Combine Rationa25 points do all pls

Value of p is 0.5.

Given

Mean of a binomial distribution np = 12

Sample n = 60

The binomial distribution is the discrete probability distribution that gives only two possible results in an experiment, either success or failure.

To calculate the mean of a binomial distribution B(n, p) we need to multiply the number of trials n by the probability of successes p,

mean = n × p .

Value of p:

np = 12

(60) p = 12

p = 12/60

p = 1/2

p = 0.5

Value of p is 0.5.

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Answer:

It should be the third bubble, because all integers are whole numbers

Answer:

c.

Step-by-step explanation:

Answer: y=-(3/4)x-1/2

Step-by-step explanation: To be in standard form the equation or the line must look like y=mx+b

m=the gradient or slope of the line and can take on both positive and negative values and can also be in fraction form

b= where the graph cuts the y axis it can also take on positive and negative values and can also be in fraction form

In this case m=-3/4 and b=-1/2

Answer: 3.6

Step-by-step explanation:

Using pythagorean we can determine that a^2+b^2=c^2

In this case the leg and adjacent make a distance of 2 and 3 we can substitute for a and b so

\(2^{2}+3^{2}=c^{2}\)

13=\(c^{2}\)

c= 3.605 rounded is 3.6

The required result of the sliding filament model given below.

The mechanism by which muscles contract is described by the sliding filament model. Actin and myosin myofilaments move over one another as a result of a cycle of repeated occurrences, constricting the sarcomere and creating tension in the muscle.

The A band is the middle, darkly pigmented area of the sarcomere that runs the entire length of the thick filaments. The portion of the thin filaments that cross over the thick filaments is also included. The I band is formed by the remaining thin filaments within the sarcomere but not by any thick filaments. Each A band has a narrow region in the middle called the H zone, which is made up entirely of dense filaments.

Myosin heads pull the thin filaments in the direction of the M line during muscle contraction. Because of this, the thin filaments glide inward, allowing their ends to overlap and connect at the sarcomere's core. The I band and H zone become smaller as a result.

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When equation f(x) = 3x + 4 is transformed to h(x) = f(x + 3) and plotted on the graph, both the lines of the equations are parallel to each other.

So, the equations are graphs are:

Now, graph both equations as follows:

Therefore, when equation f(x) = 3x + 4 is transformed to h(x) = f(x + 3) and plotted on the graph, both the lines of the equations are parallel to each other.

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Answer:

The zeros are (-5.5, 0) and (3.5, 0).

Step-by-step explanation:

g(t) = (t + 1)² − 20.25

0 = (t + 1)² − 20.25

20.25 = (t + 1)²

±4.5 = t + 1

t = -1 ± 4.5

t = -5.5 or 3.5

Answer:

if you use perimeter area you can get answer

Answer:

I-11I

Step-by-step explanation:

To find the distance between two numbers, find the absolute value of each number and add them together.

I-11I = 11

I0I = 0

11 + 0 = 11

Answer:

Hope this helps!

Step-by-step explanation:

The answer is 11. To find the distance between two numbers, find the absolute value of each number and add them together.

For the point 13. we have that the line passes through the points (-1,-2) and (0,1) then the slope is

and the line equation will be:

For the point 14: we have that the line passes through the points (-1,0) and (0,-1) then the sslope is:

and the line equation will be:

For the point 15: the line equation is y=-3 because the y coordinate will be always the same (-3) and the slope of the line will be equal to zero

Answer:

shawnmendesiskingofpop

Step-by-step explanation:

lol stream wonder

The maximum likelihood estimator for the variance, (ui|\(X_{2i}\), \(X_{3i}\), β₄\(X_{4i}\)), is unbiased.

To analyze the bias of the maximum likelihood estimator (MLE) for the variance, we need to consider the assumptions and properties of the regression model.

In the given regression model:

\(Y_i\) = β₁ + β₂\(X_{2i}\) + β₃\(X_{3i}\) + β₄\(X_{4i}\) + U\(_{i}\)

Here, \(Y_i\) represents the dependent variable, \(X_{2i}, X_{3i},\) and \(X_{4i}\) are the independent variables, β₁, β₂, β₃, and β₄ are the coefficients, U\(_{i}\) is the error term, and i represents the observation index.

