Let X be the number of material anomalies occurring in a particular region of an aircraft gas-turbine disk. The article "Methodology for Probabilistic Life Prediction of Multiple-Anomaly Materials"� proposes a Poisson distribution for X. Suppose that ? = 4. (Round your answers to three decimal places.) (a) Compute both P(X ? 4) and P(X < 4). (b) Compute P(4 ? X ? 5). (c) Compute P(5 ? X). (d) What is the probability that the number of anomalies does not exceed the mean value by more than one standard deviation?

Answer:

(a) 25%

(b) 50%

(c) 18.75%

Step-by-step explanation:

Since there are two possible outcomes for each coin the number of total possible permutations is given by:

[tex]N=2*2*2*2=16[/tex]

(a) The first and last flips come up heads.

4 possible outcomes meet this requirement:

{HHHH}, {HHTH}, {HTHH},{HTTH}

P=4/16 = 25%

(b) There are at least two consecutive flips that come up heads.

8 possible outcomes meet this requirement:

{HHHH}, {HHHT},{HHTH} {HTHH},{THHH},{TTHH},{HHTT}, {THHT}

P=8/16 = 50%

(c) The first flip comes up tails and there are at least two consecutive flips that come up heads.

3 possible outcomes meet this requirement:

{THHH},{TTHH}, {THHT}

P=3/16 = 18.75%

Answer:

Confidence Interval: (0.44,1.00)

Step-by-step explanation:

We are given the following data set:

0.60, 0.74, 0.09, 0.89, 1.31, 0.51, 0.94

Formula:

[tex]\text{Standard Deviation} = \sqrt{\displaystyle\frac{\sum (x_i -\bar{x})^2}{n-1}}[/tex]  

where [tex]x_i[/tex] are data points, [tex]\bar{x}[/tex] is the mean and n is the number of observations.  

[tex]Mean = \displaystyle\frac{\text{Sum of all observations}}{\text{Total number of observation}}[/tex]

[tex]Mean =\displaystyle\frac{:5.08}{7} = 0.725[/tex]

Sum of squares of differences = 0.8809

[tex]S.D = \sqrt{\frac{0.8809}{6}} = 0.383[/tex]

90% Confidence interval:  

[tex]\bar{x} \pm t_{critical}\displaystyle\frac{s}{\sqrt{n}}[/tex]  

Putting the values, we get,  

[tex]t_{critical}\text{ at degree of freedom 6 and}~\alpha_{0.10} = \pm 1.943[/tex]  

[tex]0.725 \pm 1.943(\frac{0.383}{\sqrt{7}} ) =0.725 \pm 0.2812 = (0.44,1.00)[/tex]

No, it does not appear that there is too much mercury in tuna​ sushi.

Answer:

77.53

Step-by-step explanation:

Sample size (n) = 15

Sample mean (μ) = 75

Sample variance (V) = 25

Sample standard deviation (σ) = 5

For a 95% confidence interval, z-score = 1.960

The upper limit of the confidence interval is defined as:

[tex]UL=\mu+z\frac{\sigma}{\sqrt{n}}\\UL=75+1.960\frac{5}{\sqrt{15}} \\UL = 77.53[/tex]

Therefore, the upper limit of the 95% confidence interval proposed is 77.53.

The upper limit of a 95% confidence interval for the population mean is approximately 76.98.

The upper limit of a 95% confidence interval for the population mean can be calculated using the formula:

Upper Limit = Sample Mean + (Z-value)(Standard Error)

In this case, since the sample size is 15 and the sample variance is 25:

Standard Error = sqrt(sample variance/sample size)

Z-value can be obtained from the standard normal distribution table or calculator based on the desired confidence level. For a 95% confidence level, the Z-value is approximately 1.96.

Plugging in the values:

Upper Limit = 75 + (1.96)(sqrt(25/15))

Upper Limit ≈ 76.98

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Answer: he drove 156 miles

Step-by-step explanation:

Aldo rented a truck for one day. There was a base fee of $20.99. This means that the fee of $20.99 is constant.

There was an additional charge of 86 cents for each mile driven.

