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# 统计代写|回归分析代写REGRESSION ANALYSIS代考|SOC605 Correlation Does Not Imply Causation

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## 统计代写|回归分析代写REGRESSION ANALYSIS代考|Correlation Does Not Imply Causation

I’m sure you’ve heard this expression before, and it is a crucial warning. Correlation between two variables indicates that changes in one variable are associated with changes in the other variable. However, correlation does not mean that the changes in one variable actually cause the changes in the other variable.

Sometimes it is clear that there is a causal relationship. For the height and weight data, it makes sense that adding more vertical structure to a body causes the total mass to increase. Or, increasing the wattage of lightbulbs causes the light output to increase.

However, in other cases, a causal relationship is not possible. For example, ice cream sales and shark attacks are positively correlated. Clearly, selling more ice cream does not cause shark attacks (or vice versa). Instead, a third variable, outdoor temperatures, causes changes in the other two variables. Higher temperatures increase both sales of ice cream and the number of swimmers in the ocean, which creates the apparent relationship between ice cream sales and shark attacks.
In statistics, you typically need to perform a randomized, controlled experiment to determine that a relationship is causal rather than merely correlation.

## 统计代写|回归分析代写REGRESSION ANALYSIS代考|How Strong of a Correlation is Considered Good?

What is a good correlation? How high should it be? These are commonly asked questions. I have seen several schemes that attempt to classify correlations as strong, medium, and weak.

However, there is only one correct answer. The correlation coefficient should accurately reflect the strength of the relationship. Take a look at the correlation between the height and weight data, 0.705. It’s not a very strong relationship, but it accurately represents our data.

An accurate representation is the best-case scenario for using a statistic to describe an entire dataset.

The strength of any relationship naturally depends on the specific pair of variables. Some research questions involve weaker relationships than other subject areas. Case in point, humans are hard to predict. Studies that assess relationships involving human behavior tend to have correlations weaker than $+/-0.6$.

However, if you analyze two variables in a physical process, and have very precise measurements, you might expect correlations near $+1$ or $-1$. There is no one-size fits all best answer for how strong a relationship should be. The correct correlation value depends on your study area. We run into this same issue in regression analysis.

## MATLAB代写

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