# UT Knoxville STAT 201 - Exam 2 Topics (4 pages)

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## Exam 2 Topics

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## Exam 2 Topics

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Pages:
4
School:
University of Tennessee
Course:
Stat 201 - Introduction to Statistics
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Unformatted text preview:

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 1 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

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