Positive vs. negative correlation of variables explained
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Analysing the relationship between two data sets is an essential skill for most jobs that deal with statistics, volumes or sales. Understanding the different types of correlation and what they tell you about data can help you make well-informed business decisions. If you often use data sets in your job, learning more about correlations can help you better communicate your results in reports and meetings. In this article, we explain the difference between positive vs negative correlation and describe the industries in which this information may be useful.
What is positive vs. negative correlation?
Positive vs. negative correlation refers to how a pattern in a data set or graph reveals the relationship between two variables. By taking a data set and displaying it graphically on an axis describing two or more variables, the result is a line that may slope upwards or downwards. This is a simple example of a positive or negative correlation, but these lines can often curve back on each other or otherwise appear more complex than one single line. You can calculate the correlation of the data by using a correlation coefficient (which uses the symbol Ï). This calculation is more useful with linear relationships than complex curves.
How are correlations relevant in the workplace?
You can improve certain processes by using the correlation of datasets as evidence to back up your decisions. By collecting data on any sort of process or ongoing project at work, you can plot the results on a graph to look for observable patterns. Calculating how closely the factors relate can also help you decide if you require a major change. It's important to remember that in correlating data, altering something about one variable does not necessarily have a corresponding effect on another.
How do positive correlations work?
A positive correlation occurs when one variable increases when the other increases. On a simple graph, this relationship usually appears as a line starting low in the bottom left corner and ending high in the top right. Positive correlation coefficients are positive numbers. Figures that increase at the same rate have a strong linear relationship. A perfect correlation would have a coefficient of +1, and the closer to +1 the coefficient is, the more directly correlated the figures are.
Common pairs of variables that yield positive correlations
Positive correlations can help many professionals to identify which positive attributes to focus on to improve the performance of a business. Here are some examples of common variable pairs that yield a positive correlation:
Successful repayments against credit rating: If a bank plots their accounts using axes to show a customer's credit rating and their successful repayments, there would be a generally positive correlation. This is because the bank uses evidence such as successful repayments to determine somebody's credit rating.
Moisture levels against crop yield: Farmers find that the higher the moisture in their soil and crops, the higher the yield their harvest gives in a particular year. You could also apply this to other aspects of farming, such as the protein content in wheat and the percentage of the harvest that's suitable for milling.
Employee benefits against job satisfaction: In a survey of employees at different workplaces, generally the more benefits a company offers, the better a satisfaction rating the employees give. These variables have a positive correlation and it may be useful for a business to know that increased benefits may have an influence on employee satisfaction.
Exercise against happiness: Doctors, therapists and personal trainers analysing a sample of people may find that those who exercise more often are generally happier due to increased serotonin levels. Although these variables have a positive correlation, it's not necessarily that exercise causes the increase in happiness and other factors may be relevant.
Number of videos against subscribers: A survey of different accounts on an online streaming platform may show a positive correlation between channels with more content and a higher rate of engagement. This sort of correlation applies not only to streamers and content creators, but also to other creative industries and marketing scenarios
How do negative correlations work?
A negative correlation happens when one variable decreases when the other increases. On a simple graph, a negative correlation is the opposite of positive correlation and the line starts high in the top left corner and ends low in the bottom right. In contrast to positive correlations, the coefficient of negative correlations is less than 0, so it's a negative number. A perfect negative correlation is -1.
It's important to remember that the coefficient changes the appearance of the graph, for example, a -0.5 correlation may start somewhere in the middle of the vertical axis of the graph and have a much flatter gradient.
Common pairs of variables that yield negative correlations
Negative correlations in workplace scenarios can help managers establish where to make cuts and reductions if necessary. Here are some examples of negative correlations:
Temperature against energy bills: During the colder months, homeowners require more heating for their homes. As a result, the cost of their energy bills compared to the ambient temperature has a negative correlation.
Transportation speed against travel time: The faster a method of transport is, the less time it takes passengers to get to their destination. These variables correlate negatively and transportation businesses can use this to determine ticket prices and measure engine performance.
Cost per unit against profit: Plotting a graph to compare the money invested in producing a product against the profit that the product makes usually shows a negative correlation. This is why businesses can focus on reducing the cost of making a product in relation to its retail price to maximise their profit margin.
Loan payments against the interest owed: A mortgage provider or bank issuing a loan receives more interest the longer a customer takes to pay their debt. As a result, the negative correlation these variables produce in a graph can help them to adjust their rates and offer support where applicable.
How do different industries use correlations?
If you're entering a new job or changing careers from the field of finance, many industries can use your knowledge of correlations to interpret data. Here are some examples of different scenarios that use positively correlated data to support business activities:
Those working in finance often use calculations of correlation coefficients in day-to-day work. Traders and investors use correlation calculations to assess the behaviour of different assets and predict how the stock market may change in the future. They can use these calculations to inform their investment decisions. Many companies invest heavily in skilled analysts or advanced calculation software to run simulations and calculate correlations more accurately and with larger samples.
Scientists typically interpret correlations between the data that experiments produce on an ongoing basis and they use correlations to inform changes in measurements. Correlations can help scientists to establish which variables influence the yield of a reaction. Identifying correlations is especially helpful in energy production for identifying the efficiency and output of different energy sources and whether they're worthwhile to invest in.
Engineers can use correlations to assess the performance of machines and fine-tune different components. Correlations between variables can help aerospace engineers to design engines and aircraft body parts. For example, there may be a correlation between the concentration of a particular chemical in a fuel and the top speed of the vehicle that uses it
Any career that involves research can use the analysis of data to present results and use correlation to draw conclusions. Most papers and studies use figures and graphs to illustrate the findings and academic presentations frequently use quantitative analysis. If you're writing academic work, consider integrating figures that show a positive correlation to display your research's findings in a visual style.
The manufacturing process for different materials may display positive correlations. The flexibility of a particular material may increase as its temperature increases or the ability of glass to store other materials. Analysing these processes can inform decisions to modify and improve materials.
In marketing, there's often a positive correlation between volume and engagement. For example, higher volumes of material output can lead to more followers. Furthermore, there's usually a positive correlation between the hours and money invested in marketing a product and the number of customers. Being able to spot and analyse these trends is a valuable tool for a business to change its approach to a demographic.
Markets usually operate on an understanding of there being a negative correlation between the price of a product and the demand for that product. This is the law of demand and it helps businesses determine whether they're selling a luxury item or an essential good and therefore set an appropriate price. Observing the scale of the correlation coefficient can help a business decide how worthwhile investments in product quality are for increasing their revenue.
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