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stich3 [128]
3 years ago
13

Six friends shared 79 trading cards and kept a record of how they did it. What does 13 represent? the answers are- The number of

trading cards each friend ended up with The number of trading cards left after each friend got the same amount The number of trading cards left after each friend got 10 The number of friends sharing trading cards
Mathematics
1 answer:
castortr0y [4]3 years ago
7 0

Answer: The number of trading cards each friend ended up with

Step-by-step explanation:

Given that:

Total Number of trading cards = 79

Number of friends = 6

Number of trading cards each friend got of shared equally :

Total Number of trading cards / number of friends

= 79 / 6

= 13.166

Highest number of cards got by each = 13

Number left : 79 - (13 * 6) = 1

Hence, 13 represents the number of trading cards each friend ended up with

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Vikki [24]

Considering that each student has only one birthday, each input will be related to only one output, hence this relation is a function.

<h3>When does a relation represent a function?</h3>

A relation represents a function when each value of the input is mapped to only one value of the output.

For this problem, we have that:

  • The input is the student's name.
  • The output is the student's birthday.

Each student has only one birthday, hence each input will be related to only one output, hence this relation is a function.

More can be learned about relations and functions at brainly.com/question/12463448

#SPJ1

5 0
1 year ago
The perimeter of a square with sides measuring 5 inches is _______ inches
Rzqust [24]
Each side is 1.25inches I don't know if this answers your question but i hope it helps
4 0
2 years ago
Real Estate One conducted a recent survey of house prices for properties located on the shores of Tawas Bay. Data on 26 recent s
Ivanshal [37]

Answer:

Step-by-step explanation:

Hello!

Given the data for the variables:

Y: Selling price of a house on the shore of Tawas Bay

X₁: Number of bathrooms of a house on the shore of Tawas Bay.

X₂: Square feet of a house on the shore of Tawas Bay.

X₃: Number of bedrooms of a house on the shore of Tawas Bay.

The multiple regression model is Y= α + β₁X₁ + β₂X₂ + β₃X₃ + εi

a. Using software I've entered the raw data and estimated the regression coefficients:

^α= a= -5531.01

Represents the mean selling price of the houses when 0 bathrooms, 0 square feet and 0 bedrooms.

^β₁= b₁= -1386.21

Represents the modification of the mean selling price of the houses when the number of bathrooms increases in one unit and the square feet and number of bedrooms remain unchanged.

^β₂= b₂= 60.28

Represents the modification of the mean selling price of the houses when the square feet increase in one unit and the number of bathrooms and bedrooms remain unchanged.

^ β₃= b₃= 54797.08

Represents the modification of the mean selling price of the houses when the number of bedrooms increase in one unit and the number of bathrooms and square feet of the houses remain unchanged.

^Y= -5531.01 -1386.21X₁ + 60.28X₂ + 54797.08X₃

b)

R²= 0.55

R²Aj= 0.49

The coefficient of determination gives you an idea of how much of the variability of the dependent variable (Y) is due to the explanatory variables. Each time you add another explanatory variable to the regression the coefficient increases regarding of real contribution of the new variable. This could lead to thinking (wrongly) that the new variables are good to explain the dependent variable.  

The adjusted coefficient of determination is a correction made to the raw coefficient of determination to have a more unbiased estimation of the effect the independent variables have over the dependent variable.

⇒ As you can see both coefficient are around 50%, which means that these explanatory variables

c)

The standard error estimate, this is the estimate of the population variance of the errors. In the ANOVA is represented by the Mean Square of the errors (MME)

Se²= MME= 3837640577.01

Se= 61948.6931

d) and f)

For the hypotheses tests for each slope the t- and p-values are:

α: 0.05

β₁: t_{H_0}= \frac{b_1-\beta_1 }{Sb_1} t= -0.06; p-value: 0.9528 ⇒ Do not reject H₀, the test is not significant.

β₂: t_{H_0}= \frac{b_2-\beta_2 }{Sb_2} t= 2.56; p-value: 0.0180 ⇒ Reject H₀, the test is significant.

β₃: t_{H_0}= \frac{b_3-\beta_3 }{Sb_3} t= 2.28; p-value: 0.0326 ⇒ Reject H₀, the test is significant.

e)

H₀: β₁= β₂= β₃

H₁: At least one βi is different from the others ∀ i=1, 2, 3

α: 0.05

F= 9.03

p-value: 0.0004

⇒ Reject H₀, the test is significant.

I hope it helps!

5 0
3 years ago
Kara is training for a 5 kilometer race. On the first day, she runs 0.75 kilometer What percent of the total distance does she r
svetoff [14.1K]

Answer:15 percent

Step-by-step explanation:

(.75) / 5 = 3/20 = 15/100 = 15%

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ivolga24 [154]

Answer:

2a+5b

Step-by-step explanation:

7a-5a =2a

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