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Unformatted text preview: 1 STA2023 Exam 2 Review/Outline (Chapters 11 - 17) The following is a list of key concepts that could be included on the exam. Note that this is not a practice exam. Instead, it is a bulleted list of topics that you should be familiar with. Many of these topics will be included on the exam, but not necessarily all of them. Furthermore, other questions may be asked that are not included in the list. In addition to this review, look over the in-class handouts/activities, the online quizzes, and the assigned problems from the textbook (as needed). What to Bring: graphing calculator, pencils, 3x5 index card ( one side only ) I suggest using your index card primarily for the many formulas covered in class. Also, you may want to include information about using your calculator (binompdf & binomcdf functions, calculating the mean and standard deviation of a discrete random variable ch. 16), and whatever else you feel is pertinent. Chapters 11 & 12 - Understand that no statistical study can overcome biased data. As such, randomization is a tool commonly used to collect unbiased data. The key is to collect sample data that is representative of the underlying population so you can make valid conclusions about the population from the sample results. - Know how to use the table of random digits to select a sample from a population. If asked on the exam, a set of random digits will be given (pg. 299 #7,8 & pg. 327 #36e) - Understand that a sample statistic, if calculated from unbiased data, can be used as a good estimator of an unknown population parameter. For example, a sample mean ( _ y ) can be used to estimate a population mean ( ). - Understand the concept of sampling variability (pg. 310) - sample results vary from sample to sample - Given a scenario, be able to identify the population of interest and/or the subjects in the sample. - Given a scenario, be able to recognize what type of sampling method is being used: (pg. 309-313) Simple or Stratified or Cluster or Systematic or Multistage random sampling - Given a sampling scenario, be able to identify any potential sources of bias (pg. 318-321): convenience sampling, undercoverage, voluntary response bias, nonresponse bias, response bias...
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- Spring '08