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aksik [14]
3 years ago
12

A linear regression was conducted on the number of cigarettes a person smokes in a day versus the number of times they cough in

a day. Suppose the number of cigarettes a person smokes in a day is our x variable and the number of times they cough in a day is the y variable.
In linear regression, the y variable is referred to as the:

a) response variable.
b) correlation coefficient.
c) explanatory variable.
d) true variable.
Mathematics
1 answer:
makvit [3.9K]3 years ago
8 0

Answer:

a) response variable

Step-by-step explanation:

The response variable is the variable that depends on other variables such as the independent or explanatory variable, it is the variable that is being tested. The response variable is also called the dependent variable.

In linear regression, the y variable is referred to as the response variable while the x variable is referred to as the explanatory or independent variable.

In this case the y variable is "the number of times they cough in a day" and this variation depends on the x variable "number of cigarettes a person smokes in a day".

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WILL MARK BRAINLIEST!!! A contractor has 34 meters of fencing that he is going to use as the perimeter of a rectangular garden.
snow_tiger [21]

Given

Shape of garden: Rectangle

Perimeter: 34 meters

Area: 72 square meters

Length of one side = x

Required

Find the dimension of the rectangle

Let the length of the other side of the rectangle be represented with y.

We start by writing out the perimeter of a rectangle

Perimeter, P = 2(L + B)

Where P is the perimeter; L and B are the length and width of the sides of the rectangle.

In this case L and B are represented by x and y

So,

P = 2(x + y)

By substituting 34 for P

34 = 2(x + y)

Multiply both sides by ½

½ * 34 = ½ * 2(x + y)

17 = x + y

Make y the subject of formula

y = 17 - x

Recall that the sides of the rectangle are represented by x and y.

Hence, the area of the rectangle is as follows.

Area = x * y

Substitute 72 for Area and 17 - x for y

72 = x(17 - x) --- Open bracket

72 = 17x - x² ---- Reorder

x² - 17x + 72 = 0

Now, we have a quadratic equation

Solving by factorisation

x² - 8x - 9x + 72 = 0

x(x - 8) - 9(x - 8) = 0

(x - 8)(x - 9) = 0

x - 8 = 0 or x - 9 = 0

x = 8 or x = 9

Recall that

y = 17 - x.

When x = 8

y = 17 - 8

y = 9

When x = 9

y = 17 - 9

y = 8

Writing out the result, we have

x = 8 , y = 9

Or

x = 9, y = 8

Hence, the dimension of the rectangle is 8 metres by 9 metres

7 0
3 years ago
How do I show the relationship between the input and the output
alexgriva [62]

Answer:

Types of Relationships between the Input and Output

The scatter plot can be a useful tool in understanding the type of relationship that exist between the inputs (X’s) and the outputs (Y’s)

Step-by-step explanation:

1. No Relationship: The scatter plot can give an obvious suggestion if the inputs and outputs on the graph are not related. The points will be scattered throughout the graph with no particular pattern. For no relationship to exist, points have to be completely diffused. If some points are in concentration, then maybe a relationship does exist and our analysis has not been able to uncover it.

2. Linear and Non-Linear: A linear correlation exists when all the points are plotted close together. They form a distinct line. On the other hand points could be close together but they could form a relationship which has curves in it. The nature of the relationship has wide ranging implications.

3. Positive and Negative: A positive relationship between the inputs and the outputs is one wherein more of one input leads to more of an output. This is also known as a direct relationship.

On the other hand a negative relationship is one where more of one input leads to less of another output. This is also known as an inverse relationship.

4. Strong and Weak: The strength of the correlation is tested by how closely the data fits the shape. For instance if all the points are scattered very close together to form a very visible line then the relationship is strongly linear. On the other hand, if the relationship does not so obviously fit the shape then the relationship is weak.

I don't know if this was exactly what you were looking for; hope it is! :)

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