Converting 86 cents to dollar, it becomes 86/100 = $0.86

Aldo had to pay $155.15

when he returned the truck. This means that the sum of the base fee and the additional fee is $155.15. The additional fee paid would be the total amount paid minus the base fee. It becomes

155.15 - 20.99 = 134.16

Since there is an additional charge of 0.86 for each mile driven, number of miles for which he was charged $134.16 will be total additional fee divided by cost per mile. It becomes

134.16/0.86 = 156 miles

Answer:

The proof is wrong. The part that says 'take an element b ∈ A such that (a, b) ∈ R' is assuming that such an element b exists, and that may not be the case.  We can give the 'theorem' validation by adding the hypothesis ' each element of A is related to any other element of A', otherwise the 'theorem' is false. An example for that is the following relation on the natural numbers:

'a R b if both numbers are even'

This relation if crearly symmetric and transitive, but it is not reflexive, an odd number does not relate with itself. In fact, odd numbers dont relate with any number.

Final answer:

The error in the given "proof" lies in the assumption that an element b ∈ A exists such that (a, b) ∈ R, which is not necessarily true for all a ∈ A. Symmetry and transitivity of a relation R do not inherently guarantee its reflexivity.

Explanation:

The error in the "proof" that a symmetric and transitive relation R on a set A is also reflexive lies in the initial assumption. The proof assumes the existence of an element b ∈ A such that (a, b) ∈ R for an arbitrary a ∈ A, but this is not guaranteed. Reflexivity requires that (a, a) ∈ R for all a ∈ A, independently of the existence of any such b. The proof fails because it assumes a specific relationship exists (between a and b) to demonstrate reflexivity, which is a general property that must hold without needing to reference any other elements.

The properties of reflexivity, symmetry, and transitivity define an equivalence relation. Symmetry and transitivity alone do not imply reflexivity. Therefore, the statement that a symmetric and transitive relation is reflexive is incorrect without additional assumptions about R.

Answer:

Which is the output of the formula =AND(12>6;6>3;3>9)?

A.

TRUE

B.

FALSE

C.

12

D.

9

Step-by-step explanation:

Answer:

A correlation coefficient close to zero

Step-by-step explanation:

This makes linear regression unreasonable because the correlation coefficient shows how related the data points are, -1 and 1 being very strong and 0 being uncorrelated. So the line of best fit would not make sense because the data points are random in relation to one another.

Step-by-step explanation:

Answer:

The sum of squares due to regression(SSR)=928.02

Step-by-step explanation:

We are given that

Dependent variable=y

Independent variable=x

[tex]\sum x=90[/tex]

[tex]\sum(y-\bar y)(x-\bar x)=466[/tex]

[tex]\sum y=170[/tex]

[tex]\sum(x-\bar x)^2=234[/tex]

n=10

[tex]\sum(y-\bar y)^2=1434[/tex]

SSE=505.98

We have to find the sum of squares due to regression.

It means we have to find SSR.

SST=[tex]\sum(y-\bar y)^2=1434[/tex]

[tex]SSR=SST-SSE=1434-505.98=928.02[/tex]

Hence, the sum of squares due to regression(SSR)=928.02

We use the Z-score to determine the probability of selecting a single woman with a height less than 64.9 inches, and the concept of a sampling distribution (standard error) for a sample of 22 women. Generally, the larger the sample size, the closer the sample mean gets to the population mean, making it usually more probable to select one woman with a height of less than 64.9 inches than for a sample of 22 women.

This question is about probability and statistics, and specifically about the Normal Distribution and its applications. We know that the height of women aged 20-29 is normally distributed with a mean of 64.4 inches and a standard deviation of 2.3 inches.

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Calculate probabilities for selecting 1 woman and a sample of 22 women with heights less than 64.9 inches; Compare which selection is more likely.

Given:

Mean (μ) = 64.4 inches

Standard deviation (σ) = 2.3 inches

Calculate the z-score:

z = (x - μ) / σ

  = (64.9 - 64.4) / 2.3

  = 0.5 / 2.3

  = 0.2174

Now, we look up the probability corresponding to a z-score of 0.2174 in the standard normal table.

