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 estimatorㆍ 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 variablesㆍ 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: Heteroscedasticityㆍ 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 binaryㆍ The estimation of logit and probit models ㆍ Statistical inference and goodness of it for probit and logit models ㆍ Chapter Summary ㆍ Exercises AppendixA. Software programs for data analysis: SPSS, SAS, Stata, RB. 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