Analyze The 2016 Presidential Election Results For Utah

Analyze The 2016 Presidential Election Results for Utah

Analyze The 2016 Presidential Election Results for Utah

In this assignment you will analyze the 2016 Presidential Election results for Utah. Please see the Excel file Scatter Plot Assignment Data for results by county as well as each county’s population, ethnic makeup, unemployment, median income, and percent college educated. Sources: Please answer the questions below.

Paper For Above instruction

Introduction

The 2016 United States presidential election was a significant political event that reflected diverse voter preferences across different states and counties. Utah, known for its conservative voting patterns, showcased interesting variations in voting behaviors influenced by demographic and socioeconomic factors. In analyzing Utah’s election data, particularly the county-level results for Donald Trump, Hillary Clinton, and third-party candidates, we can better understand the underlying correlations between demographic variables and voting outcomes. This paper aims to analyze the election results by exploring correlation patterns, performing multiple regression analyses, and making predictive assessments based on demographic data provided in the dataset.

Correlation Between Trump and Clinton Votes

Expected Correlation

Given the nature of vote percentages in a single election, I would expect a negative correlation between the percentage of votes received by Trump and Clinton. As the percentage of votes increases for one candidate, it is likely to decrease for the other because votes are allocated within a fixed total of 100%. Assigning Clinton as the independent variable, an increase in Clinton's vote percentage should correspond to a decrease in Trump's, indicating an inverse relationship.

Scatter Plot and Trendline

To visualize this relation, a scatter plot with Clinton’s vote percentage on the x-axis and Trump’s on the y-axis should be generated. The plot is expected to show a downward trend, consistent with a negative correlation. Adding a trendline facilitates observing the overall pattern in the data. Displaying the equation of the trendline and the R-squared value will help quantify this relationship. The equation typically takes the form of a linear function, and the R-squared indicates how well the trendline fits the data points.

Correlation Coefficient

Calculating the Pearson correlation coefficient (r) offers a numerical measure of the linear relationship. Using the Pearson Correlation Coefficient table, if |r| exceeds the critical value at the 0.05 significance level, we can conclude that the correlation is statistically significant. Given the expected negative relationship, a significant negative r would confirm the hypothesis that higher Clinton vote percentages are associated with lower Trump percentages, and vice versa.

Multiple Regression Analyses

Clinton’s Vote Percentage

Conducting multiple regression with Clinton’s vote percentage as the dependent variable involves analyzing the influence of independent variables such as population, ethnic makeup, unemployment rate, median income, and percent college-educated. Variables showing statistically significant relationships at the 0.05 level suggest that they meaningfully predict Clinton’s support within the county data. Variables may show positive or negative correlations depending on their nature; for example, higher median income might correlate positively if wealthier areas tend to favor Clinton or negatively if they lean conservative.

Constructing the Regression Equation

Using the statistically significant predictors identified earlier, I will create a multiple regression equation. Variables that had no significant correlation (p > 0.05) will be excluded to streamline the model. This equation allows for predicting Clinton’s vote percentage based on the demographic variables. For example, the model might take the form:

Clinton_Percentage = a + b1(Population) + b2(Unemployment) + b3*(Percent College Educated), where coefficients (b1, b2, b3) indicate the nature and strength of each predictor’s relationship.

Sign of the Correlations

The signs of the coefficients (positive or negative) will provide insights into how each demographic factor influences voting preferences. For instance, a positive coefficient for percent college educated would suggest that higher educational attainment correlates with increased support for Clinton in Utah’s counties.

Repeat Analyses for Trump, McMullin, Johnson, and Stein

To conduct a comprehensive understanding, the same multiple regression process will be repeated for each candidate—Trump, McMullin, Johnson, and Stein—as the dependent variable. Comparing these models will reveal different demographic influences and their significance, illustrating how support for each candidate relates to county demographics.

Predictive Analyses

Predict Trump’s Vote Percentage

Using the regression model for Trump, I will predict the percentage of votes Trump would receive in a hypothetical county that is 87% White and 19% College Educated. Substituting these values into the regression equation will provide an estimated vote share for Trump, reflecting how demographic composition affects support.

Limitations of Predicting Clinton’s Votes

Attempting to predict Clinton’s vote percentage in a county that is 30% White and 65% College Educated using the previous model would be inappropriate if the variables do not align with the predictors identified for Clinton’s model or if multicollinearity exists. Incompatibility occurs because different models are optimized for different dependent variables, and inputting demographic data into the wrong model could produce unreliable predictions.

Conclusion

This assignment provided insights into the relationship between demographic factors and voting behavior in Utah’s 2016 presidential election. The negative correlation between Trump and Clinton highlighted the competitive nature of voter choices within a constrained total. Multiple regression analyses illuminated which demographic variables significantly influenced each candidate’s support, revealing underlying patterns such as the influence of education and income. Predictive exercises demonstrated the practical applications of these models, while also underscoring their limitations. Overall, the analysis underscores the importance of demographic context in electoral outcomes and offers valuable perspectives for political strategists and analysts.

References

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