The probability for z = 0.2174 is approximately 0.5869.

So, the probability of randomly selecting 1 woman with a height less than 64.9 inches is approximately 0.5869.

Calculate standard error:

SE = σ / √n

    = 2.3 / √(22)

    ≈ 0.489

Find the z-score for the sample mean:

z = ([tex]^-_x[/tex] - μ) /  σ

 = (64.9 - 64.4) / 0.489

 = 0.5 / 0.489

 ≈ 1.021

Now, we look up the probability corresponding to a z-score of 1.021 in the standard normal table.

The probability for z = 1.021 is approximately 0.8451.

So, the probability of selecting a sample of 22 women with a mean height of less than 64.9 inches is approximately 0.8451.

→ Probability of randomly selecting 1 woman with a height less than 64.9 inches: 0.5869

→ Probability of selecting a sample of 22 women with a mean height less than 64.9 inches: 0.8451

Since 0.8451 is greater than 0.5869, it is more likely to select a sample of 22 women with a mean height of less than 64.9 inches.

Answer:

More than 50% would germinate

Step-by-step explanation:

Given that in the production of a plant, a treatment is being evaluated to germinate seed. From a total of 60 seed it was observed that 37 of them germinated

Let us check whether more than 50% will germinate using hypothesis test

[tex]H_0: p = 0.50\\H_a: p>0.50\\[/tex]

(right tailed test)

Sample proportion p =[tex]\frac{37}{60} =0.617\\q = 0.383\\Std error = \sqrt{\frac{pq}{n} } =0.0628[/tex]

p difference = 0.117

Test statistic Z = p difference/std error = 1.864

p value =0.0312

Since p value <0.05 our significance level of 5% we reject null hypothesis

It is  possible to claim that most of the seed will germinate (i.e. more than 50%)

Answer:

C. convenience sample

Step-by-step explanation:

Convenience sampling is a type of non-probability sampling or non-random sampling which is any sampling method where some elements of the population have no chance of selection. The selection is left at the judgment of the interviewer or investigator and this non-randomness in the statistical sense implies that the interviewer or investigator do not fulfill some of the basic requirements or assumptions of the common standard methods for testing hypothesis and drawing inferences from the sample data to the target population

The length of a curve [tex]C[/tex] given parametrically by [tex](x(t),y(t))[/tex] over some domain [tex]t\in[a,b][/tex] is

[tex]\displaystyle\int_C\mathrm ds=\int_a^b\sqrt{\left(\frac{\mathrm dx}{\mathrm dt}\right)^2+\left(\frac{\mathrm dy}{\mathrm dt}\right)^2}\,\mathrm dt[/tex]

In this case,

[tex]x(t)=\cos(\cos4t)\implies\dfrac{\mathrm dx}{\mathrm dt}=-\sin(\cos4t)(-\sin4t)(4)=4\sin4t\sin(\cos4t)[/tex]

[tex]y(t)=\sin(\cos4t)\implies\dfrac{\mathrm dy}{\mathrm dt}=\cos(\cos4t)(-\sin4t)(4)=-4\sin4t\cos(\cos4t)[/tex]

So we have

[tex]\displaystyle\left(\frac{\mathrm dx}{\mathrm dt}\right)^2+\left(\frac{\mathrm dy}{\mathrm dt}\right)^2=16\sin^24t\sin^2(\cos4t)+16\sin^24t\cos^2(\cos4t)=16\sin^24t[/tex]

and the arc length is

[tex]\displaystyle\int_0^1\sqrt{16\sin^24t}\,\mathrm dt=4\int_0^1|\sin4t|\,\mathrm dt[/tex]

We have

[tex]\sin(4t)=0\implies4t=n\pi\implies t=\dfrac{n\pi}4[/tex]

where [tex]n[/tex] is any integer; this tells us [tex]\sin(4t)\ge0[/tex] on the interval [tex]\left[0,\frac\pi4\right][/tex] and [tex]\sin(4t)<0[/tex] on [tex]\left[\frac\pi4,1\right][/tex]. So the arc length is

