Answer:
(a) <em>Linear regression</em> is used to estimate dependent variable which is continuous by using a independent variable set. <em>Logistic regression</em> we predict the dependent variable which is categorical using a set of independent variables.
(b) Finding the relationship between the Number of doors in the house vs the number of openings. Suppose that the number of door is a dependent variable X and the number of openings is an independent variable Y.
Step-by-step explanation:
(a) Linear regression is used to estimate dependent variable which is continuous by using a independent variable set .whereas In the logistic regression we predict the dependent variable which is categorical using a set of independent variables. Linear regression is regression problem solving method while logistic regression is having use for solving the classification problem.
(b) Example: Finding the relationship between the Number of doors in the house vs the number of openings. Suppose that the number of door is a dependent variable X and the number of openings is an independent variable Y.
If I am to predict that increasing or reducing the X will have an effect on the input variable X or by how much we will make a regression to find the variance that define the relationship or strong relationship status between them. I will run the regression on any computing software and check the stats result to measure the relationship and plots.
If you mean 756.04, the answer is 756.0. 4 is closer to 0.
5 Or More, raise the score
4 Or Less, let it rest
Answer:
6x + 5y + 13 = 0.
Step-by-step explanation:
y = 5/6x + 7/6
Gradient = 5/6
Since the line is perpendicular to y = 5/6x + 7/6
then its gradient is -6/5.
Hence its equation is: point (-8,7).
y - 7 = -6/5(x -(-8))
multiplying through by 5 we get;
5y - 35 = -6(x + 8)
5y - 35 = -6x - 48
6x + 5y + 13 = 0
Answer:
b=2A/h-a
Step-by-step explanation:
A=1/2h(a+b)
a+b=A/(1/2h)
a+b=A/(h/2)
a+b=(A/1)(2/h)
a+b=2A/h
b=2A/h-a
