Answer:
Help me on my question please
Step-by-step explanation:
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.
Answer:
n^6 is the answer

![\frac{1}{\sqrt[4]{n} } * n^{\frac{25}{4} } = \frac{n^{\frac{25}{4} } }{n^{\frac{1}{4} } } = n^{\frac{25}{4} } - n^{\frac{1}{4} } = n^{6}](https://tex.z-dn.net/?f=%5Cfrac%7B1%7D%7B%5Csqrt%5B4%5D%7Bn%7D%20%7D%20%2A%20n%5E%7B%5Cfrac%7B25%7D%7B4%7D%20%7D%20%3D%20%5Cfrac%7Bn%5E%7B%5Cfrac%7B25%7D%7B4%7D%20%7D%20%7D%7Bn%5E%7B%5Cfrac%7B1%7D%7B4%7D%20%7D%20%7D%20%20%3D%20n%5E%7B%5Cfrac%7B25%7D%7B4%7D%20%7D%20-%20n%5E%7B%5Cfrac%7B1%7D%7B4%7D%20%7D%20%3D%20n%5E%7B6%7D)
Step-by-step explanation:
Answer:
1) test is one tail hypothesis test.
2) 110 sampled customers must have favored Coke.
3) at 5% significance, We cannot conclude that the proportion of customers who prefer Coca-Cola exceeds 50%.
4) at 1% significance level, the conclusion would not change.
Step-by-step explanation:
1) Let p be the proportion of customers who prefer Coke to other brands
: p=0.50
: p>0.50
Since the alternative hypothesis claims p <em>more than</em> 0.50, this test is one tail hypothesis test.
2) Out of a random sample of 200 consumers, 55% favored Coca-Cola over other brands. Thus 200 × 0.55 = 110 sampled customers must have favored Coke.
3) at 5% significance level, p-value =0.07761 >0.05, therefore we fail to reject the null hypothesis. We cannot conclude that the proportion of customers who prefer Coca-Cola exceeds 50%.
4) at 1% significance level, p-value =0.07761 >0.01, thus the conclusion does not change
Answer: y=-32
Step-by-step explanation:
1. Simplify:
-6y-90=-3y+6
2. Add like terms:
-96=3y
=> y=-32
Have a nice day! :)