[tex]=\displaystyle4\left(\int_0^{\pi/4}\sin4t\,\mathrm dt-\int_{\pi/4}^1\sin4t\,\mathrm dt\right)[/tex]

[tex]=-\cos(4t)\bigg_0^{\pi/4}-\left(-\cos(4t)\bigg_{\pi/4}^1\right)[/tex]

[tex]=(\cos0-\cos\pi)+(\cos4-\cos\pi)=\boxed{3+\cos4}[/tex]

In this exercise we have to use the knowledge of the integral to calculate the length of the arc:

The length of a curve is given by  [tex]3+ cos( 4)[/tex]

The length of a curve  given parametrically by  [tex](x(t), y(t))[/tex] over some domain  is [tex]t \in [a, b][/tex]

[tex]\int\limits_C \, ds = \int\limits^a_b {\sqrt{(\frac{dx}{dt})^2 + (\frac{dy}{dt})^2 } } \, dt[/tex]

Now performing the derivatives with respect to X and Y, we find that:

[tex]x(t) = cos(cos4t) \rightarrow \frac{dx}{dt} = -sin(cos4t)(-sin4t)(4)= 4sin4tsin(cos4t)\\y(t) = sin(cos4t) \rightarrow \frac{dy}{dt} = cos(cos4t)(-sin4t)(4)=-4sin4tcos(cos4t)[/tex]

So we have:

[tex](\frac{dx}{dt} )^2+(\frac{dy}{dt} )^2= 16sin^24tsin^2(cos4t)+16sin^24tcos^2(cos4t)= 16sin^24t[/tex]

And the arc length is:

[tex]\int\limits^1_0 {\sqrt{16sin^24t} } \, dt= 4\int\limits^1_0 {sin4t} \, dt\\sin(4t)=0 \rightarrow 4t=n\pi \rightarrow t= \frac{n\pi }{4}[/tex]

where n  is any integer; this tells us  [tex]sin(4t)\geq 0[/tex] on the interval [tex][0,\pi/4][/tex] and  [tex]sin(4t)<0[/tex] on [tex][\pi/4, 1][/tex]. So the arc length is:

[tex]=4(\int\limits^{\pi/4}_0 {sin(4t)} \, dt - \int\limits^1_{\pi/4} {sin(4t)} \, dt)\\=(cos(0)-cos(\pi))+(cos(4)-cos(\pi))= 3+cos(4)[/tex]

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Answer:

Step-by-step explanation:

Answer:

- frequency of action potentials

The central nervous system determines the strength of a stimulus based on the frequency of action potentials. The intensity of the stimulus is interpreted not by the size, but by the frequency at which these potentials are produced.

The central nervous system determines the strength of a stimulus based on the frequency of action potentials. The central nervous system uses action potentials to transfer and process information. An action potential is a brief electrical charge that travels along an axon. The strength or intensity of the stimulus is interpreted not by the size of the action potentials, but by the frequency at which they are produced. For instance, a slightly warm temperature may produce action potentials at a lower frequency than a very hot temperature, which would produce action potentials at a high frequency. The central nervous system decodes this frequency to understand the intensity of the stimulus.

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Answer:

  x = 55°

Step-by-step explanation:

The sum of the angles in a triangle is 180°, so you have ...

  x + 50° +75° = 180°

  x = 55° . . . . . . . . . . . . subtract 125°

Answer: 1.41

Step-by-step explanation:

Test statistic(z) for proportion is given by :-

[tex]z=\dfrac{\hat{p}-p}{\sqrt{\dfrac{p(1-p)}{n}}}[/tex]

, where p=population proportion.

[tex]\hat{p}[/tex]= sample proportion

n= sample size.

