Microsoft DP-100 Designing and Implementing a Data Science Solution on Azure 12 31Oct2024 - Practice Quiz

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Question 1 of 23

A data scientist is tasked with predicting the sales of a new product based on various features such as advertising spend, price, and seasonality. They decide to use a linear regression model for this purpose. After fitting the model, they notice that the R-squared value is 0.85, indicating a strong relationship between the predictors and the response variable. However, they also observe that the residuals show a pattern when plotted against the predicted values. What does this indicate about the model, and what should the data scientist consider doing next?

The model may be suffering from non-linearity, suggesting the need for a more complex model or transformation of the predictors.

The high R-squared value confirms that the model is perfect and requires no further adjustments.

The residuals indicate that the model is overfitting the training data, necessitating a reduction in the number of predictors.

The presence of a pattern in the residuals suggests that the model is correctly specified and no changes are needed.

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