Chapter 1 How do we examine our interests with data?: Distribution and mean
ㆍ Understanding our world with data
ㆍ Mapping what we want to study into numbers
ㆍ Less likely or more likely? Think about the probabilities of events
ㆍ Which group of subjects do we want to study?: The population of interest and the random sample
ㆍ Random sample assumption and sampling methods
ㆍ What useful information can we have from a sample?: sample mean and sample variance
ㆍ Normal distribution and its application: One of the most popular and useful distributions
ㆍ Alternative measures to mean: median and mode
ㆍ Chapter Summary
ㆍ Exercises

Chapter 2 Do more with the sample mean: Inference
ㆍ Sampling distribution of the sample mean and the Central Limit Theorem
ㆍ The confidence interval (CI) for the population mean μ
ㆍ Hypothesis test for the population mean μ
ㆍ How to choose an appropriate sample size in the survey for inference
ㆍ Chapter Summary
ㆍ Exercises

Chapter 3 Examining the relationship between the two quantitative variables I: Correlation coefficient and introduction to the OLS regression analysis
ㆍ Covarience and correlation coefficent
ㆍ Introduction to the OLS regression analysis
ㆍ Chapter Summary
ㆍ Exercises

Chapter 4 Examining the relationship between the two continuous variables II: Inference in the OLS regression analysis
ㆍ The normally of the error term and the sampling distribution of the OLS estimator
ㆍ The linear regression model when the sample size becomes larger
ㆍ The Confidence Interval (CI) for the regression parameter β1
ㆍ Hypothesis test for the regression parameter β1
ㆍ Chapter Summary
ㆍ Exercises

Chapter 5 Handling two or more explanatory variables in OLS regression analysis I: Multivariate Regression Analysis
ㆍ Partialling out and multicollinearity in multivariate regression analysis
ㆍ Omitted variable bias in the linear regression model
ㆍ Adding an explanatory variable and the efficiency of OLS estimators
ㆍ Chapter Summary
ㆍ Exercises

Chapter 6 Handling two or more explanatory variables in OLS regression analysis II: Hypothesis tests and more in Multivariate Regression Analysis
ㆍ Hypothesis tests in multivariable regression analysis
ㆍ Adjusted R-squared
ㆍ Chapter Summary
ㆍ Exercises

Chapter 7 The OLS regression analysis when comparing the outcomes of the two or more groups: Use of binary explanatory variables
ㆍ Estimating group differences in an outcome variable
ㆍ Estimating group differences in an outcome variable without the constant
ㆍ Estimating group differences using an interval variable
ㆍ Estimating group differences in a slope coefficient
ㆍ Estimating group differences in all explanatory variables
ㆍ Estimating the nonlinear relationship between an explanatory variable and an outcome variable
ㆍ Subsample analysis based on exogenous explanatory variables
ㆍ Chapter Summary
ㆍ Exercises

Chapter 8 Developing and completing the OLS regression analysis by using rescaling and functional specifications
ㆍ Rescaling of the outcome and explanatory variables
ㆍ Linearity in the OLS analysis
ㆍ Linear and nonlinear specifications in the OLS analysis
ㆍ Choosing specifications by considering three different types of causal paths
ㆍ General rules for including additional variables and making specifications in multivariate regression analysis
ㆍ Chapter Summary
ㆍ Exercises

Chapter 9 The OLS regression analysis when the variance of the error term depends on the explanatory variables: Heteroscedasticity
ㆍ Chapter Summary
ㆍ Exercises

Chapter 10 The regression analysis when the outcome variable is binary: LPM, Logit, and Probit
ㆍ Linear Probability Model (LPM): Using OLS when the outcome variable is binary
ㆍ The estimation of logit and probit models
ㆍ Statistical inference and goodness of it for probit and logit models
ㆍ Chapter Summary
ㆍ Exercises

Appendix
A. Software programs for data analysis: SPSS, SAS, Stata, R
B. How to do a reliable empirical study
C. z distribution table: standard normal curve tail probabilities
D. t distribution table: critical values of the t distribution
E. Chi-square distribution table: critical values of the Chi-square distribution
F. F distribution table: critical values of the F distribution