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IrinaVladis [17]
3 years ago
10

Mercury is about 57,000,000 from the sun,whereas neptune is about 4.5 x 10 to the 9th power km from the sun. What is the differe

nce between mercury and neptune's distance from the sun
Mathematics
1 answer:
slavikrds [6]3 years ago
7 0

Answer:

4,443,000,000 or 4.443 * 10^9 km.

Step-by-step explanation:

Neptune's distance from the sun = 4.5 * 10^9

=  4,500,000,000 km.

The required difference is

4,500,000,000 - 57,000,000

= 4,443,000,000  or 4.443 * 10^9.

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Breyers is a major producer of ice cream and would like to test if the average American consumes more than 17 ounces of ice crea
asambeis [7]

Answer:

The conclusion for this hypothesis test would be that the average American consumes less than or equal to 17 ounces of ice cream per month.

Step-by-step explanation:

We are given that Breyers is a major producer of ice cream and would like to test if the average American consumes more than 17 ounces of ice cream per month.

A random sample of 25 Americans was found to consume an average of 19 ounces of ice cream last month. The standard deviation for this sample was 5 ounces.

<u><em>Let </em></u>\mu<u><em> = average ounces of ice cream consumed by American per month</em></u>

So, Null Hypothesis, H_0 : \mu \leq 17 ounces     {means that the average American consumes less than or equal to 17 ounces of ice cream per month}

Alternate Hypothesis, H_A : \mu > 17 ounces    {means that the average American consumes more than 17 ounces of ice cream per month}

The test statistics that will be used here is <u>One-sample t test statistics</u> as we don't know about population standard deviation;

                                 T.S.  = \frac{\bar X-\mu}{\frac{s}{\sqrt{n} } }  ~ t_n_-_1

where, \bar X = sample average = 19 ounces

             s = sample standard deviation = 5 ounces

             n = sample of Americans = 25

So, <u><em>test statistics</em></u>  =  \frac{19-17}{\frac{5}{\sqrt{25} } }  ~ t_2_4

                               =  2

The value of the test statistics is 2.

<em>Now at 0.025 significance level, the </em><u><em>t table gives critical value of 2.06 at 24 degree of freedom for right-tailed test</em></u><em>. Since our test statistics is less than the critical values of t, so we have insufficient evidence to reject our null hypothesis as it will not fall in the rejection region due to </em><u><em>which we fail to reject our null hypothesis</em></u><em>.</em>

Therefore, we conclude that the the average American consumes less than or equal to 17 ounces of ice cream per month.

8 0
4 years ago
Which type of line of symmetry does the figure have? vertical horizontal diagonal none
Natali [406]

your answer would be that the sematry would be vertical semmetry

3 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
An artist wants to frame a square painting with an area of 400 square inches . she wants
Artemon [7]
(a) x^{2} = 400
(b) \sqrt{x^2} = \sqrt{400}
x=20,-20
(c) Only the positive solution makes sense because you cannot have a negative side length.
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6 0
3 years ago
Arrange whole number smallest to largest on a number line
snow_lady [41]
What are the numbers
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3 years ago
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