A linear regression analysis uses two distinct types of data. The first are variables that are at least nominal level. a) true
b) false

The answer is

A. True

Step-by-step explanation:

In linear regression, Linear models make a prediction using a linear function of the input features, with one being

For regression, the general prediction formula for a linear model looks as follows:

ŷ = w[0] * x[0] + w[1] * x[1] + ... + w[p] * x[p] + b

Here, x[0] to x[p] denotes the features (in this example, the number of features is p)

of a single data point, w and b are parameters of the model that are learned, and ŷ is

the prediction the model makes. For a dataset with a single feature, this is

ŷ = w[0] * x[0] + b

which you might remember from high school mathematics as the equation for a line.

Here, w[0] is the slope and b is the y-axis offset. For more features, w contains the

slopes along each feature axis. Alternatively, you can think of the predicted response

as being a weighted sum of the input features, with weights (which can be negative)

given by the entries of w.

answer: evaluate the objective function at each vertex to determine which - and -values, if any, maximize or minimize the function. example: find the minimum value and maximum value of the objective function f ( x , y ) = 4 x + 5 y , subject to the following constraints

i dont know sorry

step-by-step explanation: