<span>The product of any whole number factor multiplied by 100 will always have two zeros on the end, or a zero as the digit in both the tens and units column. For example, 1 x 100 is 100, 46 x 100 is 4600 and 7258 x 100 is 725,800.</span>
A + s = 480......s = 480 - a
8a + 5s = 2892
8a + 5(480 - a) = 2892
8a + 2400 - 5a = 2892
8a - 5a = 2892 - 2400
3a = 492
a = 492/3
a = 164 <=== there were 164 adult tickets sold
Using the z-distribution, it is found that the 95% confidence interval for the proportion of sales that occured in December is (0.1648, 0.2948).
<h3>What is a confidence interval of proportions?</h3>
A confidence interval of proportions is given by:
In which:
is the sample proportion.
In this problem, we have a 95% confidence level, hence
, z is the value of Z that has a p-value of 
, so the critical value is z = 1.96.
The sample size and the estimate are given by:
Hence:
The 95% confidence interval for the proportion of sales that occured in December is (0.1648, 0.2948).
More can be learned about the z-distribution at brainly.com/question/25890103
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.
16 units just count the tiny lines in the middle by 2
