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# Statistics STA261

MU

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This 0 page Class Notes was uploaded by Orpha Swift on Sunday November 1, 2015. The Class Notes belongs to STA261 at Miami University taught by Staff in Fall. Since its upload, it has received 12 views. For similar materials see /class/233370/sta261-miami-university in Statistics at Miami University.

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Date Created: 11/01/15

STA261 Exam Study Guide Be prepared to see the following material on the exam Keep the following in mind You should focus more on the usage ofthe ideas below and being able to writeconverse about them rather than focusing primarily on doing calculations although you will be responsible for that too The material in the book is relevant too Don t just study from your notes alone A successful exam depends on reading the book too Lecture notes alone do not cover everything you ll be expected to do Please remember that the exam will have the included aspect of a time limit so prepare adequately Minitab output and formulas will be provided when needed on the exam Chapter 1 Basics and overview all Statistics as the process of learning about how the world operates in the presence of variation and uncertainty by collecting and making sense of data Three facets of statistics producing data describing data with descriptive statistics inferential statistics Chapter 2 Description of samples and populations all section except 27 Samples and statistics populations and parameters Variables categorical continuous and measurement scales nominal ordinal continuous Frequency distributions relative frequency distributions Visual displays histograms dotplots stem and leaf displays boxplots modified boxplots when to use various displays ie how to choose a method of displaying a certain data set Distributional shape symmetry skew modes outliers Measures of center sample mean sample median resistance of the median Measures of variation dispersion range IQR SD CV resistance of IQR Empirical Rule for roughly symmetric unimodal distributions Goal in sampling an unbiased representation of a target population Chapter 3 Random Sampling and Probability sections 3134 What is a simple random sample definition How to achieve randomness in sample selection methods table of random numbers random number generators how bias may appear in sampling think of examples sometimes it can creep in in subtle ways Probability definition Chance operations and events Frequency interpretation of PE t Probability trees for events that are comprised of a chain of component subeven s STA261 Exam ll Study Guide Be prepared to see the following material on the exam Keep the following in mind N 9 You should focus as much on the usage of the ideas below and being able to writeconverse about them as you do focusing on doing calculations The material in the book is relevant too Don tjust study from your notes alone Lecture notes alone do not cover everything that will be expected of you A successful exam depends on reading and understanding the book Please remember that the exam will have the included aspect of a time limit so prepare adequately Minitab output and formulas will be provided Viinen needed on the exam Chapter 3 Density Curves and the Binomial Distribution sections 35 36 Density curves are a general way of describing probability for variables measured on a continuous scale The normal distributions are one type of density curve On a density curve probability of an event area under the density curve above the values included in the event Independent trials model knowthe necessary conditions see BlnS on page 107 in SW Know how to compute probabilities associated with binomial distributed variables Y number of successes in n trials of the independent trials model using the binomial distribution formula on SW page 107 Also know how to read and interpret similar output from Minitab s binomial calculator CALCgtPROBABLITY DISTRIBUTIONSgtBNOMAL Also know how to interpret probability answers to any problemdo not be satisfied solely with being able to calculate the number Chapter 4 Normal Distributions all sections except 45 Use of the normal probability table to be provided Be able to use the normal table to find probabilities areas from known cutoff values and known cutoff values from probabilities areas This requires you to have our notational house in order For example a common mistake is to think 2 is an area It is not 2 is a cutoff value that produces a particular area Do not mix these two things up Standardizing Y Mn 5 into 2 MO 1 through use of Z Y ulo Assessing normality using a normal probability plot be able to interpret such a plot from Minitab Chapter 5 Sampling Distributions sections 5154 Knowthe concept of the metaexperiment and how it conceptually is used to describe how a statistic might behave from sample to sample Knowwhy this is important to understand Apply the metaexperiment idea to both dichotomous populations a population inhabited solely by successes and failures and continuous populations inhabited by numeric quantitative values Apply these ideas using binomial and normal models to find probabilities associated with sample proportions phat and sample means Ybar KNOW AND BE ABLE TO APPLY THEOREM 51 Especially the Qentral Limitlheorem Know the differences between a population distribution a sample distribution and a sampling distribution see SW page 163 and be able to discuss them Chapter 6 Confidence Intervals sections 6163 Know standard error SE vs standard deviation SD and the practical reasons for using SE Be able to find a confidence interval with any reasonable confidence level notjust 95 for a population mean u This requires knowing how to use the Student s tdistribution and the ttable Be able to verbally interpret any Cl you find in the physical context of the particular problem Knowwhat confidence means and what it does not mean Know how changing the confidence level andor the sample size affects confidence interval results

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