Please help !!!!!!!!!

Answer:

Bowling ball

Step-by-step explanation:

A bowling ball has much more mass than all three of the other choices and the amount of kinetic energy is determined based on a object's mass and velocity, but in this case, all 4 balls are traveling at the same velocity. Therefore, we know that the bowling ball has much more mass so therefore it has the most kinetic energy.  

Answer:

The answer is 20 feet

Step-by-step explanation:

We know that her apartment sketch is 6 by 4 inches.

If the 6 inch side is actually 30 feet and 6 goes into 30 5 times, we should multiply 4 by 5 to get proportionate answers.

Therefore her apartment is 20*30 feet

Hope this helps

Answer:

340

Step-by-step explanation

Base=10x10=100

Sides= {(12x10)/2}x4=240

Add sides plus base, 240+100=340

a. The function f(x) = 32/(x+4)³ for x < 0 is not a legitimate pdf

b. The function f(x) does not have a cumulative distribution function (cdf)

From the question, we have the following parameters that can be used in our computation:

f(x) = 32/(x+4)³ for x < 0

The density function f(x) is a legitimate pdf if

∫ f(x) dx = 1

So, we have

[tex]\int\limits^{\infty}_{-\infty} {\frac{32}{(x + 4)^3} \, dx = 1[/tex]

Integrate the function

[tex]-\frac{16}{(x + 4)^2}|\limits^{\infty}_{-\infty} = 1[/tex]

Expand the equation

So, we have

[tex]-\frac{16}{(\infty + 4)^2} + \frac{16}{(-\infty + 4)^2} = 1[/tex]

Evaluate the exponents

-16/∝ + 16/∝ = 1

So, we have

0 + 0 = 1

Evaluate

0 = 1

The above equation is false

This means that f(x) is not a legitimate pdf.

In (a), we proved that

f(x) is not a legitimate pdf.

This means that it does not have a cumulative distribution function (cdf)

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The answer is 23v

steps-

remove the parentheses

2x7v+9v

multiply the number

2x7=14+19v

add them

14v+19v=23v

Answer:

answer is on this page

Step-by-step explanation:

https://www.mathpapa.com/algebra-calculator.html

The incorrect statement is "the intercept in the multiple regression model should be excluded if one explanatory variable has negative values".

The intercept, also known as the constant term, represents the value of the dependent variable when all independent variables are equal to zero.

It is a necessary component of the regression equation regardless of the values of the independent variables. Excluding the intercept when one explanatory variable has negative values may result in biased and unreliable estimates.

The intercept does not determine the height of the regression line, but rather its slope and position. The slope represents the change in the dependent variable for a unit change in an independent variable, while the intercept represents the starting point of the regression line on the y-axis.

The intercept should not be excluded because the population regression function does not go through the origin. While some models may have a theoretical justification for excluding the intercept, such as when all independent variables are proportions or percentages that sum to one, it is generally advisable to include it.

The statistical significance of the intercept depends on its estimated value and standard error, as well as the chosen level of significance. A common level of significance is 0.05, which corresponds to a critical value of 1.96 for a two-tailed test.

If the absolute value of the t-statistic for the intercept is larger than 1.96, we can reject the null hypothesis that it is equal to zero at a 5% level of significance.

In summary, excluding the intercept in a multiple regression model based on negative values of an explanatory variable is not recommended, and its significance should be assessed based on its estimated value and standard error.

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The probability distribution of the sample mean is approximately normal, with mean of 24.4 points and standard deviation of 1.175 points.

By the Central Limit Theorem, the sampling distribution of sample means of size n has standard deviation given by the equation presented as follows: [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].

The mean is the same as the population mean, of 24.4 points, while the shape is approximately normal.

The standard error is given as follows:

[tex]s = \frac{4.7}{\sqrt{16}} = 1.175[/tex]

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Answer:

(A) 22

(B) 12

Step-by-step explanation:

Jada makes sparkling juice by mixing 3 cups of sparkling water and 5 cups of Apple juice

(A) Jada uses 30 cups of Apple juice,the amount of sparkling water needed is

3+5

= 8

30-8

= 22

(B) 9+3

= 12

Answer:

1/4

Step-by-step explanation:

Try it if you don't believe me :)

The solution of the linear equation n x (3/4) = 3/16 will be 1/4.

