1. Define "unbiasedness" and "efficiency" in reference to unstandardized partial slope coefficients (from OLS multiple regression). Why is it desirable to have the most efficient, unbiased estimate? Feel free to include hand-drawn figures to clarify the concepts. Hint – it might be useful to begin by discussing the sampling distribution of "b" ("b" is the unstandardized partial slope coefficient).
2. Sociologists who study social stratification and inequality have increasingly switched their focus from income inequality to wealth inequality. Income refers to money, wages, and payments received in return for an occupation or investments. Wealth refers to accumulated assets in various forms (e.g., real estate, stocks, bonds, cash reserves, etc.); anything of economic value that is bought, sold, or stocked for future disposition, or invested to bring economic return. Wealth inequality is more significant than income inequality partly because it can be easily transferred across generations. Thus, it plays an important role in the maintenance of social inequality over time.
   Using SPSS and data from the 1992 Health and Retirement Survey (HRS), investigate wealth inequality in the U.S. The HRS is a nationally representative biennial longitudinal survey of persons born between 1931 and 1941. Examine the relationships between net worth [ hhnetworth ] with 1) sex, 2) race, 3) religious preference, 4) age, 5) education, and 6) income. You suspect that the relationship between income and net worth differs by race. Also, you suspect that the relationship between education and net worth is non-linear. Note: net worth can be negative because some people owe more than they own. Don’t be surprised when you see negative values .
3. Pasted below are results from a binary logistic regression from SPSS. The dependent variable is political tolerance ("There are always some people whose ideas are considered bad or dangerous by other people – for instance, somebody who is against all churches and religion. Should such a person be allowed to teach in a college or university?" The variable is coded: 1=yes, 0=no). Independent variables include sex (male=1, female=0), marital status (1=married, 0=not married), age (in years), and education (in years).
   1. Use the logged odds and odds coefficients to describe the relationships (i.e., which variables are statistically significant, how would you interpret/characterize the relationships?, etc.).
   2. Use the classification tables, chi-square and -2 Log likelihood, and pseudo r-squared statistics to describe the overall fit of the model.

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