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weeeeeb [17]
3 years ago
8

0 Draw the graph of y = 3 - 0.5x

Mathematics
1 answer:
lina2011 [118]3 years ago
8 0

Answer:

Step-by-step explanation:

All you need do is set up a table with three points

x         y

2        3- 0.5*2 = 2

0        3

6        0

Now plot the 3 points. You should get something that looks like the graph I downloaded for you.

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Use the rule ƒ(x) = -2x + 3 to complete the statement. When x = 5, ƒ(x) = _____.
Mazyrski [523]

Answer:

-7

Step-by-step explanation:

To solve this equation, plug your x value in and simplify.

-2x + 3

-2(5) + 3

-10 + 3

-7

3 0
3 years ago
Read 2 more answers
The number of significant figures in 0.01500 is
madreJ [45]
Four significant is an answer 
because if 0 is the extreme right of decimal place it is significant
and if 0 lies between two significant figure than it is significant like 1.020 is 4 sifnificant and 100 is 1 significant figure
8 0
3 years ago
A gas pump fills 2^-2 gallon of gasoline per second. how many gallons does the pump fill in one minute?
astraxan [27]

Answer:

The gas pump filling the gallons in 1 minute will be: 15

Step-by-step explanation:

  • Given that a gas pump fills 2^-2 gallon of gasoline per second.

As there are 60 seconds in 1 minute.

Thus,

Gas pump filling the gallons in 1 minute will be:

60\:\times 2^{-2}

=60\times \frac{1}{2^2}             ∵ a^{-b}=\frac{1}{a^b}

\mathrm{Multiply\:fractions}:\quad \:a\times \frac{b}{c}=\frac{a\:\times \:b}{c}

=\frac{1\times \:60}{2^2}

=\frac{60}{2^2}

=\frac{2^2\times \:3\times \:5}{2^2}

=3\times \:5

=15 gallons in one minute

Therefore, the gas pump filling the gallons in 1 minute will be: 15

6 0
3 years ago
The table shows ordered pairs for a polynomial function, f. What is the degree of f?
Kazeer [188]

Answer:

5th degree

Step-by-step explanation:

To solve this problem/table, you have to find the differences in the f(x) side of the table until the differences between the numbers are the same, and then you have your degree. Look at the image attached for more sense

4 0
3 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
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