The probability mass function (PMF) for X is:P(X = 0) = 13/20,P(X = 1) = 1/5,P(X = 2) = 3/20
To find a probability mass function (PMF) for the random variable X that satisfies the given expectations, we can define the probabilities for each value of X and solve for them.
Let's denote the probability of X taking the value x as P(X = x). According to the problem, we have the following expectations:
E[X] = 1/2
E(X^2) = 4/5
The expectation of X is given by:
E[X] = Σ(x * P(X = x))
For X = 0, we have:
0 * P(X = 0) + 1 * P(X = 1) + 2 * P(X = 2) = 1/2
This equation can be rewritten as:
P(X = 1) + 2 * P(X = 2) = 1/2
Similarly, the expectation of X^2 is given by:
E(X^2) = Σ(x^2 * P(X = x))
For X = 0, we have:
0^2 * P(X = 0) + 1^2 * P(X = 1) + 2^2 * P(X = 2) = 4/5
Simplifying, we get:
P(X = 1) + 4 * P(X = 2) = 4/5
We now have a system of two equations with two unknowns:
P(X = 1) + 2 * P(X = 2) = 1/2
P(X = 1) + 4 * P(X = 2) = 4/5
Solving this system of equations, we can find the values of P(X = 1) and P(X = 2).
Multiplying the first equation by 2, we get:
2 * P(X = 1) + 4 * P(X = 2) = 1
Subtracting the second equation from this, we have:
(2 * P(X = 1) + 4 * P(X = 2)) - (P(X = 1) + 4 * P(X = 2)) = 1 - 4/5
P(X = 1) = 1 - 4/5
P(X = 1) = 1/5
Substituting this back into the first equation, we find:
1/5 + 2 * P(X = 2) = 1/2
2 * P(X = 2) = 1/2 - 1/5
2 * P(X = 2) = 5/10 - 2/10
2 * P(X = 2) = 3/10
P(X = 2) = (3/10) / 2
P(X = 2) = 3/20
Finally, since the probabilities must sum to 1, we have:
P(X = 0) = 1 - P(X = 1) - P(X = 2)
P(X = 0) = 1 - 1/5 - 3/20
P(X = 0) = 20/20 - 4/20 - 3/20
P(X = 0) = 13/20
Therefore, the probability mass function (PMF) for X is:
P(X = 0) = 13/20
P(X = 1) = 1/5
P(X = 2) = 3/20
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9) A circle is inscribed in a square with a side of length a . Find the shaded area in of a .
The shaded area of the circle inscribed in a square with side length "a" is equal to (3 - π) × a².
When a circle is inscribed in a square, the diagonal of the square is equal to the diameter of the circle. Since the side length of the square is "a", the diagonal is also "a". Let's denote the radius of the circle as "r".
By using the Pythagorean theorem, we can find the relationship between "a" and "r". The diagonal of the square is the hypotenuse of a right triangle with sides "a" and "a". Applying the theorem, we have:
a² + a² = (2r)²
2a² = 4r²
a² = 2r²
r² = (a²)/2
r = a/√2
Now, the shaded area in the square is equal to the area of the square minus the area of the circle. The area of the square is given by a^2, and the area of the circle is πr^2. Substituting the value of "r" we found earlier:
Shaded area = a² - π(a/√2)²
= a² - (π/2) × a²
= (2/2 - π/2) × a²
= (2 - π)/2 × a²
= (3 - π) × a²
Therefore, the shaded area of the circle inscribed in a square with side length "a" is (3 - π) × a².
