The document discusses different machine learning models including linear regression, multiple linear regression, and logistic regression. Linear regression is used to model relationships between variables to predict a continuous dependent variable. Multiple linear regression extends this to include multiple independent variables. Logistic regression can be used for classification problems to predict a binary dependent variable. It calculates the probability of an event occurring versus not occurring based on independent variable values.

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y = b0 + b1*x1 + … + bn*xn
Linear Regression:
y = b0 + b1*x
- Simple:
- Multiple:

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y = b0 + b1*x??
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y = b0 + b1*x
1 + e-y
1
p =
ln ( ) = b0 + b1*x1 – p
p

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ln ( ) = b0 + b1*x1 – p
p
y (Actual DV)
X
p̂ (Probability)
p_hat

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X
p̂ (Probability)
20 30 40 50
p̂ =0.7%
p̂ =23%
p̂ =85%
p̂ =99.4%
p_hat

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X
p̂ (Probability)y (Actual DV)
ŷ (Predicted DV)
0.5
ŷ = 0 ŷ = 0
ŷ = 1 ŷ = 1
1
Fin.