Difference between revisions of "De Veaux Map"

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(Chapter 10: Understanding Randomness)
(Chapter 10: Understanding Randomness)
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* 10.2: Simulating by Hand
 
* 10.2: Simulating by Hand
 
:::Basic terminology: Simulations, trials, components, response variable
 
:::Basic terminology: Simulations, trials, components, response variable
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=== Chapter 11: Sample Surveys ===
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* 11.1: The Three Big Ideas of Sampling
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:::Idea 1: Examine a part of the whole
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::::Population versus sample
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::::Bias
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:::Idea 2: Randomize
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:::Idea 3: It's the sample size
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::::Sample size
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::::Does a census make sense
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* 11.2: Populations and Parameters
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* 11.3: Simple Random Samples
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* 11.4: Other Sampling Designs
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* 11.5: From the Population to the Sample: You Can't Always Get What You Want
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* 11.6: The Valid Survey
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* 11.7: Common Sampling Mistakes or How to Sample Badly

Revision as of 21:41, 17 November 2018

Part I: Exploring and Understanding Data

Chapter 1: Exploring and Understanding Data =

  • 1.1: What is Statistics?
  • 1.2: Data
  • 1.3: Variables
Types of Variables: Categorical, Quantitative, Identifier, Ordinal

Chapter 2: Displaying and Describing Categorical Data

  • 2.1: Summarizing and Displaying a Single Categorical Variable
The area principle
Frequency tables
Bar charts
Pie charts
  • 2.2: Exploring the Relationship Between Two Categorical Variables
Contingency tables
Conditional distributions
Independence
Plotting conditional distributions (with pie charts, bar charts and segmented bar charts)

Chapter 3: Displaying and Displaying Quantitative Data

  • 3.1: Displaying Quantitative Variables
Histograms
Stem and leaf displays
Dotplots
  • 3.2: Shape
Unimodal, bimodal or multimodal
Symmetric or skewed
Outliers
  • 3.3: Center
Median
  • 3.4: Spread
Range, min, max
Interquartile range, Q1, Q3
  • 3.5: Boxplots and 5-Number Summaries
  • 3.6: The Center of a Symmetric Distribution: The Mean
Mean or Median?
  • 3.7: The Spread of a Symmetric Distribution: The Standard Deviation
  • 3.8: Summary---What to Tell About a Quantitative Variable

Chapter 4: Understanding and Comparing Distributions

  • 4.1: Comparing Groups with Histograms
  • 4.2: Comparing Groups with Boxplots
  • 4.3: Outliers
  • 4.4: Timeplots
  • 4.5: Re-Expressing Data: A First Look
...To improve symmetry
...To equalize spread across groups

Chapter 5: The Standard Deviation as a Ruler and the Normal Model

  • 5.1: Standardizing with z-Scores
  • 5.2: Shifting and Scaling
Shifting to adjust the center
Rescaling to adjust the scale
Shifting, scaling and z-Scores
  • 5.3: Normal Models
The "nearly normal condition"
The 68-95-99.7 Rule
  • 5.4: Finding Normal Percentiles
Normal percentiles
From percentiles to scores: z in reverse
  • 5.5: Normal Probability Plots

Part II: Exploring Relationships Between Variables

Chapter 6: Scatterplots, Association, and Correlation

  • 6.1: Scatterplots
Direction (negative or positive)
Form
Strength
Outliers
Explanatory and response variables
  • 6.2: Correlation
Formula
Assumptions and conditions for correlation, including...
"Quantitative variables condition,"
"Straight enough condition,"
"No outliers condition"
  • 6.3: Warning: Correlation Does Not Equal Causation
  • 6.4: Straightening Scatterplots

Chapter 7: Linear Regression

  • 7.1 Least Squares: The Line of "Best Fit"
The linear model
Predicted values and residuals
The least squares line and the sense in which it is the best fit
  • 7.2 The Linear Model
Using the linear model to make predictions
  • 7.3 Finding the Least Squares Line
Formulas for slope and intercept
  • 7.4 Regression to the Mean
Etiology of the word "Regression"
Math Box: Derivation of regression formula
  • 7.5 Examining the Residuals
Formula for residuals
Appropriate (lack of) form of Residuals versus x-Values plot
The residual standard deviation
  • 7.6 R^2---The Variation Accounted For by the Model
How big should R^2 be?
Predicting in the other direction---A tale of two regressions
  • 7.7 Regression Assumptions and Conditions
"Quantitative variable" condition
"Straight enough" condition
"Outlier" condition
"Does the plot thicken?" condition
Judging the conditions with the residuals-versus-predicted-values plot

Chapter 8: Regression Wisdom

  • 8.1: Examining Residuals
Getting the "bends": When the residuals aren't straight
Sifting residuals for groups
Subsetting with a categorical variable
  • 8.2: Extrapolation: Reaching Beyond the Data
Warning with extrapolation
Warning with predicting what will happen to cases in the regression if they were changed
  • 8.3: Outliers, Leverage, and Influence
  • 8.4: Lurking Variables and Causation
  • 8.5: Working with Summary Values

Chapter 9: Re-expressing Data: Get It Straight!

  • 9.1: Straightening Scatterplots -- The Four Goals
Goal 1: Make the distribution of a variable more symmetric.
Goal 2: Make the spread of several groups more alike, even if their centers differ
Goal 3: Make the form of a scatterplot more nearly linear
Goal 4: Make the scatter in a scatterplot spread out evenly rather than thinkening at one end
Recognizing when a re-expression can help
  • 9.2: Finding a Good Re-Expression
Plan A: The ladder of powers
Re-expressing to straighten a scatterplot
Comparing re-expressions
Plan B: Attack of the logarithms
Multiple benefits to re-expressions
Why not just fit a curve?

Part III: Gathering Data

Chapter 10: Understanding Randomness

  • 10.1: What Is Randomness?
Meaning of the word "random"
Discussion of the process of generating random numbers
  • 10.2: Simulating by Hand
Basic terminology: Simulations, trials, components, response variable

Chapter 11: Sample Surveys

  • 11.1: The Three Big Ideas of Sampling
Idea 1: Examine a part of the whole
Population versus sample
Bias
Idea 2: Randomize
Idea 3: It's the sample size
Sample size
Does a census make sense
  • 11.2: Populations and Parameters
  • 11.3: Simple Random Samples
  • 11.4: Other Sampling Designs
  • 11.5: From the Population to the Sample: You Can't Always Get What You Want
  • 11.6: The Valid Survey
  • 11.7: Common Sampling Mistakes or How to Sample Badly