Resolved: Linear vs Correlaion
why did you use regression for a correlation relationship and how accurate and reliable these results, since temperature is not a guarantee for any sales.
2 answers ( 1 marked as helpful)
Hello Doaa,
Thanks for reaching out!
Great question!
Regression and correlation are closely related but serve different purposes. Correlation measures the strength and direction of a relationship between two variables—temperature and ice cream sales in this case—but it doesn't provide a predictive model. On the other hand, regression allows us to quantify this relationship and make predictions based on it.
By using linear regression, we can establish a mathematical equation that estimates how sales might change with temperature. Of course, you're absolutely right—temperature alone is not a guarantee of sales. Many other factors (such as marketing efforts, consumer preferences, or economic conditions) can influence sales, which is why regression models should always be interpreted with some caution.
In terms of accuracy and reliability, regression results depend on the data quality and the strength of the relationship. Metrics like R-squared and p-values help us evaluate how well the model fits the data and whether the relationship is statistically significant. However, it's important to recognize that regression models simplify reality and should be used as a tool for insight rather than a perfect predictor.
Let me know if you’d like more details on how we assess the reliability of regression models!
Kind regards,
The 365 Team
Thanks for reaching out!
Great question!
Regression and correlation are closely related but serve different purposes. Correlation measures the strength and direction of a relationship between two variables—temperature and ice cream sales in this case—but it doesn't provide a predictive model. On the other hand, regression allows us to quantify this relationship and make predictions based on it.
By using linear regression, we can establish a mathematical equation that estimates how sales might change with temperature. Of course, you're absolutely right—temperature alone is not a guarantee of sales. Many other factors (such as marketing efforts, consumer preferences, or economic conditions) can influence sales, which is why regression models should always be interpreted with some caution.
In terms of accuracy and reliability, regression results depend on the data quality and the strength of the relationship. Metrics like R-squared and p-values help us evaluate how well the model fits the data and whether the relationship is statistically significant. However, it's important to recognize that regression models simplify reality and should be used as a tool for insight rather than a perfect predictor.
Let me know if you’d like more details on how we assess the reliability of regression models!
Kind regards,
The 365 Team
thank you for the detailed informative answer. yes I would love to gain more knowledge from your experience on the reliability of regression models, cause as far as I know other ML models are prefered.
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