As per given , we have

[tex]H_0:\mu=0.68\\\\ H_a: \mu\neq0.68[/tex]

n= 102

[tex]\hat{p}=0.745[/tex]

Then, the test statistic (z) for this hypothesis test will be :-

[tex]z=\dfrac{0.745-0.68}{\sqrt{\dfrac{0.68(1-0.68)}{102}}}\\\\=\dfrac{0.065}{\sqrt{\dfrac{0.2176}{102}}}\\\\=\dfrac{0.065}{\sqrt{0.0021333}}\\\\=\dfrac{0.065}{0.04618802}=1.40729132792\approx1.41[/tex]

[Rounded to the two decimal places]

Hence, the  test statistic (z) for this hypothesis test = 1.41

The P-value of 0.0072, we can conclude that there is statistically significant evidence to support the claim that the proportion of wives married less than two years who planned to have children is significantly higher than the proportion of wives married five years.

To determine whether the proportion of wives married less than two years who planned to have children is significantly higher than the proportion of wives married five years, we can conduct a hypothesis test, following these steps:

1. Define the null and alternative hypotheses:

Null hypothesis (H0): The proportion of wives planning to have children is the same in both groups, regardless of the length of marriage.

Alternative hypothesis (H1): The proportion of wives planning to have children is higher in the group married less than two years compared to the group married five years.

2. Calculate the proportions and standard errors:

Proportion of wives planning children (<2 years): 240/300 = 0.8

Proportion of wives planning children (5 years): 288/400 = 0.72

Pooled standard error: sqrt((0.8*(1-0.8))/300 + (0.72*(1-0.72))/400) = 0.0298

3. Calculate the test statistic:

z = (0.8 - 0.72) / 0.0298 = 2.68

4. Find the P-value:

Using a z-score table or statistical software, look up the P-value associated with z = 2.68. This gives us a P-value of approximately 0.0072.

5. Make a decision:

Typically, we use a significance level of alpha (α) = 0.05. Since the P-value (0.0072) is less than alpha, we reject the null hypothesis.

This means there is statistically significant evidence to conclude that the proportion of wives planning to have children is higher in the group married less than two years compared to the group married five years.

Therefore, based on the P-value of 0.0072, we can conclude that there is statistically significant evidence to support the claim that the proportion of wives married less than two years who planned to have children is significantly higher than the proportion of wives married five years.

Answer:

50%

Step-by-step explanation:

68-95-99.7 rule

68% of all values lie within the 1 standard deviation from mean [tex](\mu-\sigma,\mu+\sigma)[/tex]

95% of all values lie within the 1 standard deviation from mean  [tex](\mu-1\sigma,\mu+1\sigma)[/tex]

99.7% of all values lie within the 1 standard deviation from mean  [tex](\mu-3\sigma,\mu+3\sigma)[/tex]

The distribution of the number of daily requests is bell-shaped and has a mean of 55 and a standard deviation of 4.

[tex]\mu = 55\\\sigma = 4[/tex]

68% of all values lie within the 1 standard deviation from mean [tex](\mu-\sigma,\mu+\sigma)[/tex] = [tex](55-4,55+4)[/tex]= [tex](51,59)[/tex]

95% of all values lie within the 2 standard deviation from mean  [tex](\mu-1\sigma,\mu+1\sigma)[/tex]= [tex](55-2(4),55+2(4))[/tex]= [tex](47,63)[/tex]

99.7% of all values lie within the 3 standard deviation from mean  [tex](\mu-3\sigma,\mu+3\sigma)[/tex]= [tex](55-3(4),55+3(4))[/tex]= [tex](43,67)[/tex]

Refer the attached figure

P(43<x<55)=2.5%+13.5%+34%=50%

Hence The approximate percentage of light bulb replacement requests numbering between 43 and 55 is 50%

Answer: 1.4375

Step-by-step explanation:

Pela has 5 3/4 feet of string to make necklaces. We would convert 5 3/4 feet to improper fraction. It becomes 23/4 feet. So Pela has 23/4 feet of string to make necklaces.

She wants to make 4 necklaces that are the same length. To determine how long should each necklace be,

We would divide the total length of the string by the number of necklaces to be made. It becomes

23/4 ÷ 4 = 23/4 × 1/4 = 23/16

The length of each string is 23/16 or 1.4375 feet

Answer:

Step-by-step explanation:

Hello!

