How many days in April

Answer:

4.0731 cm

Step-by-step explanation:

Use pythagorean Theorem which is a^2 + b^2 = c^2 where a and b are the sides of the triangle and c is the hypotenuse, in other words the longest side.

Since x is in line with the center of the circle and is perpendicular with line that measures 15.6 cm we know that one side of the triangle is half of 15.6 or 7.8.

So then we input our known values into the formula and solve for the missing one. The formula looks like this

7.8^2 + b^2 = 8.8^2

solve for b and you get about 4.07431

your very welcome

Answer:

X= 4.1

Step-by-step explanation:

Other answer wasn't rounded.

X= 4.1 cm

a right angle so 90 degrees

Answer: (I am really not sure the answer if it is wrong I am so sorry)

h=3, k=2

Horizontal: up, 2 units

Vertical: up, 3 units

The 99% confidence interval for the mean monthly rent of all families with a monthly income of $2500 is given as follows:

(5.96, 22.38)

The t-distribution is used when the standard deviation for the population is not known, and the bounds of the confidence interval are given according to the equation presented as follows:

[tex]\overline{x} \pm t\frac{s}{\sqrt{n}}[/tex]

The variables of the equation are listed as follows:

The critical value, using a t-distribution calculator, for a two-tailed 99% confidence interval, with 12 - 1 = 11 df, is t = 3.1058.

The parameters are given as follows:

[tex]\overline{x} = 14.17, s = 9.16, n = 12[/tex]

The lower bound of the interval is given as follows:

[tex]14.17 - 3.1058 \times \frac{9.16}{\sqrt{12}} = 5.96[/tex]

The upper bound of the interval is given as follows:

[tex]14.17 + 3.1058 \times \frac{9.16}{\sqrt{12}} = 22.38[/tex]

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Answer: 5 people

Step-by-step explanation:

The easiest way to do this is to set up a proportion. 6/12=x/10. To solve, cross-multiply and divide. Multiply 6 and 10 (6*10=60) and divide by 12 (60/12=5).

You can also find that 6/12 is equal to 1/2 by dividing each side by 6. Then multiply 10 by 1/2 (or divide 10 by 2) to get the final answer of 5 people.

The Durbin-Watson test is an important tool for determining autocorrelation.

The steps involved in testing autocorrelation theory using the Durbin-Watson (DW) test are as follows:

a. Develop the hypothesis to be tested.

In this case, the hypothesis is that the model residuals are autocorrelated

b. Estimate the original model, and obtain the residuals .

c. The residuals should be arranged in order of time.

d. The formula for Durbin-Watson is applied to the residuals.

e. The Durbin-Watson statistic value is interpreted in the context of the hypothesis to determine whether to accept or reject the null hypothesis.

If the Durbin-Watson statistic is greater than two but less than or equal to four, the residuals are said to be uncorrelated.

On the other hand, if the Durbin-Watson statistic is less than two, positive autocorrelation exists.

Similarly, if the Durbin-Watson statistic is greater than four, negative autocorrelation exists.

Now, considering the values given in part 2 of the question, we have;

60 quarterly observations3 explanatory variables (plus a constant term)Durbin-Watson statistic of 0.95The calculation of DW statistic can be done as follows:

DW = 2.0 - 2(r)where, r is the sample autocorrelation coefficient of the residuals. To test the hypothesis, we need to perform the following actions;

Calculate the critical value of DW at 1% level of significance and for the given degrees of freedom.

Decide the conclusion by comparing the calculated value of DW with the critical value.

If the calculated value is less than the lower critical value, we reject the null hypothesis; otherwise, we accept the null hypothesis. Therefore, The null hypothesis H0:

No first-order autocorrelation versus alternative hypothesis Ha:

first-order autocorrelation exists can be tested with a DW test. The test statistic (DW) is 0.95, which is greater than the lower critical value (dL) of 1.28.

Therefore, the null hypothesis H0 is not rejected, and it can be concluded that there is no first-order autocorrelation in the regression model residuals.

