<|im_start|>user
What are the limitations of using linear regression for predictive modeling in real-world applications?<|im_end|>
<|im_start|>assistant
Linear regression is a popular statistical method for predictive modeling, but it has several limitations that can impact its effectiveness in real-world scenarios. One major issue is the assumption of a linear relationship between the independent and dependent variables, which may not hold true in many cases. If the actual relationship is nonlinear, using linear regression can lead to poor predictions and misinterpretation of the data.<|im_end|>

<|im_start|>user
What are the limitations of using linear regression for predictive modeling in real-world applications?<|im_end|>
<|im_start|>assistant
Linear regression is a method used for modeling data, but it has some problems. One issue is that it assumes a straight-line relationship between variables. If the relationship is not straight, the predictions may not be accurate. Also, linear regression has some assumptions about the data, such as normal distribution of errors. If these assumptions are not met, it can lead to wrong results. Additionally, outliers can affect the regression results a lot. So, while linear regression is useful, it's important to be careful and consider other methods if the data is complicated.<|im_end|>

What are the limitations of using linear regression for predictive modeling in real-world applications?