Select a response (dependent) variable of your choice, for example US corn yield in the year 2012, from the USDA site (www.usda.gov (Links to an external site.)Links to an external site.); or the gasoline price at pump in the US in 2013; or the current obesity level in the US population from (www.cdc.gov). After learning about this variable and doing some research, identify one or two classification (independent) variable(s) that may be affecting your selected response variable. Your classification (independent) variable may be a quantity or a category. Depending upon your collected data, now perform an adequate analysis. For example, if both of your variables (independent and dependent) are quantitative, then perform correlation and regression analysis; however, if your classification variable is a category and you have more than two means to compare, then perform analysis of variance. In each case, your analysis should be comprehensive, using SPSS, including all the required components of the selected analysis, such as r, R2, residuals, forecast equation (y = intercept + slope * x; where y is response (dependent) variable and x is independent variable). In the case of ANOVA, develop an appropriate table and test your hypothesis.
In either analysis, determine if there exist other variables (confounding or lurking) that may be significantly affecting your model outcome.