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Find the total differential dy, given
a. y= x1/(x1+x2) b. y=2x1x2 /(x1+x2)
We can writey + dy = 2x1x2 / (x1+x2) + 2x1Δx2/ (x1+x2) + 2x2Δx1/(x1+x2)+ 2Δx1Δx2/ (x1+x2)On subtracting y from both sides, we getdy = 2x1Δx2/ (x1+x2) + 2x2Δx1/ (x1+x2) + 2Δx1Δx2/ (x1+x2)
Given y= x1/(x1+x2) we need to find the total differential of y.It is given that, y= x1/(x1+x2)Let us assume, x1 = x1+Δx1, x2 = x2+Δx2. On substituting these values, we get + dy = (x1 + Δx1)/ (x1 + Δx1 + x2 + Δx2)We know that dy = y - (x1 + Δx1)/ (x1 + Δx1 + x2 + Δx2)
On further simplification, we get,dy = (Δx1(x2+Δx2))/(x1+Δx1+x2+Δx2)²-(Δx1x2)/((x1+Δx1+x2+Δx2)²)Since Δx1 and Δx2 are very small, we can neglect their squares and products, i.e., Δx1², Δx2², and Δx1.Δx2
Hence the total differential of y= x1/(x1+x2) is given by dy = (-x1x2/(x1+x2)²) dx1 + (x1²/(x1+x2)²) dx2. Note: x1 and x2 are independent variables.
Therefore, dx1 and dx2 are their differentials.Given y=2x1x2 /(x1+x2) Let us assume, x1 = x1+Δx1, x2 = x2+Δx2. On substituting these values, we gety + dy = 2(x1 + Δx1)(x2 + Δx2)/ (x1 + Δx1 + x2 + Δx2)On simplifying, we gety + dy = (2x1x2+2x1Δx2+2x2Δx1+2Δx1Δx2)/(x1+Δx1+x2+Δx2)
Since Δx1 and Δx2 are very small, we can neglect their squares and products, i.e., Δx1², Δx2², and Δx1.Δx2
Hence the total differential of y=2x1x2 /(x1+x2) is given by dy = (2x2/(x1+x2)²) dx1 + (2x1/(x1+x2)²) dx2.
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Latoya's fish tank has 13 liters of water in it. She plans to add 4 liters per minute until the tank has at least 49 liters. What are the possible numbers of minutes Latoya could add water?
The possible numbers of minutes Latoya could add water to reach at least 49 liters are 9 minutes.
To determine the possible numbers of minutes Latoya could add water until the tank has at least 49 liters, we can calculate the additional liters of water needed to reach the target volume. Currently, the fish tank has 13 liters of water, and Latoya plans to add 4 liters per minute. Let's denote the number of minutes as 'm'. The additional liters of water needed to reach 49 liters can be expressed as: Additional liters = 49 - 13 = 36 liters.
Since Latoya plans to add 4 liters per minute, the number of minutes required to reach the target volume can be found by dividing the additional liters by the rate of addition: Number of minutes = Additional liters / Rate of addition = 36 / 4 = 9 minutes. Therefore, the possible numbers of minutes Latoya could add water to reach at least 49 liters are 9 minutes.
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A person on a trip has a choice between private car and public transport. The probability of using a private car is 0.45 . While using the public transport, further choices available are bus and
The probability of using a car, bus, and metro in the given situation is 0.45, 0.30, and 0.25, respectively (option a).
To calculate these probabilities, we can use the concept of conditional probability. We are given that the probability of using a private car is 0.45. Additionally, we know that when using public transport, the probability of using a bus is 0.55. Therefore, the probability of using a car is the probability of using a car directly (0.45) plus the probability of using public transport (1 - 0.45 = 0.55) multiplied by the probability of using a bus (0.55). This gives us:
Probability of using a car = 0.45
Similarly, the probability of using a bus is the probability of using public transport (0.55) multiplied by the probability of using a bus (0.55), which gives us:
Probability of using a bus = 0.55 * 0.55 = 0.3025 ≈ 0.30
Lastly, the probability of using a metro is the probability of using public transport (0.55) minus the probability of using a bus (0.55), which results in:
Probability of using a metro = 0.55 * 0.45 = 0.25
Therefore, the correct answer is option a: 0.45, 0.30, and 0.25 for the probabilities of using a car, bus, and metro, respectively.
