Much of the analysis about why the UK voted to leave the European Union in June 2016 has been done by looking at individual factors in isolation, or using opinion poll data from both before and after the vote.
In a new research paper, I applied two statistical analyses to the actual referendum voting data obtained from the Electoral Commission and the UK’s latest census data. I found that while voters’ level of higher education was the most important factor, the gender of voters and the turnout level also had parts to play in the victory for the Leave campaign.
In a new research paper, I applied two statistical analyses to the actual referendum voting data obtained from the Electoral Commission and the UK’s latest census data. I found that while voters’ level of higher education was the most important factor, the gender of voters and the turnout level also had parts to play in the victory for the Leave campaign.
The first method I used was called a “multivariate regression” analysis – a powerful statistical technique used for predicting the unknown value of a dependent variable, such as the percentage of Leave votes, from variables that can explain it, such as education, turnout, age and gender. This method can answer the question: “How much does the percentage of Leave votes change when we alter a significant factor, keeping other factors unchanged?”

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