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Essential Statistics, Regression, and Econometrics

Book Description

Essential Statistics, Regression, and Econometrics provides students with a readable, deep understanding of the key statistical topics they need to understand in an econometrics course. It is innovative in its focus, including real data, pitfalls in data analysis, and modeling issues (including functional forms, causality, and instrumental variables). This book is unusually readable and non-intimidating, with extensive word problems that emphasize intuition and understanding. Exercises range from easy to challenging and the examples are substantial and real, to help the students remember the technique better.




  • Readable exposition and exceptional exercises/examples that students can relate to

  • Website includes java applets and Excel applications

  • Focuses on key methods for econometrics students without including unnecessary topics

  • Covers data analysis not covered in other texts

  • Ideal presentation of material (topic order) for econometrics course

Table of Contents

    1. 1.1 Measurements
      1. Flying Blind and Clueless
    2. 1.2 Testing Models
      1. The Political Business Cycle
    3. 1.3 Making Predictions
      1. Okun’s Law
    4. 1.4 Numerical and Categorical Data
    5. 1.5 Cross-Sectional Data
      1. The Hamburger Standard
    6. 1.6 Time Series Data
      1. Silencing Buzz Saws
    7. 1.7 Longitudinal (or Panel) Data
    8. 1.8 Index Numbers (Optional)
      1. The Consumer Price Index
      2. The Dow Jones Index
    9. 1.9 Deflated Data
      1. Nominal and Real Magnitudes
      2. The Real Cost of Mailing a Letter
      3. Real Per Capita
    10. Exercises
    1. 2.1 Bar Charts
      1. Bowlers’ Hot Hands
      2. Bar Charts with Interval Data
    2. 2.2 Histograms
      1. Letting the Data Speak
      2. Understanding the Data
    3. 2.3 Time Series Graphs
      1. Unemployment during the Great Depression
      2. Cigarette Consumption
    4. 2.4 Scatterplots
      1. Okun’s Law
      2. The Fed’s Stock Valuation Model
      3. Using Categorical Data in Scatter Diagrams
    5. 2.5 Graphs: Good, Bad, and Ugly
      1. Omitting the Origin
      2. Changing the Units in Mid-Graph
      3. Choosing the Time Period
      4. The Dangers of Incautious Extrapolation
      5. Unnecessary Decoration
    6. Exercises
    1. 3.1 Mean
    2. 3.2 Median
    3. 3.3 Standard Deviation
    4. 3.4 Boxplots
    5. 3.5 Growth Rates
      1. Get the Base Period Right
      2. Watch out for Small Bases
      3. The Murder Capital of Massachusetts
      4. The Geometric Mean (Optional)
    6. 3.6 Correlation
    7. Exercises
    1. 4.1 Describing Uncertainty
      1. Equally Likely
      2. A Chimp Named Sarah
      3. Long-Run Frequencies
      4. A Scientific Study of Roulette
      5. Experimental Coin Flips and Dice Rolls
      6. Subjective Probabilities
      7. Bayes’s Approach
    2. 4.2 Some Helpful Rules
      1. The Addition Rule
      2. Conditional Probabilities
      3. Independent Events and Winning Streaks
      4. The Fallacious Law of Averages
      5. The Multiplication Rule
      6. Legal Misinterpretations of the Multiplication Rule
      7. The Subtraction Rule
      8. Bayes’s Rule
      9. A Bayesian Analysis of Drug Testing
    3. 4.3 Probability Distributions
      1. Expected Value and Standard Deviation
      2. The Normal Distribution
      3. Probability Density Curves
      4. Standardized Variables
      5. The Central Limit Theorem
      6. Finding Normal Probabilities
      7. The Normal Probability Table
      8. Nonstandardized Variables
