Answer:
11 degrees.
Step-by-step explanation:
-2 + 13 = 11 Good luck man! :)
Answer:
Part A:
Part B:
Step-by-step explanation:
Part A:
The number of rivets=22 rivets
Probability that no rivet is defective= (1-p)^22
The probability that at least one rivet is defective=1-(1-p)^22
For 19% of all seams need reworking, probability that a rivet is defective is given by:
![(1-p)^{22}=0.81\\p=1-\sqrt[22]{0.81} \\p=0.0095](https://tex.z-dn.net/?f=%281-p%29%5E%7B22%7D%3D0.81%5C%5Cp%3D1-%5Csqrt%5B22%5D%7B0.81%7D%20%5C%5Cp%3D0.0095)
Part B:
For 9% of all seams need reworking, probability of a defective rivet is:
![1-(1-p)^{22}=0.09\\p=1-\sqrt[22]{0.91} \\p=0.0043](https://tex.z-dn.net/?f=1-%281-p%29%5E%7B22%7D%3D0.09%5C%5Cp%3D1-%5Csqrt%5B22%5D%7B0.91%7D%20%5C%5Cp%3D0.0043)
Answer:
x= 4
Step-by-step explanation:
40x-20=100+10x
Subtract 10x and add 20 both sides:
30x= 120
x=4
there are 16 ounces in a pound so 16x7=112 +2= 114
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.
