Answer:
No idea
Step-by-step explanation:
Answer:
1.5 white and 2 blue is an equivalent ratio
Step-by-step explanation:
Answer:
3 grams
Step-by-step explanation:
We are going to take the mass of a bunch of little strips below the triangle "roof." To do this, we must figure out what formula for the mass we'll use, in this case, we'll use:
Mass of strip = denisty * area = (1+x)*y*deltax grams
now, because the "roof" of the triangle contains two different integrals (it completely changes direction), we will use TWO integrals!
**pretend ∈ is the sum symbol
Mass of left part = lim x->0 ∈ (1+x)*y*deltax = inegral -1 to 0 of (1+x)*3*(x+1) = 3 * integral -1 to 0 of (x^2 + 2x + 1) = 3 * 1/3 = 1
Mass of left part = lim x->0 ∈ (1+x)*y*deltax = inegral 0 to 1 of (1+x)*3*(-x+1) = 3 * integral 0 to 1 of (-x^2 + 1) = 3 * 2/3 = 2
Total mass = mass left + mass right = 1 + 2 = 3 grams
Answer:
a,d,e and f.
Step-by-step explanation:
hope this helps!
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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.
