Final answer:
Without the specific regression equation and data points, an exact prediction cannot be provided. However, historical data suggest that while a smaller percentage of registered voters may have voted in 2020 compared to 2012, the overall number of votes cast increased due to a larger pool of registered voters.
Step-by-step explanation:
To predict the voter turnout percentage for the 2020 election using a regression equation, we need specific numerical data such as prior voter turnout rates and variables that may affect these rates. Without the actual regression equation and these data points, we cannot provide an exact prediction. However, based on historical trends and available data, it may be possible to estimate this figure by considering factors like voter registration rates, the percentage of eligible voters, and the general interest in the election.
To illustrate using available data, if we knew that 77 percent of registered voters voted in the 2020 presidential election and that the number of registered voters had increased, we could use this information as part of our estimation. We would also take into account that in previous years, like in 2012, 87 percent of registered voters voted. These comparisons can be deceiving, however, without considering the overall increase in the number of votes cast and the increase in the voting-age population. This context suggests that while a smaller percentage of registered voters may have voted in 2020 versus 2012, the pool of registered voters was larger, leading to more votes being cast overall.
Voter registration and the percentage of eligible voters are critical metrics to consider for such an estimation. Still, believe that any prediction made without the actual regression equation should be approached with caution, as various unpredictable factors can influence election turnout.