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→Example: round up
Whether the person voted for Trump is the dependent variable, and each of the other questions are independent variables. The correlation coefficient can then be computed comparing the column of each dependent variable with the Trump column. What this data tells us is that being blue collar is a perfect predictor of voting for Trump -- the correlation coefficient is 1. The next best predictive factor would be whether the voter was a Republican, where they match 6 out of 7 times. Then comes gender and West Virginia residence. Assuming that the data sample was large enough to be statistically significant, we could build a model to predict whether other voters would vote for Trump by asking the same questions and computing a score using the formula:
Whether the person voted for Trump is the dependent variable, and each of the other questions are independent variables. The correlation coefficient can then be computed comparing the column of each dependent variable with the Trump column. What this data tells us is that being blue collar is a perfect predictor of voting for Trump -- the correlation coefficient is 1. The next best predictive factor would be whether the voter was a Republican, where they match 6 out of 7 times. Then comes gender and West Virginia residence. Assuming that the data sample was large enough to be statistically significant, we could build a model to predict whether other voters would vote for Trump by asking the same questions and computing a score using the formula:
.75*Republican + 1*Blue collar + .73*Male + .42*WVa
.75*Republican + 1*Blue collar + .73*Male + .42*WVa
A perfect score would be 2.896, and someone with a 0 score would be most unlikely to vote for Trump. Of course, the polling firm would be using many more questions and would experiment to fine-tune the questions to get the best predictive scores. They would also use much larger data samples.
A perfect score would be 2.897, and someone with a 0 score would be most unlikely to vote for Trump. Of course, the polling firm would be using many more questions and would experiment to fine-tune the questions to get the best predictive scores. They would also use much larger data samples.
This fine-tuning process is a form of learning. When a computer does this by itself it is called [[machine learning]].
This fine-tuning process is a form of learning. When a computer does this by itself it is called [[machine learning]].