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Empirical_Knowledge_of_Cause_and_Effect

# Empirical_Knowledge_of_Cause_and_Effect - Empirical...

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Empirical Knowledge of Cause and Effect Here is a relevant question for college students. Will more study hours improve one’s grades? This is a question of cause and effect. Does an increase in study hours cause an improvement in grades? There are good reasons to expect an affirmative answer. After all, grades are supposed to measure the amount of learning. And learning takes place while one studies. Also, professors tell students to study and assign material that will appear on the exam. Presumably, time spent studying the material that will appear on the exam will result in better performance on the exam. But these arguments are theoretical. They claim that the cause and effect relationship appears to be true, that it makes sense. But many arguments that make sense turn out to be false. We want to know whether, in the real world, regardless of the logic, the presumed relationship exists? Does more studying lead to higher grades? This paper will look at how economists investigate such a question. Regression The first step is to find some data. We need to measure study hours and measure grades. Let’s measure study time as the average number of hours studied per day during the semester and grades as the semester grade point average. Hours studied per day is the independent variable ; it is the cause. The dependent variable is the semester grade

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point average; it measures the effect. Now the question is specific. Will an increase in hours studied per week result in an increase in the semester grade point average? To help understand the relationship, we plot the data in the chart below. Perhaps we can visually infer the relationship. The chart below does not seem to indicate that there is a relationship. The points are scattered about without any obvious pattern. Should we then conclude that studying does not affect grades? studying and grades 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 5 10 15 20 25 30 35 hours studied grade point average Series1 Perhaps more precision is in order. The relevant statistical technique for inferring a relationship is called linear regression. Basically, we construct a line that approximates
the relationship inherent in the scatter of points. (The exact process draws the line that minimizes the sum of the squares of the distances from the line to the points.) This is show in the next chart. studying and grades 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 5 10 15 20 25 30 35 hours studied grade point average Series1 slope = .01 The regression line shows that as the number of hours studied increases we do see a small uptick in the grade points averages. The slope is 0.01, meaning that for every additional hour the gpa rises by .01. In other words, to raise it by a whole grade would require 100 additional hours per week!

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