By the end of the unit, students should be able to
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Interpret the slope of the linear regression model in the context of the data.
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Interpret the y-intercept of the linear regression model in the context of the data.
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Use the correlation coefficient for a scatterplot to describe the form, direction, and strength of the linear relationship between the explanatory and response variables.
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Use the coefficient of determination to determine the fraction of variation in the dependent variable that is explained by the independent variable.
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Understand that correlation does not imply causation.
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Create a residual plot for a bivariate data set and use the plot to determine whether a linear model is appropriate for the data.
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Use information from statistical analyses to come up with possible solutions to income inequality.
In this unit, students will have the opportunity to explore the extent to which variables of their choosing correlate with the distribution of income in a location, whether a state or country. They will acquire the data they need from resources like the World Inequality Database or the National Bureau of Labor Statistics. Once they have analyzed their data, they will share their results with their peers. By the end of the unit, they should be able to use their gathered data, research, and even personal convictions to formulate answers to the following questions:
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Do you believe that income inequality should exist to a certain degree? Why or why not?
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What are the ways in which income inequality can negatively impact our society?
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Which variables most strongly correlate with income inequality and why?
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Which variables do not seem to correlate strongly with income inequality?
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What are the consequences of income inequality for earners in all brackets?
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What are the public policies that have kept income inequality low in the countries or states with the lowest Gini coefficients?
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How can the United States reform public policies to close the gap between high-income and low-income earners?
