# 10 Positive Correlation Examples

Reviewed by Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

When two variables in a data set are connected, it’s known as positive correlation. Such analysis determines how an increase or decrease of one factor results in the same alteration for another variable – be it rising or falling.

When there is a positive correlation between two variables, an increase in one variable is associated with a corresponding increase in the other, and vice versa.

For a positive correlation example, when one devotes more hours to studying, they will likely achieve higher grades in school. In other words, if you study more frequently and for longer periods of time, then your grades are bound to improve.

By assessing positive correlations, one can gain valuable insight into links between variables which may not be apparent at first. This analysis also reveals potential investment prospects and could provide a glimpse of what the future holds.

Contents

## Positive Correlation Definition

A positive correlation occurs when two variables display a linear relationship. An increase in one is directly linked with a rise in the other (Heiman, 2014).

In the fields of economics, psychology, and philosophy, a positive correlation between two variables implies that there is an inseparable relationship. In other words, any adjustment to one variable will cause a linked alteration to its counterpart.

According to Sharagwal and Sharagwal (2021),

“…when two variables change in the same direction, i.e., variable Y also increases due to an increase in the variable X, a correlation between Y and X is said to be positive” (p. 142).

When two figures show a positive correlation, their correlation coefficient will always exceed zero. If these numbers increase in an orderly and consistent pace, then it implies that there exists a powerful linear relationship between them.

For example, if one variable is ice cream consumption and the other is the crime rate, then a positive correlation would mean that as ice cream consumption increases, so does the crime rate. Similarly, if ice cream consumption decreases, the crime rate will also decrease.

It is important to note that a positive correlation does not necessarily mean that one variable directly causes the other but rather that they change in tandem.

## 10 Positive Correlation Examples

• Exercise & Health: Exercise is widely known to have a strong association with excellent overall health. Multiple studies confirm that engaging in consistent physical activity can lead to better mental and physical wellbeing, longer life expectancy, as well as improved quality of living.
• Education Level and Income: It is a widely accepted notion that the more educated an individual becomes, the greater their income potential. This relationship can be seen within numerous countries, demographics and industries globally.
• Sleep & Memory: A person’s sleep is directly correlated with their ability to remember information. Studies have found that individuals who get enough quality sleep are more likely to remember details better than those who don’t get enough, or any at all.
• Sugar Intake and Obesity: Eating large amounts of sugar has been associated with an increased risk for weight gain, obesity, and other health issues like diabetes. This link between sugar intake and obesity can be explained by a positive correlation – as sugar consumption increases, so does the likelihood of becoming overweight or obese.
• Price Point & Quality: For many products, there is a positive correlation between price point and product quality—the higher the price you pay for something, the better its quality will typically be compared to similar products at lower prices.
• Temperature and Sunscreen Use: As air temperature rises, so does sunscreen use—people understand that when it’s hot outside, it’s important to wear sunscreen to protect yourself from UV rays and potential skin damage from sunburns or cancerous moles developing on your skin from prolonged exposure without protection from ultraviolet radiation.
• Age & Technical Knowledge: It’s no secret that younger generations are more tech-savvy than older generations due to how much technology has evolved in recent years. A strong positive correlation between age and technical knowledge can explain this difference. Older people tend to have less experience when it comes to using new technology (like smartphones or computers) compared to younger people who grow up surrounded by modern technology from birth onward.
• Diet and Exercise Habits & Mental Health Outcomes: A diet rich in whole foods (vegetables, fruits, lean proteins) combined with regular physical activity have both been linked to improved mental health outcomes like reduced stress levels, decreased symptoms of depression/anxiety, etc. This connection could be partially explained through a positive correlation – when one increases (diet/exercise habits), so does the other (mental health).
• Climate Change & Frequency of Droughts: Global warming impacts weather worldwide, making droughts common occurrences during hot summers. There is a correlation between the intensity of climate change and the frequency of droughts due to warmer air temperatures drying out soils faster than usual, leading to water tables depleting quicker than before, thus causing droughts more often.
• School Attendance & Academic Performance: It’s widely accepted that there is a direct link between school attendance rates and academic performance. Students who attend school regularly are more likely to do better academically than those who do not. This connection can be described through a strong positive correlation in which an increase in one results in an increase in the other (school attendance resulting in improved academic performance).

## Negative vs. Positive vs. Zero Correlation

While the above examples describe positive correlations, there are also negative and zero ones. The former occurs when an increase in one variable leads to a decrease in the other, while the latter refers to when two variables have no direct correlation (Burns & Burns, 2012).