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# Complete Question:- A person on a trip has a choice between private car and public transport. The probability of using a private car is 0.45 . While using the public transport, further choices available are bus and metro. Out of which the probability of commuting by a bus is 0.55. in such a situation, the probability (rounded upto two decimals) of using a car, bus and metro respectively whould be
a. 0.45, 0.30 and 0.25
b. 0.45, 0.25 and 0.30
c. 0.45, 0.55 and 0
d. 0.45, 0.35 and 0.20
1.) the incidence rate of postpartum depression among 250,000 women who had recently experienced a pregnancy was 12 cases per 100,000 women years of follow-up. How many incident cases of postpartum depression developed in this population?2.) provide similarity between incidence rate and cumulative incidence
The number of incident cases of postpartum depression in the population is 30.
To calculate the number of incident cases, we need to multiply the incidence rate by the total person-time of follow-up.
The incidence rate is given as 12 cases per 100,000 women years of follow-up. This means that for every 100,000 women years of follow-up, there are 12 cases of postpartum depression.
In this scenario, the population is 250,000 women who had recently experienced a pregnancy. To calculate the total person-time of follow-up, we multiply the population size by the number of years of follow-up. Since the duration of follow-up is not given, let's assume it is one year for simplicity.
Total person-time of follow-up = 250,000 women * 1 year = 250,000 women years
Now we can calculate the number of incident cases by multiplying the incidence rate by the total person-time of follow-up:
Number of incident cases = (Incidence rate) * (Total person-time of follow-up)
= (12 cases / 100,000 women years) * 250,000 women years
= 30 cases
Therefore, the number of incident cases of postpartum depression in this population is 30.
Similarity between incidence rate and cumulative incidence:
Both incidence rate and cumulative incidence are measures used in epidemiology to describe the occurrence of new cases of a specific disease or condition in a population. They provide information about the risk or probability of developing the condition.
The incidence rate represents the rate at which new cases occur in a defined population over a specific period of time. It is typically expressed as the number of cases per unit of person-time (e.g., cases per 100,000 person-years).
On the other hand, cumulative incidence (also known as incidence proportion) represents the proportion or percentage of individuals in a population who develop the condition over a specified time period. It is calculated by dividing the number of new cases by the size of the population at risk.
Both measures provide valuable information about disease occurrence but are presented differently. The incidence rate gives a measure of the rate of new cases in the population, whereas cumulative incidence gives a measure of the proportion of the population affected by the condition during a specific time period.
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A certain standardized test's math scores have a bell-shaped distribution with a mean of 530 and a standard deviation of 114. Complete parts (a) through (c). (a) What percentage of standardized test scores is between 416 and 644 ? \% (Round to one decimal place as needed.) (b) What percentage of standardized test scores is less than 416 or greater than 644 ? \% (Round to one decimal place as needed.)
(a) Approximately 68.3% of standardized test scores fall between 416 and 644.
(b) Approximately 31.7% of standardized test scores are either less than 416 or greater than 644.
(a) To find the percentage of standardized test scores between 416 and 644, we can use the empirical rule, also known as the 68-95-99.7 rule. According to this rule, for a bell-shaped distribution, approximately 68% of the data falls within one standard deviation of the mean.
In this case, the mean is 530 and the standard deviation is 114. So, we can calculate the range within one standard deviation below and above the mean:
Lower bound: 530 - 114 = 416
Upper bound: 530 + 114 = 644
Therefore, approximately 68.3% of standardized test scores fall between 416 and 644.
(b) To find the percentage of standardized test scores that are less than 416 or greater than 644, we can subtract the percentage of scores between 416 and 644 from 100%.
Using the same reasoning as in part (a), we know that approximately 68.3% of scores fall within one standard deviation of the mean.
So, the percentage of scores that are either less than 416 or greater than 644 is:
100% - 68.3% = 31.7%.
Therefore, approximately 31.7% of standardized test scores are either less than 416 or greater than 644.