The assumption of the classical linear regression model states that the error term, U\(_{i}\), follows a normal distribution with zero mean and constant variance (σ²).

Let's denote the variance as Var(U\(_{i}\)) = σ².

The maximum likelihood estimator (MLE) for the variance, σ², in a simple linear regression model is given by:

σ² = (1 / n) × Σ[( \(Y_i\) - β₁ - β₂\(X_{2i}\) - β₃\(X_{3i}\) - β₄\(X_{4i}\))²]

To determine the bias of this estimator, we need to compare its expected value (E[σ²]) to the true value of the variance (σ²). If E[σ²] ≠ σ², then the estimator is biased.

Taking the expectation (E) of the MLE for the variance:

E[σ²] = E[ (1 / n) × Σ[( \(Y_i\) - β₁ - β₂\(X_{2i}\) - β₃\(X_{3i}\) - β₄\(X_{4i}\))²]

Now, let's break down the expression inside the expectation:

[( \(Y_i\) - β₁ - β₂\(X_{2i}\) - β₃\(X_{3i}\) - β₄\(X_{4i}\))²]

= [ (β₁ - β₁) + (β₂\(X_{2i}\) - β₂\(X_{2i}\)) + (β₃\(X_{3i}\) - β₃\(X_{3i}\)) + (β₄\(X_{4i}\) - β₄\(X_{4i}\)) + \(U_{i}\)]²

= \(U_{i}\)²

Since the error term, \(U_{i}\), follows a normal distribution with zero mean and constant variance (σ²), the squared error term \(U_{i}\)² follows a chi-squared distribution with one degree of freedom (χ²(1)).

Therefore, we can rewrite the expectation as:

E[σ²] = E[ (1 / n) × Σ[\(U_{i}\)²] ]

= (1 / n) × Σ[ E[\(U_{i}\)²] ]

= (1 / n) × Σ[ Var( \(U_{i}\)) + E[\(U_{i}\)²] ]

= (1 / n) × Σ[ σ² + 0 ] (since E[ \(U_{i}\)] = 0)

Simplifying further:

E[σ²] = (1 / n) × n × σ²

= σ²

From the above derivation, we see that the expected value of the MLE for the variance, E[σ²], is equal to the true value of the variance, σ². Hence, the MLE for the variance in this regression model is unbiased.

Therefore, the maximum likelihood estimator for the variance, (ui|\(X_{2i}\), \(X_{3i}\), β₄\(X_{4i}\)), is unbiased.

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Answer:

it takes José an 1.18181818 hours

Step-by-step explanation:

since d is miles you replace the d in the equation with 100

so: 100=55t

divide both sides by 55

1.18181818=t

The parabola is represented by the following expression: y - 3 = (1 / 8) · (x - 3)². Concerning the following points:

(x, y) = (- 1, 5): YES (The point is 4 units left from focus and 4 units up from directrix)

(x, y) = (3, 3): YES (The point is 2 units down from focus and 2 units up from directrix)

(x, y) = (5, 5): NO

In this question we find a graph of the focus and the directrix of a parabola. The least distance between the focus and the directrix is represented by the following expression:

d = 2p

Where is the distance between the vertex and the focus.

The vertex of the parabola is the midpoint of the line segment of the least distance between focus and directrix. And the equation of the parabola in vertex form is:

y - k = [1 / (4 · p)] · (x - h)²

Where (h, k) are the coordinates of the vertex of the parabola.

First, determine the distance between vertex and focus:

(0, 2 · p) = (3, 5) - (3, 1)

(0, 2 · p) = (0, 4)

p = 2

Second, find the vertex of the parabola:

(h, k) = 0.5 · (3, 5) + 0.5 · (3, 1)

(h, k) = (3, 3)

Third, build the equation of the parabola:

y - 3 = (1 / 8) · (x - 3)²

Fouth, check if each point belongs to the parabola:

(x, y) = (- 1, 5)

y = (1 / 8) · (x - 3)² + 3

y = (1 / 8) · (- 1 - 3)² + 3

y = (1 / 8) · (- 4)² + 3

y = 2 + 3

y = 5

YES (The point is 4 units left from focus and 4 units up from directrix)