In other words, the collection of all feasible values for the parameters that satisfy the specified mathematical equation is the convenient storage of the bunch of equations.

The equation is given below.

n x (3/4) = 3/16

The degree of the equation is one. Then the equation will be a linear equation.

Simplify the equation, then the value of the variable 'n' is calculated as,

n x (3/4) = 3/16

n = (3/16) x (4/3)

n = 4/16

n = 1/4

The solution of the linear equation n x (3/4) = 3/16 will be 1/4.

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Answer:

(-4-y) = -6(7 - x)

Step-by-step explanation:

To test whether one type of high school better equips students to attain high academic honors at a college, we can compare the observed proportions of Honors Graduates from different high school types to the expected proportions based on the overall distribution. Using a chi-squared test, the hypothesis test assesses whether the observed differences are statistically significant.

To test the hypothesis, we set up the null hypothesis (H0) that the type of high school attended does not affect the likelihood of attaining high academic honors. The alternative hypothesis (Ha) states that there is a significant association between high school type and academic honors.
Using the given information, we calculate the expected number of Honors Graduates for each high school type based on the overall distribution of high school attendance. Then, we compare the observed and expected frequencies using a chi-squared test. The test evaluates the difference between the observed and expected counts to determine if it is statistically significant.If the chi-squared test results in a p-value below a predetermined significance level (e.g., 0.05), we reject the null hypothesis, suggesting that there is evidence to support the alternative hypothesis.

This would indicate that one type of high school may better equip students to attain high academic honors at the college. Conversely, if the p-value is above the significance level, we fail to reject the null hypothesis, indicating that there is insufficient evidence to conclude a significant association between high school type and academic honors.
To draw a conclusion, we would need to perform the chi-squared test and compare the calculated p-value to the chosen significance level.

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The area of the polygon with the vertices (-4, 5), (-1, 5), (4, -3), and (-4, -3) is 12 square units.

To calculate the area of the polygon, we can use the shoelace formula, also known as Gauss's area formula or the surveyor's formula. The formula involves writing the x-coordinates and y-coordinates of the vertices in a specific order and performing a series of calculations.

1. We write the x-coordinates of the vertices in one row, repeating the first coordinate at the end: -4, -1, 4, -4.

2. We write the y-coordinates of the vertices in the next row, in the same order: 5, 5, -3, -3.

3. Next, we multiply each pair of adjacent x and y coordinates and add them together in a counterclockwise direction.

4. Then, we subtract the sum of the products of the y-coordinates and the x-coordinates in a counterclockwise direction.

5. Taking the absolute value of this result, we divide it by 2 to obtain the area.

Applying the shoelace formula:

Area = |((-4*5) + (-1*-3) + (4*-3) + (-4*5)) - (5*-1 + 5*4 + -3*-4 + -3*-4)| / 2

    = |-49 - (-25)| / 2

    = |-24| / 2

    = 12 / 2

    = 12.

Therefore, the area of the polygon with the given vertices is 12 square units.

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Answer:

Option B

Step-by-step explanation:

[tex](q + p)q = (s + r)s[/tex]

Lets open the brackets of L.H.S.

[tex]=> q^2 + pq = (s+ r)s[/tex]

Lets substract q² from both the sides.

[tex]=> q^2 + pq - q^2 = (s + r)s - q^2[/tex]

[tex]=> pq = (s + r)s - q^2[/tex]

Lets divide both the sides by q.

[tex]=> \frac{pq}{q} = \frac{(s + r)s - q^2}{q}[/tex]

[tex]=> p = \frac{(s + r)s }{q} - \frac{q^2}{q} = \frac{(s + r)s}{q} - q[/tex]

Answer:

4/5

Step-by-step explanation:

50-10 = 40

40/50 = 4/5

the answer is 40/50

Step-by-step explanation:

50-10=40

it's easy

Answer:

72 hours

Step-by-step explanation:

Answer: 34 hours

Step-by-step explanation: 68 divided by 34 is 2 and thats the rate the temperature is dropping.