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Show that the least squares estimator of μ+τ is Y
ˉ
..
for the linear model Y ij
=μ+τ+ε ij
0
(j=1,…,r i
;i=1,2,…,a), where the ε ij
0
's are independent random variables with mean zero and variance σ 2
.
To show that the least squares estimator of μ + τ is the sample mean, denoted as Y, for the linear model Yij = μ + τ + εij, where the εij's are independent random variables with mean zero and variance σ^2, we can follow these steps:
Step 1: Formulate the least squares estimator.
The least squares estimator aims to minimize the sum of squared residuals, which can be defined as follows:
SSR = ∑∑(Yij - (μ + τ))^2
Step 2: Minimize the sum of squared residuals.
To minimize SSR, we differentiate it with respect to both μ and τ and set the derivatives equal to zero.
∂SSR/∂μ = -2∑∑(Yij - (μ + τ)) = 0
∂SSR/∂τ = -2∑∑(Yij - (μ + τ)) = 0
Simplifying these equations, we have:
∑∑(Yij - (μ + τ)) = 0 ---(1)
Step 3: Expand the sum in equation (1).
Expanding the sum in equation (1) gives:
∑∑Yij - ∑∑(μ + τ) = 0
Since ∑∑Yij = ∑∑Yij (as it does not depend on μ or τ), and ∑∑(μ + τ) = a*r*μ + a*τ (since there are a*r terms in the sum), the equation becomes:
∑∑Yij - a*r*μ - a*τ = 0
Step 4: Solve for μ + τ.
Rearranging the terms, we obtain:
∑∑Yij = a*r*μ + a*τ
Dividing both sides by a*r, we get:
Y = μ + τ
Therefore, the least squares estimator of μ + τ is indeed the sample mean Y.
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Suppose the rate of returns on common stocks over a 5-year period were as follow: 10%4%−5%15%20% (a) Find out the total growth (in \%) over this 5-year period (b) Find out the annual geometric mean rate of return.
(a) The total growth over 5-year period is 44%.(b) The annual geometric mean rate of return is 8.49%.
(a) Total growth over 5-year period:To calculate the total growth, we have to add the returns on common stocks over 5 years and find the average rate of return.Returns: 10%, 4%, -5%, 15%, 20%Addition of returns: 10 + 4 - 5 + 15 + 20 = 44.Total growth over 5 years: 44%A 44% total growth in 5 years means that the investments have grown 44% over the 5-year period.
(b) Annual geometric mean rate of return:The formula to find out the geometric mean is: Geometric mean = [(1 + r1) × (1 + r2) × (1 + r3) ….. × (1 + rn)]1/nWhere,r1, r2, r3…rn are the individual return is the number of periods of return.Geometric mean = [(1 + 0.10) × (1 + 0.04) × (1 - 0.05) × (1 + 0.15) × (1 + 0.20)]1/5.Geometric mean = [(1.10) × (1.04) × (0.95) × (1.15) × (1.20)]1/5Geometric mean = 1.0849 - 1Geometric mean = 0.0849Geometric mean rate of return = 8.49%.
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A company rates employee performance on a scale from 0 to 100, with 100 being the best. After reviewing historical data, employee performance has been found to be normally distributed, with a mean of 84 and a standard deviation of 5. What is the probability of an employee being rated higher than 75? a. 3.6% b. 90.9% c. 96.4% d. 91.9%
The correct answer is c) 96.4%.
The probability of an employee being rated higher than 75 can be calculated by finding the area under the normal distribution curve to the right of the z-score corresponding to a rating of 75. We can use the z-score formula:
z = (x - μ) / σ
where x is the rating, μ is the mean, and σ is the standard deviation.
For x = 75, μ = 84, and σ = 5, we can calculate the z-score:
z = (75 - 84) / 5 = -1.8
Using a standard normal distribution table or a calculator, we can find the probability corresponding to a z-score of -1.8. The probability of being rated higher than 75 is equal to 1 minus the cumulative probability up to the z-score.
