Data Principles week 2 part 2

What does the ordinary least squares regression aim to minimize?

• The sum of the absolute error of each data point

• The sum of the squared error of each data point

• The product of the squared error of each data point

• The maximum error of any data point

In the context of ordinary least squares regression, what is the goal when there are multiple input variables?

• To maximize the coefficients of each input variable

• To minimize the coefficients of each input variable

• To estimate the parameter value (coefficients/weights) of each input variable

• To eliminate the need for input variables

According to the text, how is the optimization problem of ordinary least squares regression typically solved in practice?

• By using a calculator

• By doing it manually

• By using data science software packages

• By ignoring the optimization problem

What assumption does Linear Regression make about the relationships between the input and output variables?

• The relationships are non-linear.

• The relationships are linear.

• The relationships are exponential.

• The relationships are logarithmic.

What is a necessary step when preparing data for Linear Regression if the data is non-linear?

• Apply a square root transformation.

• Apply a log transform.

• No transformation is needed.

• Apply an exponential transformation.

What should be done to prepare data for Linear Regression to ensure it is clean?

• Add more noise and outliers.

• Apply data cleaning techniques to remove noise and outliers.

• Ignore the noise and outliers.

• Increase the number of variables.

What principle is applied to remove collinearity in the context of preparing data for Linear Regression?

• Newton's Third Law

• Occam's Razor

• Murphy's Law

• Pareto Principle

According to Occam's Razor, how should a model be designed for an event?

• With the most complex explanation.

• With the least correlated variables.

• With the simplest explanation.

• With the maximum number of variables.

What is the relationship of a linear curve?

• Non-linear

• Straight line

• Exponential

• Logarithmic

What can be used to solve a non-linear problem as a linear one?

• Linear regression

• Non-linear transformation

• Exponential smoothing

• Logarithmic reduction