Page 253 - Touhpad Ai

Now you can use this equation to estimate the height of the child at 9 years.
y = 17.17 (9) + 23.24 = 177.77 cm

Types of Linear Regression
There are two types of Linear Regression, which are as follows:
u Simple linear regression: It refers to the utilisation of a single independent variable for forecasting an outcome of a
numerical dependent variable.

y

x
u Multiple linear regression: It demonstrates a connection between two or more independent variables and the
associated variables that are dependent. The variables that are independent can be continuous or categorical.
This kind of regression type allows you to forecast patterns, predict potential outcomes, and forecast the effects of
adjustments.

y

x

Applications of Linear Regression
Linear regression can be used to predict future values based on existing data, identifying trends and relationships between
variables. Linear regression is used in various Artificial Intelligence applications. It has its limitations, but its simplicity,
interpretability, and efficiency often exceed these limitations. Real life applications of linear regression include:
u Prediction of product demand
u Sales forecasting
u Analysing the effect of price change of a service
u Predict the effect of fertilisers on crop yield
u Prediction of revenue through advertisements
u Predicting salary of a person based on number of years of experience

AI REBOOT

1. State the two types of Regression.
2. How many variables are used in linear regression?

3. State the equation of the line of best fit.
4. Why is it called the line of best fit?

5. State two applications of regression.

Data Modelling and Simple Linear Regression 251