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.

$655.61 which is option c

step-by-step explanation:

price of house =   $146,000

initial rate = 3.5%

mortgaged paid in this = 3.5 % of $146,000

                                        =

                                        =$5110

the total years would be = 7

total mortgaged paid = 7 * $5110

                                      =$35770

remaining money = $146000-35770

                                =$110230

new mortgaged payment = 5 % of 110230

                                            =  

                                            =$655.61

the equation is not included but the answer is (1.50, 2.00, 2.75)

step-by-step explanation: