![]() ![]() ![]() Biology: When consumption of ‘adult drinks’ rises, mental acuity and physical coordination decrease sharply.Consequently, an upswing in these rates leads to a drop in discretionary spending. Consumer Economics: When mortgage interest rates surge, consumers feel the pinch of extra housing costs and decrease their spending in other areas.With less demand in the market, the less people will be inclined to buy a house. This is because high interest rates make it harder to get a mortgage. Consumer Economics: As interest rates go up, house prices go down.Medicine: Excessive sugar intake has been linked to decreased oral hygiene, as it not only stimulates bacterial growth but also weakens tooth enamel.This is because putting off tasks can lead to incomplete work, or increase stress levels, or cause missed deadlines. Psychology: When a student’s procrastination increases, their academic performance decreases.We could also state this as a positive correlation: when there is more smog in our atmosphere, we experience more asthma. Negative Correlation in the Environment: A study of air pollution and asthma rates reveals a clear correlation: the higher the levels of air contamination, the poorer out health becomes.When there are too many students per teacher, it becomes difficult for individual attention to be given, which can ultimately impact student performance negatively. Negative Correlation in Education: Higher student-teacher ratios are correlated with lower student achievement scores.This is because poorer people have less access to nutrition, healthcare, and other goods and services that help achieve a healthy lifestyle. Negative Correlation in Sociology: As poverty rises, life expectancy decreases.This is because conflicts can erode trust, communication, and intimacy between individuals. Negative Correlation in Psychology: When interpersonal conflict increases, satisfaction in relationships decreases.Negative correlations can be a powerful tool for researchers, allowing them to uncover and reveal cause-and-effect relationships between various aspects of our world.īy studying these connections, we can gain much-needed insights into the environment around us. This process makes it possible to assess how changes in one variable may influence the other. Researchers can quantify the strength of a negative correlation between two variables and measure its effect on one another by utilizing techniques such as regression analysis. Mathematicians seem to simply call these scenarios "non-linear" or "curvilinear" relationships, without seeming to notice that there are invariably two distinct relationships being identified by the data.A negative correlation is a relationship between two variables in which one variable decreases as the other increases.Īs a negative correlation example from psychology, one might observe a negative correlation between happiness and the number of hours worked that is, as working time increases, contentment diminishes.įrom poverty and life expectancy to crime rates and education levels, as well as employment rates and inflation – negative correlations can be found in many areas. While I have always used the term "split" effect to describe such phenomenon, I have not been able to find this phenomenon acknowledged or identified (by any particular term) amongst economists or mathematicians. Thus, we often see two or more different effects express themselves through a full range of data. This is because at very high rates of taxation, people either lose interest in working, or they start to seek ways of hiding their income from the government. ![]() However, after a certain tax rate is reached, we start to see a new effect take place wherein the tax revenue drops off as the tax rate is increased further. I call this phenomenon a "split" effect.įor example, in the Laffer curve, we at first see the government raise more tax revenue as tax rates increase because they collect more money from citizens. However, sometimes one effect drops off and then a new effect takes over. In economics, we're always interested in identifying "effects" that take place between variables. In Problem #3, illustrations A and B, you show something we see in economics quite a bit. ![]()
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