You have the difference between two sample means. Remember the sample means are variables with certain distribution, let's say, in this case, both sample means have a normal distribution. X[bar] = sample mean

X₁[bar]~N(μ₁;σ₁²/n₁)

X₂[bar]~N(μ₂;σ₂²/n₂)

Then the difference between these two variables results in a third variable that will also have a normal distribution:

X₁[bar]-X₂[bar]~N(μ₁-μ₂;σ₁²/n₁+σ₂²/n₂)

These are some of the properties of the normal distribution

Centered in μ

Symmetrical

Bell-shaped

[μ - σ; μ + σ]= 68% of the distribution

[μ - 2σ; μ+ 2σ]= 95% of the distribution

[μ - 3σ; μ+ 3σ]= 99.7% of the distribution

Check attachment.

Taking these properties into account, if you where to draw the results of the 100 trials, where μ₁-μ₂=0 would be its center and the standard deviation of the difference is 1.78.

68% of the information will be between (μ₁-μ₂) ± [(σ₁/√n₁)+(σ₂/√n₂)], this is 0 ± 1.78

I hope it helps!

[tex]\vec F(x,y,z)=(x^2+y)\,\vec\imath+(y^2+x)\,\vec\jmath+ze^z\,\vec k[/tex]

We want to find [tex]f(x,y,z)[/tex] such that [tex]\nabla f=\vec F[/tex]. This means

[tex]\dfrac{\partial f}{\partial x}=x^2+y[/tex]

[tex]\dfrac{\partial f}{\partial y}=y^2+x[/tex]

[tex]\dfrac{\partial f}{\partial z}=ze^z[/tex]

Integrating both sides of the latter equation with respect to [tex]z[/tex] tells us

[tex]f(x,y,z)=e^z(z-1)+g(x,y)[/tex]

and differentiating with respect to [tex]x[/tex] gives

[tex]x^2+y=\dfrac{\partial g}{\partial x}[/tex]

Integrating both sides with respect to [tex]x[/tex] gives

[tex]g(x,y)=\dfrac{x^3}3+xy+h(y)[/tex]

Then

[tex]f(x,y,z)=e^z(z-1)+\dfrac{x^3}3+xy+h(y)[/tex]

and differentiating both sides with respect to [tex]y[/tex] gives

[tex]y^2+x=x+\dfrac{\mathrm dh}{\mathrm dy}\implies\dfrac{\mathrm dh}{\mathrm dy}=y^2\implies h(y)=\dfrac{y^3}3+C[/tex]

So the scalar potential function is

[tex]\boxed{f(x,y,z)=e^z(z-1)+\dfrac{x^3}3+xy+\dfrac{y^3}3+C}[/tex]

By the fundamental theorem of calculus, the work done by [tex]\vec F[/tex] along any path depends only on the endpoints of that path. In particular, the work done over the line segment (call it [tex]L[/tex]) in part (a) is

[tex]\displaystyle\int_L\vec F\cdot\mathrm d\vec r=f(4,0,4)-f(4,0,0)=\boxed{1+3e^4}[/tex]

and [tex]\vec F[/tex] does the same amount of work over both of the other paths.

In part (b), I don't know what is meant by "df/dt for F"...

In part (c), you're asked to find the work over the 2 parts (call them [tex]L_1[/tex] and [tex]L_2[/tex]) of the given path. Using the fundamental theorem makes this trivial:

[tex]\displaystyle\int_{L_1}\vec F\cdot\mathrm d\vec r=f(0,0,0)-f(4,0,0)=-\frac{64}3[/tex]

[tex]\displaystyle\int_{L_2}\vec F\cdot\mathrm d\vec r=f(4,0,4)-f(0,0,0)=\frac{67}3+3e^4[/tex]

Answer:

[tex]H_{0}: \mu \leq 63500\\H_A: \mu > 63500[/tex]

Step-by-step explanation:

We are given the following in the question:

The owner of a football team claims that the average attendance at games is over 63,500.

He wants to justify that the team needs to be moved to a larger stadium outside the city.

If the attendance is larger than 63,500 the team would be moved to a larger stadium and if it is less than or equal to 63,500 that it would not.