      9. One, Two, Three Standard Deviations
    4. Exercises
    1. 5.1 Populations and Samples
    2. 5.2 The Power of Random Sampling
      1. Random Samples
      2. Sampling Evidence in the Courtroom
      3. Choosing a Random Sample
      4. Random Number Generators
    3. 5.3 A Study of the Break-Even Effect
      1. Imprecise Populations
      2. Texas Hold ’Em
    4. 5.4 Biased Samples
      1. Selection Bias
      2. Survivor Bias
      3. Does Anger Trigger Heart Attacks?
    5. 5.5 Observational Data versus Experimental Data
      1. Conquering Cholera
      2. Confounding Factors
    6. Exercises
    1. 6.1 Estimating the Population Mean
    2. 6.2 Sampling Error
    3. 6.3 The Sampling Distribution of the Sample Mean
      1. The Shape of the Distribution
      2. Unbiased Estimators
      3. Sampling Variance
      4. Putting It All Together
      5. Confidence Intervals
    4. 6.4 The t Distribution
    5. 6.5 Confidence Intervals Using the t Distribution
      1. Do Not Forget the Square Root of the Sample Size
      2. Choosing a Confidence Level
      3. Choosing a Sample Size
      4. Sampling from Finite Populations
    6. Exercises
    1. 7.1 Proof by Statistical Contradiction
    2. 7.2 The Null Hypothesis
    3. 7.3 P Values
      1. Using an Estimated Standard Deviation
      2. Significance Levels
    4. 7.4 Confidence Intervals
    5. 7.5 Matched-Pair Data
      1. Immigrant Mothers and Their Adult Daughters
      2. Fortune’s Most Admired Companies
      3. Fateful Initials?
    6. 7.6 Practical Importance versus Statistical Significance
    7. 7.7 Data Grubbing
      1. The Sherlock Holmes Inference
      2. The Super Bowl and the Stock Market
      3. Extrasensory Perception
      4. How (Not) to Win the Lottery
    8. Exercises
    1. 8.1 The Regression Model
    2. 8.2 Least Squares Estimation
    3. 8.3 Confidence Intervals
    4. 8.4 Hypothesis Tests
    5. 8.5 R<sup xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops">2</sup>
    6. 8.6 Using Regression Analysis
      1. Okun’s Law Revisited
      2. The Fair Value of Stocks
      3. The Nifty 50
    7. 8.7 Prediction Intervals (Optional)
    8. Exercises
    1. 9.1 Regression Pitfalls
      1. Significant Is Not Necessarily Substantial
      2. Correlation Is Not Causation
      3. Detrending Time Series Data
      4. Incautious Extrapolation
      5. Regression Toward the Mean
      6. The Real Dogs of the Dow
    2. 9.2 Regression Diagnostics (Optional)
      1. Looking for Outliers
      2. Checking the Standard Deviation
      3. Independent Error Terms
    3. Exercises
    1. 10.1 The Multiple Regression Model
      1. The General Model
      2. Dummy Variables
    2. 10.2 Least Squares Estimation
      1. Confidence Intervals for the Coefficients
      2. Hypothesis Tests
      3. The Coefficient of Determination, R<sup xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops">2</sup>
      4. Prediction Intervals for Y (Optional)
      5. The Fortune Portfolio
      6. The Real Dogs of the Dow Revisited
      7. Would a Stock by Any Other Ticker Smell as Sweet?
      8. The Effect of Air Pollution on Life Expectancy
    3. 10.3 Multicollinearity
      1. The Coleman Report
    4. Exercises
    1. 11.1 Causality
    2. 11.2 Linear Models
    3. 11.3 Polynomial Models
      1. Interpreting Quadratic Models
    4. 11.4 Power Functions
      1. Negative Elasticities
      2. The Cobb-Douglas Production Function
    5. 11.5 Logarithmic Models
    6. 11.6 Growth Models
      1. The Miracle of Compounding
      2. Frequent Compounding
      3. Continuous Compounding
    7. 11.7 Autoregressive Models
    8. Exercises