• A negative correlation is a relationship between two variables where one variable increases as the other decreases. It means that when one variable increases, the other decreases, and vice versa.
• A positive correlation is a relationship between two variables where both variables move in the same direction. It means that when one variable increases, so does the other, or when one variable decreases, so does the other (Heiman, 2014).
• A zero correlation (also known as no correlation) occurs when there is no relationship between two variables. As one variable changes (whether it increases or decreases), it has no effect on the other variable and vice versa (Burns & Burns, 2012).
• An illusory correlation happens when two variables (people, events, or behaviors), are perceived to have a relationship, when in fact, there is no logical reason for them to be correlated. For example, if you see that a coin has flipped heads six out of six times, you may think that heads is likely to turn up again the next time. This is an illusion. There is still a 50/50 chance that tails will turn up.

## How to Determine Positive Correlation

To determine whether two variables have a positive correlation, one needs to calculate the correlation coefficient (often represented as r).

This value will range from -1 to +1, with -1 representing perfect negative correlation, 0 representing no correlation at all, and +1 representing perfect positive correlation.

The formula for calculating the correlation coefficient is as follows:

R = Σ(x_i – x̅) (y_i – y̅) / √(Σ(x_i – x̅)² Σ(y_i-y̅)² ),

where:

• x_i represents each individual x variable value;
• y_i represents each individual y variable value;
• x̅ represents the mean of all x values;
• y̅ represents the mean of all y values (Sharma, 2019).

If the calculated r-value is closer to +1, then there is a positive correlation between your variables; if it’s closer to 0, then there is no significant relationship, and if it’s closer to -1, then there is a negative correlation.

## Positive Correlation Strength

Positive correlation strength is a measure of the degree to which two variables are positively correlated. It can range from 0 (no positive correlation) to +1 (perfect positive correlation) (Mcmillan, 2008).

For example, if you were looking at whether ice cream consumption and crime rate were related, a zero correlation would indicate that these variables have no relationship.

At the same time, a perfect positive correlation of +1 would indicate that as ice cream consumption increases, so does the crime rate.

The strength of the relationship between two variables depends on the value of their correlation coefficient.

Generally speaking, values closer to +1 indicate a strong positive correlation, and values closer to 0 indicate a weaker or non-existent relationship (Mcmillan, 2008).

For example, if your correlation coefficient was 0.5, it would be considered to have moderate positive correlation strength. In contrast, an r-value of 0.75 would be considered to have strong positive correlation strength.

## Importance and Application of Positive Correlation

A positive correlation has many important applications across various fields, from economics to social sciences, since it can provide insight into relationships between different variables.

For example, in economics, an understanding of positive correlations can help assess risk and inform sound investment decisions.

A deeper understanding of the correlations between stock prices and other substantive factors can be used to project potential returns on investment and improve the accuracy with which predictions are made.

In healthcare, understanding positive correlations can be used to identify potential medical advances based on existing research.

By studying different variables that may be positively correlated with one another, researchers may be able to make connections between seemingly unrelated elements that could lead to groundbreaking treatments or cures for certain diseases.

Finally, a positive correlation is also important in social science research, as it can be used to study relationships between different variables such as gender and education level.

By studying the degree of correlation between these variables, researchers can gain insight into how different social factors relate to one another and use this knowledge for decision-making in areas such as policy planning or public outreach efforts.

## Conclusion

A positive correlation is an important measure of the degree to which two variables are related. The strength of a positive correlation can range from 0 (no correlation) to +1 (perfect positive correlation).

This valuable metric has many practical applications, from economics and healthcare to social science. It is computed with the Pearson correlation coefficient that takes into account the mean and standard deviation of both x and y values.

By delving into the connections between multiple variables, researchers can accurately assess risks and make educated decisions in their areas of expertise.

A positive correlation is one of the most helpful tools when forecasting investment returns or discovering potential medical breakthroughs, especially in comparison to other measurements.

## ​References

Agarwal, A., & Agarwal, S. (2021). Economics class XI –SBPD publications. SBPD Publications.

Burns, R. B., & Burns, R. A. (2012). Business research methods and statistics using SPSS. New York: Sage.

DePoy, E., & Gilson, S. (2016). Social work research and evaluation. SAGE Publications.

Heiman, G. (2014). Basic statistics for the behavioral sciences. Wadsworth.

Mcmillan, J. H. (2008). Assessment essentials for standards-based education. New York: Corwin Press.

Sharma, J. K. (2019). Business statistics. London: Vikas Publishing.

Viktoriya Sus (MA)

Viktoriya Sus is an academic writer specializing mainly in economics and business from Ukraine. She holds a Master’s degree in International Business from Lviv National University and has more than 6 years of experience writing for different clients. Viktoriya is passionate about researching the latest trends in economics and business. However, she also loves to explore different topics such as psychology, philosophy, and more.

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.