1.16 Income and education in US counties: The scatterplot below shows the relationship between percapita income (in thousands of dollars) and percent of population with a bachelor's degree in 3,143counties in the US in 2010.Per capita income (in $1,000)6040-20-a) What is the explanatory variable?O Per Capita Income (in $1000s)O Percent with a Bachelor's Degree1030Percent with Bachelor's degreeb) What is the response variable?5) Baby Learni...50O Percent with a Bachelor's DegreeO Per Capita Income (in $1000s)70

The objective of the question is to identify the explanatory and response variables in a given scenario. In statistics, an explanatory variable is used to predict or explain changes in the response variable.In this case, we are looking at the relationship between per capita income and the percent of population with a bachelor's degree in US counties. The question is asking us to identify which of these two variables is the explanatory variable (the one that explains or predicts changes in the other) and which is the response variable (the one that responds to changes in the other).The explanatory variable is the one that is presumed to cause or determine the response variable. In this case, it is more logical to assume that the level of education (percent with a Bachelor's degree) would influence the per capita income, rather than the other way around. Therefore, the explanatory variable is 'Percent with a Bachelor's Degree'.The response variable is the one that is affected by the explanatory variable. In this case, the per capita income is likely to be affected by the level of education. Therefore, the response variable is 'Per Capita Income (in $1000s)'.In the given scenario, the explanatory variable is 'Percent with a Bachelor's Degree' and the response variable is 'Per Capita Income (in $1000s)'. This is because the level of education is likely to influence the per capita income.