Question

short answer questions answer the following short answer questions about the reading. 1. what is linear regression, and what does it help us understand by looking at past events? 2. explain the difference between the independent and dependent variables in linear regression, and provide an example from the reading passage. 3. what is the best - fit line in linear regression, and why is it important to consider outliers before using this method?

Explanation:

1. Linear regression is a statistical method that finds a linear relationship between two variables. By looking at past events, it helps predict future values based on historical data trends.
2. In linear regression, the independent variable is the predictor variable, while the dependent variable is the response variable that is being predicted. For example, if predicting sales (dependent) based on advertising spend (independent), advertising spend is what we can control or manipulate to observe changes in sales.
3. The 'best - fit line' is the line that minimizes the sum of the squared distances between the observed data points and the line. Outliers can greatly affect the position of the best - fit line, so considering them is important to ensure the accuracy and reliability of the regression model.

Answer:

1. Linear regression is a statistical method for finding linear relationships between variables. It helps predict future values from past data trends.
2. Independent variable is the predictor, dependent is the response. E.g., advertising spend (independent) predicting sales (dependent).
3. The best - fit line minimizes squared distances to data points. Outliers can skew it, so they must be considered for model accuracy.