Thus, the null and alternate hypothesis will be designed as:

[tex]H_{0}: \mu \leq 63500\\H_A: \mu > 63500[/tex]

The null hypothesis says that the average attenders is equal to or less than 63,500 and alternate supports the claim that the attenders average is greater than 63,500.

Answer:

There is a 26.39% probability that the coin comes up heads and the die comes up less than 4 and the roulette wheel comes up with a number greater than 17.

Step-by-step explanation:

We have to find the probability of the three separate events, and then multiply them.

coin comes up heads

There is a [tex]\frac{1}{2}[/tex] probability that the coin comes up heads.

the die comes up less than 4

The are 15 sides on the die, from 0 to 14.

The values that satisfy us are 0,1,2,3.

So the probability is [tex]\frac{4}{15}[/tex].

the roulette wheel comes up with a number greater than 17

There roulette wheel can come from 1 to 36. There are 19 values greater than 17. So this probability is [tex]\frac{19}{36}[/tex]

What is the probability that the coin comes up heads and the die comes up less than 4 and the roulette wheel comes up with a number greater than 17 ?

[tex]P = \frac{1}{2}*\frac{4}{15}*\frac{19}{36} = 0.2639[/tex]

There is a 26.39% probability that the coin comes up heads and the die comes up less than 4 and the roulette wheel comes up with a number greater than 17.

The combined probability of the coin landing on heads, the die rolling less than 4, and the roulette wheel landing on a number greater than 17 is found by multiplying the individual probabilities of each event.

The question involves calculating the combined probability of independent events in a probability experiment: flipping a coin, rolling a 15-sided die, and spinning a roulette wheel. Each event's probability is calculated separately and then multiplied together to find the probability of all events occurring simultaneously.

The probability the coin comes up heads is 0.5, because a fair coin has two sides and the event has a theoretical probability of 1 in 2. When rolling a 15-sided die, the probability it comes up less than 4 (that is, obtaining a 1, 2, or 3) is 3 out of 15, as there are 3 favorable outcomes out of 15 possible outcomes. And for the roulette wheel, assuming it has numbers from 0 to 36, the probability of spinning a number greater than 17 is 19 out of 38, since there are 19 numbers from 18 to 36 (excluding 0 and 00 which might also be present).

To find the combined probability, you would multiply these probabilities together:

Combined Probability = Probability(Heads) * Probability(Die < 4) * Probability(Roulette > 17)
= 0.5 * (3/15) * (19/38)

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Answer:

13,8%

Step-by-step explanation:

There are six employees and six cheks, so there are 36 (6x6) possible combinations so if we need to measure the probability that five of them receive the exact check  is only one for each one of them over the 36 possibilities, so 1/36 for one plus 1/36 the second and so on.  5/36 = 13,8%.

Answer:

0

Step-by-step explanation:

If 5 of them are correct, then the 6th one will also be correct. Thus, it is impossible to have exactly 5 correct, therefore 0

Answer:

Step-by-step explanation:

Answer:

We conclude that the storm is not increasing above the severe rating.

Step-by-step explanation:

We are given the following in the question:

Population mean, μ = 16.4 feet

Sample mean, [tex]\bar{x}[/tex] = 17.3 feet

Sample size, n = 34

Alpha, α = 0.01

Population standard deviation, σ = 3.5 feet

First, we design the null and the alternate hypothesis

[tex]H_{0}: \mu = 16.4\text{ feet}\\H_A: \mu > 16.4\text{ feet}[/tex]

We use One-tailed(right) z test to perform this hypothesis.

Formula:

[tex]z_{stat} = \displaystyle\frac{\bar{x} - \mu}{\frac{\sigma}{\sqrt{n}} }[/tex]

Putting all the values, we have

[tex]z_{stat} = \displaystyle\frac{17.3 - 16.4}{\frac{3.5}{\sqrt{34}} } = 1.49[/tex]

Now, [tex]z_{critical} \text{ at 0.01 level of significance } = 2.33[/tex]

Since,  

[tex]z_{stat} < z_{critical}[/tex]

We fail to reject the null hypothesis and accept null hypothesis. Thus, the  the storm is not increasing above the severe rating.

