DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST · Question #41
Which of the following statement true with regards to Linear Regression Model?
The correct answer is A. Ordinary Least Square can be used to estimates the parameters in linear model D. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted. Linear regression model are represented using the below equation Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and…
Question
Which of the following statement true with regards to Linear Regression Model?
Options
- AOrdinary Least Square can be used to estimates the parameters in linear model
- BIn Linear model, it tries to find multiple lines which can approximate the relationship between the
- COrdinary Least Square is a sum of the individual distance between each point and the fitted line of
- DOrdinary Least Square is a sum of the squared individual distance between each point and the fitted
How the community answered
(24 responses)- A79% (19)
- B13% (3)
- C8% (2)
Explanation
Linear regression model are represented using the below equation Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1). And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values and the model's predicted values are small and unbiased.
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