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UT Knoxville STAT 201 - Exam 2 Topics

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1 Stat 201 ‐ Exam 2 ‐ Topics List – Spring 2014 Chapter 8: Linear Regression - Four conditions for valid regression o Quantitative variables o Straight‐enough condition o Outlier condition o “Does the plot thicken?” condition - What is special about the regression (“least squares”) line compared to any other line drawn through the data - Given JMP output, write out the regression model with actual variable names - Interpret regression coefficients (b0 and b1) - Know when b0 has no logical interpretation - Know the difference between y and y‐hat - Be able to use a regression equation to make an estimate of y for a given value of x - Be able to calculate and interpret a residual - Be able to interpret a residuals plot and spot problems - Be able to find r from r2 or vice versa o Taking care of the direction of the slope - Interpretation of R‐square as the percentage of the variation in y associated with the variation in x - Don’t interpret the relationship to imply cause and effect Chapter 9: Regression Wisdom - Understand extrapolation o Dangers o Valid only when one can safely assume that the model will hold outside the range of the data into the region where the extrapolation is desired - Groups in residual plots - Correlation is not causation - Lurking variable and how they explain a correlation that is not due to causation - Identify outliers from a scatter plot Decision Trees (from class notes) - Response and explanatory variables o One y‐variable o Multiple x‐variables - Partitioning the data o R2 increases with each partition o When to stop partitioning  Illogical partitions  Small increases in R2 - Interpreting the tree o Interpretation of R2 o Leaf report 2 Chapter 11: Understanding Randomness - Understand what “random” means - Simulations and associated terminology Chapter 12: Sample surveys - Populations and samples - Parameters and statistics - Representative samples - Sampling frame - Nonrandom (Bad) samples o Voluntary samples o Convenience samples - Randomized samples o Simple random samples o Stratified random samples o Cluster random samples o Systematic samples o Multistage samples - Sampling biases o Nonresponse o Undercoverage o Response bias Chapter 14: What is Probability? - Probabilities have to be between 0 and 1 inclusive - What it means to have a probability of 0 or 1 - Recognize valid and invalid assignments of probabilities - Law of large numbers - Know that “disjoint” is the same as “mutually exclusiv e” and means that two events that can’t happen at the same time - Know what it means for two events to be independent - Know that disjoint events are never independent (and, independent events are never disjoint) - Understand the addition rule and necessary conditions for using this rule – Be able to apply this rule - Understand the multiplication rule and necessary conditions for using this rule – Be able to apply this rule - Understand complement rule and be able to apply it [P(A’) = 1 – P(A)] o Recognize that when the words “probability of at least….” are used, this many times involves using this rule - State whether or not it would be reasonable to assume a set of events is independent or disjoint 3 Chapter 18: Sampling Distribution Models + some info on probability - Discrete random variables - Probability distribution - Probability mass function (pmf) - What it means for trials to be independent - Know the difference between a population and a sample - Know what pˆ and p represent - Have a general understanding of what a sampling distribution is - Know how to find the mean and standard deviation of the sampling distribution of pˆ, given the known population proportion, p, and the sample size, n. - Know when the shape of a sampling distribution of pˆ is approximately Normal o Success/failure condition o Independence condition or as a practical approximation the randomization and 10% conditions - Be able to use the 68‐95‐99.7 rule to find the approximate probability of a specific pˆ or greater (or less) from a normally distributed sa mpling distribution of pˆ. - Know the difference between a population and a sample - Know what y and µ represent - Have a general understanding of what a sampling distribution is - Have a general understanding of the Central Limit Theorem - Know assumptions for CLT o Independence condition or as a practical approximation the randomization and 10% conditions o Large enough sample condition - Know the difference between two distributions o Real‐world distribution of the sample that one might see in a histogram o Math‐world distribution of a sample statistic such as the sample mean -


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UT Knoxville STAT 201 - Exam 2 Topics

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Chapter 8

Chapter 8

43 pages

Chapter 7

Chapter 7

30 pages

Chapter 6

Chapter 6

43 pages

Chapter 5

Chapter 5

23 pages

Chapter 3

Chapter 3

34 pages

Chapter 2

Chapter 2

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Chapter 1

Chapter 1

11 pages

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