Answer:

[tex]\frac{dy}{dx}=-(8t+1)e^{9t}\\\frac{d^{2}y}{dx^{2}}=(72t+17)e^{10t}[/tex]

Step-by-step explanation:

The explanation is attached below.

dy/dx = -te^(9t) (1 + 8t)

d²y/dx² = e^(8t) (16t + 1)

Firstly, we will find the value of dx/dt using the Chain rule as follows: dx/dt = - e^(-t)Then, the value of dy/dt can be calculated as

dy/dt = (d/dt) [t(e^(8t))]   [Using product rule]= te^(8t) + t * 8e^(8t)  

[Using the product rule again]= te^(8t) + 8te^(8t)  

[Factoring out t]= te^(8t) (1 + 8t)So, we have obtained the first derivative of y w.r.t t.

Now, let's find the second derivative of y w.r.t t.d²y/dt² = (d/dt) [te^(8t) (1 + 8t)]  

[Using the product rule]= e^(8t)(1 + 8t) + t * 8e^(8t)

  [Using the product rule again]= e^(8t)(1 + 8t) + 8te^(8t)  

[Factoring out 8e^(8t)]= e^(8t) (1 + 8t + 8t)

 [Factorizing]= e^(8t) (16t + 1)

So, the value of the derivative dy/dx is:dy/dx = (dy/dt) / (dx/dt) = te^(8t) (1 + 8t) / - e^(-t) = -te^(9t) (1 + 8t)

Thus, the values of derivatives as functions of t are:

dy/dx = -te^(9t) (1 + 8t)

d²y/dx² = e^(8t) (16t + 1)

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To determine the area of a region, we need to know the specific shape and dimensions of the region in question. The process for finding the area varies depending on the shape. P(Y - X < 1) = Area of region below y - x = 1 within the given constraints= 1/6 sq. units.

Given, f(x, y) = x + y, where 0 ≤ x ≤ 1 and 0 ≤ y ≤ 2 We are to find P(Y - X < 1)

Now, Y - X < 1 is the equation of the line in the XY plane. We have to find the area of the region below this line within the given constraints. To find this, we have to integrate over the region below the line,

i.e.,y - x = 1 implies x = y - 1∫∫R (x + y)dA = ∫₀² ∫₀^(y-1) (x + y) dx dy= ∫₀² [(y²/2) - y + (1/2)] dy= 1/6 sq. units.

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Answer:

Step-by-step explanation:

Given function f(x, y) = x + y, where 0 ≤ x ≤ 1 and 0 ≤ y ≤ 2. The probability of P(Y - X < 1) is 1/4.

Now we have to find P(Y - X < 1).

The probability of (Y - X < 1) is given by:

P(Y - X < 1) = Area of the region {(x, y): y - x < 1} / Area of the region R.

Here R is the region bounded by 0 ≤ x ≤ 1 and 0 ≤ y ≤ 2.

We know that the function f(x, y) = x + y is a plane with slope 1, and intersects the y-axis at (0, 0) and the x-axis at (0, 0).

Now the line y - x = 1 intersects the plane at x = 0 and y = 1, so the intersection point is (0, 1).

Now the area of the region {(x, y): y - x < 1} can be found by finding the area of the triangle ABC as shown below: [tex]A=\frac{1}{2}Base×Height[/tex], Where base BC is of length 1 and height AB is of length 2 - 1 = 1.

Therefore, the area of the triangle ABC is:

Area of the triangle ABC = 1/2 × 1 × 1

= 1/2

The area of the region R can be found by finding the area of the rectangle with sides of length 1 and 2.

Therefore, the area of R is:

Area of R = 1 × 2

= 2

P(Y - X < 1) = Area of the region {(x, y): y - x < 1} / Area of the region RP(Y - X < 1)

= (1/2) / 2P(Y - X < 1)

= 1/4

Therefore, P(Y - X < 1) = 1/4.

