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Question 1

Regression

Answer 1

• regression is the mathematical relationship between the

dependent variable and the independent variable.

the dependent variable is the value to be predicted while the independent variable is called the predictor

• the simplest forms of regression assume that the relationship between the independent and dependent variables follows a straight line i.e y=mx+b where b is the slope and ‘a’ is the intercept
• Y is the dependent variable and ‘X’ is the independent variable.
• the ‘m’ determines how much the line rises for each increase in x

Question 2

Dependent Variable

Answer 2

• the dependent variable is the value to be predicted while the independent variable is called the predictor

Question 3

Multiple Linear Regression

Answer 3

• takes multiple independent variables into consideration

Question 4

Strengths of Multiple linear regression

Answer 4

• by far the most common approach for modeling numeric data
• can be adapted to model almost any modeling task
• provides estimates of both the strength and size of the relationship among features and the outcomes

Question 5

Weaknesses

Answer 5

• makes strong assumptions about the data
• the models form must be specified by the user in advance
• does not handle missing data
• only works with numeric features so categorical data requires extra processing
• requires some knowledge of statistics to understand the model