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The Particular Integral is,$$y_p = -\frac{512}{6301}sin(8t)+\frac{343}{6301}cos(8t)$$

The given differential equation is:$$\frac{d^2}{dy^2}+49y = 4.5sin(8t)$$For finding the Particular Integral, the initial steps are: Finding the characteristic equation of the homogeneous equation.$$m^2+49=0$$$$\Rightarrow m = 7i, -7i$$Let us assume the Particular Integral of the form,$$y_p = A sin(8t)+Bcos(8t)$$ Differentiating the Particular Integral to get,$$\frac{d}{dt}y_p = 8Acos(8t)-8Bsin(8t)$$$$\frac{d^2}{dy^2}y_p = -64Asin(8t)-64Bcos(8t)$$ Substituting these in the differential equation, we get$$(-64Asin(8t)-64Bcos(8t))+49(Asin(8t)+Bcos(8t)) = 4.5sin(8t)$$S implifying the equation, we get,$$(49A-64B)cos(8t)+(49B+64A)sin(8t)=4.5sin(8t)$$Comparing the coefficients, we get$$49A-64B=0$$$$49B+64A=4.5$$Solving these equations, we get,$$A = -\frac{512}{6301}$$$$B = \frac{343}{6301}$$

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The number of different simple random samples of size 5 that can be obtained from a population of size 35 is 324,632.

To calculate the number of different simple random samples of size 5 that can be obtained from a population of size 35, we can use the combination formula. The formula for combination is given by:

C(n, r) = n! / (r! * (n-r)!)

where n is the population size and r is the sample size.

In this case, we have n = 35 (population size) and r = 5 (sample size). Plugging in these values into the formula:

C(35, 5) = 35! / (5! * (35-5)!)

Simplifying this expression:

C(35, 5) = 35! / (5! * 30!)

Calculating the factorial values:

C(35, 5) = 35 * 34 * 33 * 32 * 31 / (5 * 4 * 3 * 2 * 1)

C(35, 5) = 324,632

Therefore, the number of different simple random samples of size 5 that can be obtained from a population of size 35 is 324,632.

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Given: For a standard normal distribution, we need to find the probability between P(-2.46 < z < 2.82). It is found that P(-2.46 < z < 2.82) = 0.9905.

Explanation: Given, P(-2.46 < z < 2.82)

The standard normal distribution has a mean of μ=0 and a standard deviation of σ=1. It is called the standard normal distribution, because it is the normal distribution where z-scores correspond to the number of standard deviations above or below the mean.

A z-score tells us how many standard deviations a value is from the mean.

A positive z-score indicates a value above the mean, while a negative z-score indicates a value below the mean.

To find the probability of P(-2.46 < z < 2.82), we need to find the area under the standard normal distribution curve between -2.46 and 2.82.

To find this probability, we can use a standard normal distribution table or a calculator that has a normal distribution function.

Using a standard normal distribution table, we can find the area to the left of z=2.82 and the area to the left of z=-2.46 and then subtract the two values to find the area between these z-scores.

The area to the left of z=2.82 is 0.9974, and the area to the left of z=-2.46 is 0.0069.

Therefore, the area between these z-scores is:

P(-2.46 < z < 2.82) = 0.9974 - 0.0069

= 0.9905

Therefore, P(-2.46 < z < 2.82) = 0.9905.

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Answer:

The type of television program viewed

Step-by-step explanation:

The Independent variable may be explained as those variables which determines the outcome or output of an experiment or analysis. The output is the dependent variable. Hence, the independent variable determines the predicted variable.

In the scenario described above, the type of television program viewed, either violent or nonviolent is the independent or predictor variable. This determines the level or incidence of aggressive behavior exhibited by the subject, this incidence of aggressive behavior is the dependent or predicted variable.

