHOICE 1. Sampling distributions describe the distribution of a) parameters. b) statistics. c) both parameters and statistics. d) neither parameters nor statistics. 2. The Central Limit Theorem is important in statistics because a) for a large n, it says the population is approximately normal. b) for any population, it says the sampling distribution of the sample mean
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Variance Sampling Distribution from The Normal Distribution Sampling from A Finite Population ◦ Distribution of The Sample Mean ◦ Joint Distribution of X and S2 ◦ Approximate Distribution of The Sample Mean ◦ How Large A Sample Is Needed 2 1 10/24/2011 Recall definitions of: ◦ ◦ ◦ ◦ ◦ ◦ ◦ Population Sample Inferential statistics Sampling Random sampling Parameter Statistic 3 If X1, . . . , Xn are independent random variables having a common distribution F, g , then we say
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65 8 – 7 Fitting to a Normal Distribution A normal curve is used in a wide variety of situations to estimate probabilities. Before we examine exactly what a normal curve is, we will recap how it is related to what we’ve already learned. Say you were rolling a die for a binomial experiment. There is a random variable associated with the outcomes of the experiment that we can calculate the probabilities for using the equations from the last section. A probability distribution shows the probabilities
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* Both and are random variables * Both and have probability distribution called the sampling distribution of * Scetch sampling distribution is mean and standard deviation and shape. [-μ] or │- p│.= sampling error In order to solve a question you should know 3 things; mean of ; E() which is μ, the standard deviation of , called σ and of course the ‘shape’ is it a normal distribution? T distribution df(degrees of freedom)=n -1 A. Central Limit Theorem: in selecting
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