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| What is correlation in statistics |
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In life, we want to draw some correlations. We want to compare two different things which can help us understand better what type of activities are more effective for different groups in certain situations.
We can measure our satisfaction with a particular place by asking ourselves if it is safe or clean or people are kind and helpful to us. It can be just about anything we want from our experience. Then we can draw some correlations, like whether there is something that works better at night than during the day. The correlation between your friends is always positive or negative.
In this blog article, I’m going to talk about such correlation. Here we will see how to interpret these numbers from a statistical point of view. If you don’t know much about this field, be aware of this post! (Read here for an overview)
When we do some research, we have to take into account a lot of factors. For example, your task, results, and hypothesis. The hypothesis is the reason why we are doing this activity: can the treatment increase the chance of success? But it doesn’t tell us about the cause of that result, the treatment itself or our expectations or understanding. So let’s discuss some hypotheses using correlation numbers.
Correlation measures the strength of two variables. In other words, they provide quantitative information about the relationship between two variables.
Example:
Let’s say you want to study the correlation of the temperature of water in New York city to average of temperature in each zip code. To obtain this data from the city government, you need to collect it every year from the annual thermometer surveys. That way, our study will be based on years.
The formula for calculating the correlation between two measurements is shown below:
How Can I Predict These Values? :
We can predict values of correlations from their historical samples or historical averages. If we want to estimate the heat on Manhattan as its temperature, we need to analyze our historic data of high temperatures on the island. Our hypothesis should be a good approximation of what happens in future. We cannot use predictions without having to test them. Asking a few questions and observing the response can also make us sure if our prediction is correct or not. Let’s draw some examples from real-life:
You’ve been studying a bit and find out that some students in your class score lowest grades on the midterm examination this term. How would you think about their learning? Do you think you would improve their knowledge? Maybe by making them remember the information better than before the exam?
It can be useful to predict the values from time series analysis. Suppose we find the maximum value on the monthly sales of Coca Cola in 2010. By trying to look back in time in order to answer these questions, we could get that the value was higher in 2004 but now it is lower than this year. Also, we can try to calculate the changes in the value based on trends.
Another advantage is that we can also find out if the value is stable or not. For example, if we have a value 0.5, and we observe a small, positive change, and then we can predict that if this small positive change will happen again this year, it is possible that the value will become almost zero.
This method can be also used for forecasting. Instead of looking back in time, we can use historical data and try to apply predictions this time around. More articles on this topic on Forbes!
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