The level of significance is 0.01 and the sample test statistic is 1.30.

The level of significance is given as ? = 0.01.

To test if the storm is increasing above the severe rating, we can calculate the z-score.

The formula for the z-score is: z = (x - ?) / (σ / √n), where x is the sample mean, ? is the population mean, σ is the population standard deviation, and n is the sample size.

In this case, x = 17.3 feet, ? = 16.4 feet, σ = 3.5 feet, and n = 34 waves.

Plugging in these values into the formula, we get: z = (17.3 - 16.4) / (3.5 / √34) ≈ 1.30 (rounded to two decimal places).

The sample test statistic is 1.30.

https://brainly.com/question/13077021

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Answer:

False.

Step-by-step explanation:

Hello!

29.9% is the proportion of adults aged 20 and over that were obese in January - September 2014.

This percentage was obtained from a sample of 27 787 adults, it's not a parameter but an estimation of the population proportion obtained from this sample.

I hope it helps!

False, the 29.9% does not represent a parameter for the specific population of 27,787 adults surveyed in the National Health Interview Survey (NHIS).

The 29.9% figure representing U.S. adults aged 20 and over who were estimated to be obese based on the NHIS data is a statistic, not a parameter of the surveyed 27,787 adults, as it aims to generalize the obesity rate to the broader U.S. adult population.

Instead, this percentage is a statistic representing an estimate of the entire U.S. adult population aged 20 and over, based on the sample surveyed.

In statistics, a parameter refers to a numerical measure that describes a characteristic of a population, whereas a statistic is a numerical measure that describes a characteristic of a sample from the population.

Therefore, since the 29.9% figure estimated from the NHIS sample aims to generalize to the broader U.S. adult population, it is classified as a statistic, and not a parameter of the 27,787 adults directly surveyed.

Answer:

Part A: The population are the American adult citizens, excluding the ones from Alaska and Hawaii. The population is the people which the sample is trying to represent, as a hole.

The sample is a portion of this population, and in this case is represented by a randomly selected amount of people whose response to the interview has been selected.

Part B: The sample here has been selected in two steps. The first step is the one that we must pay attention to: the numbered list of one million responses from around the US (excluding Alaska and Hawaii). Because these responses were obtained by an online survey, the sample looks like a convenience sampling, as it depends on the availability and willingness from participants to take part of the study (is not compulsory for everyone, so not everyone is going to response, then there are people that is not going to be represented by). The second step is the random selection of a part of the previous responses. This last part will ensure that, the individuals that took part of the group that was interviewed, are well represented in the results.

Part C: As it was mentioned, there is a selection bias, because the information from the sample comes from a specific group of people that has certain features that may not represent all American adults citizens. For example, the opinion of  those people who do not use internet, will not be considered (and they may be a large number of persons). This situations weaken the conclusions obtained in the study, as they are not representative of the hole population.

Answer:

  see below

Step-by-step explanation:

The definition of a parallelogram is that opposite sides of the quadrilateral are parallel. Felice already knows one pair of opposite sides is parallel. By showing the slope of RQ is the same as the slope of ST, she can show the other pair of opposite sides is parallel, hence the figure is a parallelogram.

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The other answer choices are essentially nonsense.

Answer:

Top Right

Step-by-step explanation:

Answer:

Step-by-step explanation:

Given that Advandia is a drug used to treat diabetes, but it may cause an increase in heart attacks among a population already susceptible.

Sample size n =1`456

Sample proportion of persons having heart attack p1= 27/1456 = 0.0186

II sample

Size = 2895

p2 = sample proportion = 41/2895 = 0.0142

a) Not matched pair data but independent proportions

b)

Step 1:  [tex]H_0: p_1=p_2\\H_a: p_1 > p_2[/tex]

(right tailed test at 5% significance level)

Step 2:  Test statistic z = 1.0995

Step 3:  p value = 0.13567

Step 4:  Since p >0.05 accept null hypothesis.

There is no evidence to suggest that there is a significant increase in the proportion of people who suffer heart attacks when using Avandia compared to other treatments.

Step 5: