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Over [174]
2 years ago
7

PLEASE NO LINKS DONT WASTE YOUR TIME IM NOT GONNA CLICK. Use the graph of the polynomial function to find the factored form of t

he
related polynomial. Assume it has no constant factor.

Mathematics
1 answer:
USPshnik [31]2 years ago
5 0

Answer:

B.

Step-by-step explanation:

factored form actually contains the x intercepts right in it. Basically whatever sets the whole equation equal to zero.

On the graph you can see that it passes through both positive and negative 4. so (x+4)(x-4) is the answer because plugging in 4 for x will make it zero. Same for plugging in negative 4

Hope this helps!

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X+8=-15 help again sorryyy
suter [353]
X by its self is always 1x

1x+8=-15 you have to get rid of 8 by subtracting on both sides

1x=-23 now get rid of 1 by dividing on both sides

x=-23
8 0
2 years ago
This graph shows a proportional relationship.
Artemon [7]
It a keep on the work
7 0
2 years ago
This table shows the number of marbles in a bag by color. If one marble is randomly selected from the bag, what is the
ratelena [41]

Answer:

p(blue or red) = 0.52 = 52%

Step-by-step explanation:

total number of marbles = 7 + 10 + 14 + 19 = 50

number of blue marbles = 7

number of red marbles = 19

number of blue or red marbles = 7 + 19 = 26

p(blue or red) = 26/50

p(blue or red) = 0.52 = 52%

3 0
3 years ago
Read 2 more answers
Use a scientific calculator to find the logarithm for each number rounded ro four decimal places. Then state characteristic and
anzhelika [568]
Using a scientific calculator:
log 12 = 1.079181246
Rounded to four decimal places:
log 12 = 1.0792
Characteristic: 1; Mantis: 0.0792

Answer: OPtion B. 1.0792; 1; 0.0792
6 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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