(x, y) = (3, 3)

y = (1 / 8) · (x - 3)² + 3

y = (1 / 8) · (3 - 3)² + 3

y = 3

YES (The point is 2 units down from focus and 2 units up from directrix)

(x, y) = (5, 5)

y = (1 / 8) · (x - 3)² + 3

y = (1 / 8) · (5 - 3)² + 3

y = 1 / 2 + 3

y = 4.5

NO

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Answer:

-3+\(4x^{2}\)

Step-by-step explanation:

What do the two variables in this system represent?

the two variables represent the number of hot dogs or soft drinks you will get.

Write a system of equations to represent the model.

let s = soft drinks and let h = hot dogs

3s+2h=7.70

2s+h=4.55

What is the cost of 1 soft drink? $1.4

What is the cost of 1 hot dog? $1.75

Solving for the above two questions

solve the second eqaution for h:

2s+h=4.55

subtract 2s

h=4.55-2s

substitute this into the first equation:

3s+2(4.55-2s)=7.70

distribute the 2

3s+9.1-4s=7.70

add like terms

-s+9.1=7.70

subtract 9.1

-s=-1.4

divide by -1

s=1.4

substitute into the equation for h:

h=4.55-2s

h=4.55-2(1.4)

h=4.55-2.8

h=1.75

Percentage of data within 2 population standard deviations of the mean is 68%.

To calculate the percentage of data within two population standard deviations of the mean, we need to first find the mean and standard deviation of the data set.

The mean can be found by summing all the values and dividing by the total number of values:

Mean = (20*2 + 22*8 + 28*9 + 34*13 + 38*16 + 39*11 + 41*7 + 48*0)/(2+8+9+13+16+11+7) = 32.68

To calculate standard deviation, we need to calculate the variance first. Variance is the average of the squared differences from the mean.

Variance = [(20-32.68)^2*2 + (22-32.68)^2*8 + (28-32.68)^2*9 + (34-32.68)^2*13 + (38-32.68)^2*16 + (39-32.68)^2*11 + (41-32.68)^2*7]/(2+8+9+13+16+11+7-1) = 139.98

Standard Deviation = sqrt(139.98) = 11.83

Now we can calculate the range within two population standard deviations of the mean. Two population standard deviations of the mean can be found by multiplying the standard deviation by 2.

Range = 2*11.83 = 23.66

The minimum value within two population standard deviations of the mean can be found by subtracting the range from the mean and the maximum value can be found by adding the range to the mean:

Minimum Value = 32.68 - 23.66 = 9.02 Maximum Value = 32.68 + 23.66 = 56.34

Now we can count the number of data points within this range, which are 45 out of 66 data points. To find the percentage, we divide 45 by 66 and multiply by 100:

Percentage of data within 2 population standard deviations of the mean = (45/66)*100 = 68% (rounded to the nearest percent).

Therefore, approximately 68% of the data falls within two population standard deviations of the mean.

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Answer:

32°

Step-by-step explanation:

line segment= 180 - 20 (we know that) 160. divide that by 5 (cause there are 5 x's, gets you 32°

Answer: 5

Step-by-step explanation:

  8-2b=-2

8-2b-8=-2-8

    -2b=-10

-2b/-2=-10/-2

       b=5

The mass of the radioactive sample at the beginning of the 8th day of the experiment a_13=310.7.

In a lab experiment, the decay of a radioactive isotope is being observed.

At the beginning of the first day of the experiment, the mass of the substance was 1300 grams and the mass decreased by 6% per day.

\(a_n=a_1r^{n-1}\)

Use the given value in the formula so we get,

\(a_{13}=1100(0.9)^{13-1}\)

Simplify the given term

\(a_{13}=310.7\)

Therefore, The mass of the radioactive sample at the beginning of the 8th day of the experiment a_13=310.7.