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Answer:

(1) It is given that line AB is tangent to the circle at A.

∴ ∠CAB = 90º     (Tangent at any point of a circle is perpendicular to the radius throught the point of contact)

Thus, the measure of ∠CAB is 90º.

(2) Distance of point C from AB = 6 cm    (Radius of the circle)

(3) ∆ABC is a right triangle.

CA = 6 cm and AB = 6 cm

Using Pythagoras theorem, we have

BC2=AB2+CA2⇒BC=

√

62+62

⇒BC=6

√

2

 cm

Thus, d(B, C) = 6

√

2

cm

(4) In right ∆ABC, AB = CA = 6 cm

∴ ∠ACB = ∠ABC      (Equal sides have equal angles opposite to them)

Also, ∠ACB + ∠ABC = 90º            (Using angle sum property of triangle)

∴ 2∠ABC = 90º

⇒ ∠ABC =

90°

2

= 45º

Thus, the measure of ∠ABC is 45º.

Step-by-step explanation:

Answer:

The change in delta x would be 0 in the second option because the change is exactly the same.

Answer:

x = [tex]25.8^{o}[/tex] and y = [tex]51.4^{o}[/tex]

Step-by-step explanation:

Known that he sum of angles on a straight line is [tex]180^{o}[/tex]. From the diagram;

2y + x + y = [tex]180^{o}[/tex]

x + 3y = [tex]180^{o}[/tex] ................ 1

Also,

2x + x + 2y = [tex]180^{o}[/tex]

3x + 2y = [tex]180^{o}[/tex] .............. 2

Using the elimination method, multiply equation 1 by 3 and equation 2 by 1

3x + 9y = 540 ............ 3

3x + 2y = [tex]180^{o}[/tex] ............. 4

7y = 360

y = [tex]\frac{360}{7}[/tex]

y = [tex]51.4^{o}[/tex]

substitute the value of y in equation 1,

x + 3y = [tex]180^{o}[/tex]

x + 3(51.4) = [tex]180^{o}[/tex]

x + 154.2 = [tex]180^{o}[/tex]

x = [tex]180^{o}[/tex] - 154.2

x = [tex]25.8^{o}[/tex]

Thus, x = [tex]25.8^{o}[/tex] and y = [tex]51.4^{o}[/tex]

Answer:

3 units

Step-by-step explanation:

-1, 0, 1 ,2

( 1, 2 ,3)

Give the other person Brainliest :)

To test the claim that the incidence of malaria is lower for infants using bednets, a hypothesis test is conducted using the given data. The test statistic is calculated and compared to the critical value at a significance level of 0.01.

The null hypothesis (H0) assumes that the incidence of malaria is the same for infants using bednets and those not using bednets. The alternative hypothesis (Ha) suggests that the incidence of malaria is lower for infants using bednets.

To conduct the hypothesis test, the test statistic can be calculated using the formula:

test statistic (p) = (p1 - p2) / sqrt[(p(1 - p) / n1) + (p(1 - p) / n2)]

Where:

p1 = proportion of infants using bednets who developed malaria

p2 = proportion of infants not using bednets who developed malaria

n1 = number of infants using bednets

n2 = number of infants not using bednets

Using the given data, the test statistic is computed as:

p = (10/320) - (24/279) / sqrt[((10+24)/(320+279)) * (1 - ((10+24)/(320+279))) * ((1/320) + (1/279))]

By comparing the calculated test statistic with the critical value at a significance level of 0.01, a conclusion can be made regarding the effectiveness of bednets in reducing malaria incidence.

If the test statistic falls within the critical region (beyond the critical value), the null hypothesis is rejected in favor of the alternative hypothesis, indicating that the bednets appear to be effective in reducing malaria incidence. Conversely, if the test statistic does not fall within the critical region, there is not enough evidence to reject the null hypothesis, suggesting that the bednets may not be effective in reducing malaria incidence.

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Answer:

8x12 and 2x2x53

Step-by-step explanation: