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aleksley [76]
3 years ago
5

Diego is making a large mural. He draws a hexagon with a perimeter of 10.5 meters. Each side of the hexagon is the same length.

Mathematics
1 answer:
miss Akunina [59]3 years ago
7 0

Answer:

Part A: 6a = 10.5 and a = 1.75 meters.

Part B: $3.87.

Step-by-step explanation:

Part A: Diego draws a hexagon with a perimeter of 10.5 meters and each side of the hexagon is the same length.

Now, the hexagon has 6 sides and if each side is of length 'a' meters, then the equation that models the situation is  

6a = 10.5 ......... (1) (Answer)

⇒ a = 1.75 meters. (Answer)

Part B:

The total cost of supplies to paint the mural is $38.70. Diego and 9 friends divide the cost equally.

Hence, the total cost will be divided into 10 persons.

Therefore, each person will get \frac{38.70}{10} = 3.87 dollars. (Answer)

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Answer:

30%

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The answer is 30%

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Perfect Linear relationship between two or more independent variables is a violation of .... a. MLR.1 - Linearity in parameters
nexus9112 [7]

Answer:

b. MLR.3 - No perfect collinearity assumption

Step-by-step explanation:

There is an assumption that nothing in the error term should correlate with the explanatory variable (x) of interest and outcome variable of (y). It does not allow any linear relation between two or more variables. If there is a relation between the variable it the the violation of this assumption.  

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3 years ago
given examples of relations that have the following properties 1) relexive in some set A and symmetric but not transitive 2) equ
rodikova [14]

Answer: 1) R = {(a, a), (а,b), (b, a), (b, b), (с, с), (b, с), (с, b)}.

It is clearly not transitive since (a, b) ∈ R and (b, c) ∈ R whilst (a, c) ¢ R. On the other hand, it is reflexive since (x, x) ∈ R for all cases of x: x = a, x = b, and x = c. Likewise, it is symmetric since (а, b) ∈ R and (b, а) ∈ R and (b, с) ∈ R and (c, b) ∈ R.

2) Let S=Z and define R = {(x,y) |x and y have the same parity}

i.e., x and y are either both even or both odd.

The parity relation is an equivalence relation.

a. For any x ∈ Z, x has the same parity as itself, so (x,x) ∈ R.

b. If (x,y) ∈ R, x and y have the same parity, so (y,x) ∈ R.

c. If (x.y) ∈ R, and (y,z) ∈ R, then x and z have the same parity as y, so they have the same parity as each other (if y is odd, both x and z are odd; if y is even, both x and z are even), thus (x,z)∈ R.

3) A reflexive relation is a serial relation but the converse is not true. So, for number 3, a relation that is reflexive but not transitive would also be serial but not transitive, so the relation provided in (1) satisfies this condition.

Step-by-step explanation:

1) By definition,

a) R, a relation in a set X, is reflexive if and only if ∀x∈X, xRx ---> xRx.

That is, x works at the same place of x.

b) R is symmetric if and only if ∀x,y ∈ X, xRy ---> yRx

That is if x works at the same place y, then y works at the same place for x.

c) R is transitive if and only if ∀x,y,z ∈ X, xRy∧yRz ---> xRz

That is, if x works at the same place for y and y works at the same place for z, then x works at the same place for z.

2) An equivalence relation on a set S, is a relation on S which is reflexive, symmetric and transitive.

3) A reflexive relation is a serial relation but the converse is not true. So, for number 3, a relation that is reflexive but not transitive would also be serial and not transitive.

QED!

6 0
3 years ago
The linear regression method seeks to predict values of a(n) variable based on values of a(n) variable.
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The linear regression method seeks to predict values of a(n) dependent variable based on values of a(n)  independent variable.

According to the statement

we have to explain the linear regression method and explain the way by which this method is used to predict the values.

So, For this purpose we know that the

Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship.

And

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.

from these definitions it is clear that the there is a presence of two types of variables which are dependent and independent variables.

So, The linear regression method seeks to predict values of a(n) dependent variable based on values of a(n)  independent variable.

Learn more about Linear regression here brainly.com/question/25987747

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