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Answer:

389

Step-by-step explanation:

The perimeter of the triangle is 3x + 10

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

Side lengths = (x+6) and (x+4) and x​

The perimeter is the distance AROUND the shape

Using the above as a guide, we have the following:

Perimeter = x + 6 + x + 4 + x

Evaluate the like terms

Perimeter = 3x + 10

HEnce, the perimeter is 3x + 10

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As the size of a sample increases, the mean of the distribution of sample means increases always. - False

The mean of the distribution of sample means does not always rise as sample number rises. The population mean is not changed by increasing the sample number because the population mean is equivalent to the mean of the distribution of sample means. The variation of the distribution of the sample means does, however, decline as the sample number rises.

The distribution of sample means approximates normality with a mean equal to population mean and a standard deviation equal to population standard deviation divided by square base of the total sample size. This is known as the central limit theorem. As a result, the distribution of sample means becomes more tightly focused around the community mean and less variable as the sample number rises.

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Answer:

1/2 of her beads are not red, and 3/4 of her beads are not green.

Step-by-step explanation:

If 1/2 of her beads are red, the other half can not be red, therefore meaning 1/2 of her beads are not red. If 1/4 if her beads are green, then the other 3/4 of her beads can not be green.

Step-by-step explanation:

please mark me as brainlest

Answer:

\(\sf \longmapsto 51^2\)

\(\sf \longmapsto (a + b)^2 = a^2 + 2ab + b^2\)

Here,

\(\sf \longmapsto (50 + 1)^2 = 50^2 + 2(50)(1) + 1^2\)

\(\sf \longmapsto 50^2 + 2(50)(1) + 1^2\)

\(\sf \longmapsto 2500 + 100 + 1\)

\(\sf \longmapsto 2601\)

Hence,solved.

Answer:

3/32

Step-by-step explanation:

prob greater than 5

that would be 6,7 or 8

p = 3/8

prob purple

p = 1/4

Prob of both

p = 3/8 * 1/4

p = 3/32

Answer:

y = x + 6

Step-by-step explanation:

m = gradient

m = rise / run = 4/4 = 1

m = 1

c = y intercept at x = 0

c = 6

now sub into y = mx+c

y = x + 6

you dont need to write the 1 in front of the x becuase 1 x X is the same as just X

The probability that the sample proportion will be between 0.9115 and 0.946 is 0.1496.

To calculate the probability that the sample proportion will be between 0.9115 and 0.946, we can use the sampling distribution of the sample proportion, assuming that the sample is taken from an infinite population.

The standard deviation of the sample proportion is given by:

σ_p = sqrt((p * (1 - p)) / n)

where p is the population proportion and n is the sample size.

In this case, p = 0.85 and n = 51. Plugging these values into the formula, we get:

σ_p = sqrt((0.85 * (1 - 0.85)) / 51)

= sqrt(0.127275 / 51)

≈ 0.092

Now, we can standardize the interval (0.9115, 0.946) using the sample proportion distribution:

z1 = (0.9115 - p) / σ_p

= (0.9115 - 0.85) / 0.092

≈ 0.667

z2 = (0.946 - p) / σ_p

= (0.946 - 0.85) / 0.092

≈ 1.043

Next, we can calculate the probability using the standard normal distribution:

P(0.9115 < p < 0.946) = P(z1 < Z < z2)

Looking up the values in the standard normal distribution table, we find:

P(0.9115 < p < 0.946) ≈ P(0.667 < Z < 1.043)

≈ 0.1496

Therefore, the probability that the sample proportion will be between 0.9115 and 0.946 is approximately 0.1496.

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The probability that your single battery-powered alarm clock works successfully when you need it is 99.5% and probability that both the battery-powered alarm clock and the smartphone alarm clock fail is 0.026%

It is a branch of mathematics that deals with the occurrence of a random event.

a. The probability that a single battery-powered alarm clock works successfully is 1 minus the probability that it fails.

1 - 0.005 = 0.995 or 99.5%.

b. The probability that both the battery-powered alarm clock and the smartphone alarm clock fail is the product of their individual probabilities of failure.

0.005 x 0.052 = 0.00026 or 0.026%.

The probability that both of them do not fail is the complement of the probability that they both fail

1 - 0.00026 = 0.99974 or 99.974%.

Hence, the probability that your single battery-powered alarm clock works successfully when you need it is 99.5% and probability that both the battery-powered alarm clock and the smartphone alarm clock fail is 0.026%

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