# Econometrics For Dummies

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Econometrics For Dummies to help you get the most out of your economics education. By using econometrics carefully and conscientiously, you can get the data to speak. But you better learn the language if you hope to understand what it’s saying!

**Getting Started with Econometrics**

Econometrics: The Economist’s Approach to Statistical Analysis

Evaluating Economic Relationships

Using economic theory to describe outcomes and make predictions

Relying on sensible assumptions

Applying Statistical Methods to Economic Problems

Recognizing the importance of data type, frequency, and aggregation

Avoiding the data-mining trap

Incorporating quantitative and qualitative information

Using Econometric Software: An Introduction to STATA

Getting acquainted with STATA

Creating new variables Estimating, testing, and predicting

**Getting the Hang of Probability**

Reviewing Random Variables and Probability

Distributions Looking at all possibilities: Probability density function (PDF)

Summing up the probabilities: Cumulative density function (CDF)

Putting variable information together: Bivariate or joint probability density

Predicting the future using what you know: Conditional probability density

Understanding Summary Characteristics of Random Variables

Making generalizations with expected value or mean

Measuring variance and standard deviation

Looking at relationships with covariance and correlation

**Making Inferences and Testing Hypotheses**

Getting to Know Your Data with Descriptive Statistics

Calculating parameters and estimators

Determining whether an estimator is good

Laying the Groundwork of Prediction with the Normal and Standard Normal

Distributions Recognizing usual variables: Normal distribution

Putting variables on the same scale: Standard normal distribution (Z)

Working with Parts of the Population: Sampling Distributions Simulating and using the central limit theorem Defining the chi-squared (χ2), t, and F distributions

Making Inferences and Testing Hypotheses with Probability

Distributions Performing a hypothesis test

The confidence interval approach

The test of significance approach

** Building the Classical Linear Regression Model**

**Understanding the Objectives of Regression Analysis**

Making a Case for Causality

Getting Acquainted with the Population Regression Function (PRF)

Setting up the PRF model Walking through an example

Collecting and Organizing Data for Regression Analysis

Taking a snapshot: Cross-sectional data

Looking at the past to explain the present: Time-series data

Combining the dimensions of space and time: Panel or longitudinal data

Joining multiple snapshots: Pooled cross-sectional data

**Going Beyond Ordinary with the Ordinary Least**

Squares Technique Defining and Justifying the Least

Squares Principle Estimating the Regression

Function and the Residuals

Obtaining Estimates of the Regression Parameters

Finding the formulas necessary to produce optimal coefficient values

Calculating the estimated regression coefficients

Interpreting Regression Coefficients

Seeing what regression coefficients have to say

Standardizing regression coefficients

Measuring Goodness of Fit Decomposing variance

Measuring proportion of variance with R2

Adjusting the goodness of fit in multiple regression

Evaluating fit versus quality

Assumptions of OLS Estimation and the Gauss-Markov Theorem

Characterizing the OLS Assumptions Linearity in parameters and additive error Random sampling and variability Imperfect linear relationships among the independent variables Error term has a zero conditional mean; correct specification Error term has a constant variance Correlation of error observations is zero Relying on the CLRM Assumptions: The Gauss-Markov Theorem Proving the Gauss-Markov theorem Summarizing the Gauss-Markov theorem Chapter 7: The Normality Assumption and Inference with OLS Describing the Role of the Normality Assumption The error term and the sampling distribution of OLS coefficients Revisiting the standard normal distribution Deriving a chi-squared distribution from the random error