Step-by-step explanation:

y-intercept=when x is 0

So just set 0 for x in all equations

2(3^0)

2(1)=2

(0,2)

-5(.5)^0

-5(1)

-5=y

(0,-5)

6(3)^0

6(1)=6

(0,6)

Hope that helps :)

Answer:

3,992

Step-by-step explanation:

1/10 = 0.1

399.2/0.1 = 3,992

Answer:

3990

Step-by-step explanation:

Place the decimal to a fraction:

399.2 = 399 2/10

Simplify =  399 1/5

Put in improper fraction = 1995/5

Simplify more = 399/1 or 399

So:

399 divided by 1/10 = 3990

Answer:

no

Step-by-step explanation:

no because its equal to 2x+10

Answer:

1335.18

Step-by-step explanation:

V=π(d/2)2h

Answer:

1335.18

Step-by-step explanation:

V=π(d/2)2h

Answer:

47 degrees

Step-by-step explanation:

A triangle’s degrees add up to 180, so we can use that theorem

102 + 31 + x = 180

133 + x = 180

x = 47 degrees

this is 3rd grade math? jesus..

Answer:

that poor 3rd grader O.O that is hard

Step-by-step explanation:

online

Answer:

(7.5, 3)

Step-by-step explanation:

(5,5) and (10, 1)

Midpoint:

[tex](\frac{x1 + x2}{2} ,\frac{y1+y2}{2} )\\\\(\frac{5 + 10}{2} ,\frac{5+1}{2} )\\\\(\frac{15}{2} ,\frac{6}{2} )\\\\(7.5,3)[/tex]

Answer:

it should be (7.5,11.25)

Step-by-step explanation:

The members chosen to participate in the upcoming moral doction can be determined through a random selection process.

One way to generate a random number is to use a random number generator (RNG). In this case, the list of members includes names like Cena, Prece, Thompson, Noon, Cooper, Zen, OG, Man, Loche, Dan, Zana, DE, Wine, and Tale. To select four members randomly, you can use the RNG to generate four random numbers within the range of the list indexes. The corresponding names at those indexes would be the chosen members.

In order to select four random members from the list of names including Cena, Prece, Thompson, Noon, Cooper, Zen, OG, Man, Loche, Dan, Zana, DE, Wine, and Tale, you can use an RNG. The RNG generates random numbers within a specified range. In this case, the range would be the number of elements in the list, which is 14.

Let's assume the RNG generates the following numbers: 21, 15, 23, and 23. Using these numbers as indexes, you would select the corresponding names in the list: Thompson, Noon, Dan, and Dan. Therefore, the four randomly selected members would be Thompson, Noon, Dan, and Dan.

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Answer:

x=18

y=27

explanation: 4 times 3 is 12

Answer:

2

6

9

Step-by-step explanation:

Because if you pay attention to our teacher he told us how to respond it!

If an analyst wants to estimate the mean for an entire population (μ), the estimate would be more accurate if the analyst computed the sample mean.

If an analyst wants to estimate the mean for an entire population (μ), the estimate would be more accurate if the analyst computed the sample mean.

What is a population?In statistics, a population is a complete set of events that a statistician desires to investigate. A population is a collection of individuals, items, or data points that have a specific attribute of interest to the analyst.

What is an estimate? An estimate is an approximation of an unknown quantity that is dependent on imperfect or incomplete information. In statistics, an estimate is a projection of a population parameter dependent on data collected from a sample. An estimate is a numerical value generated from a statistical formula that is intended to provide an approximate value for an unknown population parameter.

What is an analyst?An analyst is an individual who examines a company or business's financial and business data to evaluate their health and determine their future development.

What does it mean to estimate the mean for an entire population?In statistics, estimating the mean for an entire population entails using a sample of data to calculate an approximate value of the mean for the entire population. The mean is the numerical value that provides information about the data set's central tendency. The analyst should choose a representative sample of the population to ensure the estimate is accurate.

What is the best way to estimate the mean of the entire population?

The estimate would be more accurate if the analyst computed the sample mean. The sample mean is an estimate of the population mean, denoted as μ. Sample mean is the average of the sampled data, and it is computed as follows;$$\overline{x}=\frac{\sum_{i=1}^{n}x_i}{n}$$

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If an analyst wants to estimate the mean for an entire population (μ), the estimate would be more accurate if the analyst computed the mean of a larger random sample.

A random sample is a method of selecting a subset from the entire population. It should be such that every member of the population has an equal chance of being chosen.

The sample should be sufficiently large and representative of the population as a whole to make reasonable inferences regarding the population. The mean value computed from a sample is used as an estimate of the true population mean. The sample mean is a random variable that can fluctuate from one sample to the next, depending on the sample that is taken. It has a standard deviation, called the standard error of the mean, that can be calculated using the following formula:standard error of the mean = standard deviation of the population / square root of the sample size.The larger the sample size, the smaller the standard error of the mean, and hence the more accurate the estimate of the population mean will be.

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The center of mass can be determined by dividing the moment of the solid with respect to each coordinate axis by the total mass.

In cylindrical coordinates, the paraboloid and the plane can be represented as z = 12r^2 and z = a, respectively. To find the mass, we integrate the density function k over the region of the solid, which is bounded by z = 12r^2, z = a, and the region in the xy-plane where the paraboloid intersects the plane z = a. The integral becomes M = k * ∭ρ dV, where ρ is the density function.

To find the center of mass, we calculate the moments of the solid with respect to each coordinate axis. The x-coordinate of the center of mass can be obtained by dividing the moment about the x-axis by the total mass. Similarly, the y-coordinate and z-coordinate of the center of mass can be calculated by dividing the moments about the y-axis and z-axis, respectively, by the total mass.

By evaluating the triple integral and performing the necessary calculations, we can determine the mass and center of mass of the given solid in cylindrical coordinates.

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Answer:

Each term in the second sequence is double the corresponding term in the first sequence.

Step-by-step explanation:

pls mark as brainliest

The correct statement that describe the two sequences is:

Each term in the second sequence is 10 times the corresponding term in the first sequence.

This is a type of sequence which have common difference between each term. It is represent mathematically as:

Tₙ = a + (n – 1)d

Where

Tₙ is the nth term

a is the first term

n is the number of terms

d is the common difference

Relationship = sequence 2 / sequence 1

Relationship = sequence 2 / sequence 1

Sequence 2 / sequence 1 = 10 / 1

Cross multiply

Sequence 2 = 10 × sequence 1

Relationship = sequence 2 / sequence 1

Sequence 2 / sequence 1 = 20 / 2

Sequence 2 / sequence 1 = 10 / 1

Cross multiply

Sequence 2 = 10 × sequence 1

If we continue with the pattern above, we'll discovered that

Sequence 2 = 10 × sequence 1

Thus, we can conclude that:

Each term in the second sequence is 10 times the corresponding term in the first sequence.

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Answer:

b

Step-by-step explanation:

2x^2 = 72

Divide both sides by 2:

X^2 = 36

Take the square root of both sides

X = 6

Main answer: RP* = RP.

Supporting answer:

RP* is defined as the set of languages L for which there exists a polynomial-time Turing machine M that, on input w, halts with probability at least 1/2 if w is not in L, and halts with probability at least 1 - 2-l|w| if w is in L. This definition is almost identical to the definition of RP, except that the probability of error is reduced from 1/2 to 2-l|w|. However, RP* and RP are equivalent.

To see that RP* is a subset of RP, note that if a language L is in RP*, then there exists a polynomial-time Turing machine M and a polynomial p such that (i) if we L, then Prųe{0,1 }p(w) [M accepts (w,t)]21-(1/2)lwl and (ii) if w & L, then Prge{0,1}P(w) [M rejects (w,t)] = 1. This means that M can be used as a randomized algorithm for L with error probability at most 1/2. Therefore, L is in RP.

To see that RP is a subset of RP*, note that if a language L is in RP, then there exists a polynomial-time Turing machine M and a constant c such that (i) if we L, then Prųe{0,1}c|w|[M accepts (w,t)] >= 1/2 and (ii) if w & L, then Prge{0,1}P(w) [M rejects (w,t)] < 1/2. By setting p(n) = c * 2n, we obtain a Turing machine M' that satisfies the conditions of RP*. Therefore, L is in RP*.

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