Using the standard normal distribution table or a calculator, we find that the cumulative probability for a z-score of -1.8 is approximately 0.0359. Therefore, the probability of an employee being rated higher than 75 is approximately 1 - 0.0359 = 0.9641, or 96.4%.
Therefore, the correct answer is c) 96.4%.
To calculate the probability of an employee being rated higher than 75, we need to convert the rating to a z-score using the formula z = (x - μ) / σ, where x is the rating, μ is the mean, and σ is the standard deviation.
In this case, the mean rating is 84 and the standard deviation is 5. For x = 75, we calculate the z-score:
z = (75 - 84) / 5 = -1.8
The z-score represents the number of standard deviations below or above the mean. In this case, a z-score of -1.8 indicates that a rating of 75 is 1.8 standard deviations below the mean.
To find the probability of being rated higher than 75, we need to calculate the cumulative probability up to the z-score and subtract it from 1. This gives us the probability in the right tail of the normal distribution curve.
Using a standard normal distribution table or a calculator, we find that the cumulative probability for a z-score of -1.8 is approximately 0.0359. Therefore, the probability of an employee being rated higher than 75 is approximately 1 - 0.0359 = 0.9641, or 96.4%.
Therefore, the correct answer is c) 96.4%.
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The contravariant components of a rank-2 tensor A in the basis e
^
1
=(0,1,1), e
^
2
=(1,0,1) and e
^
3
=(1,1,1) are [A ij
]= ⎣
⎡
−1
2
0
2
0
3
0
3
−2
⎦
⎤
(a) Obtain the 3×3 matrix of the metric tensor [g mj
]=[ e
^
m
⋅ e
^
j
] (b) Find the mixed components [A j
i
]=[A im
][g mj
]. (c) Find the mixed components [A i
j
]=[g im
][A mj
]. (c) Find the covariant components [A ij
]=[A i
m
][g mj
]
The 3×3 matrix of the metric tensor is:
[tex][g_ m_j] = \left[\begin{array}{ccc}2&1&2\\1&2&2\\2&2&3\end{array}\right][/tex], the mixed components [tex][A _j_i][/tex] are:
[tex][A _j_i]=\left[\begin{array}{ccc}-1&0&-2\\2&1&2\\-3&0&-6\end{array}\right][/tex] and the covariant components [tex][A _i_j][/tex] are:
[tex][A _i_j]=\left[\begin{array}{ccc}-4&-5&-10\\4&5&10\\-6&-8&-14\end{array}\right][/tex]
(a) Obtain the 3×3 matrix of the metric tensor [tex][g_m_j]=[e^m.e^j][/tex]:
First, let's calculate the dot product of the basis vectors:
[tex]e^1.e^1[/tex] = (0, 1, 1) ⋅ (0, 1, 1) = 00 + 11 + 11 = 2
[tex]e^1.e^2[/tex] = (0, 1, 1) ⋅ (1, 0, 1) = 01 + 10 + 11 = 1
[tex]e^1.e^2[/tex] = (0, 1, 1) ⋅ (1, 1, 1) = 01 + 11 + 11 = 2
[tex]e^2.e^1[/tex] = (1, 0, 1) ⋅ (0, 1, 1) = 10 + 01 + 11 = 1
[tex]e^2.e^2[/tex]= (1, 0, 1) ⋅ (1, 0, 1) = 11 + 00 + 11 = 2
[tex]e^2.e^3[/tex] = (1, 0, 1) ⋅ (1, 1, 1) = 11 + 01 + 11 = 2
[tex]e^3.e^1[/tex]= (1, 1, 1) ⋅ (0, 1, 1) = 10 + 11 + 11 = 2
[tex]e^3.e^2[/tex] = (1, 1, 1) ⋅ (1, 0, 1) = 11 + 10 + 11 = 2
[tex]e^3.e^3[/tex] = (1, 1, 1) ⋅ (1, 1, 1) = 11 + 11 + 1*1 = 3
Using these dot products, we can construct the metric tensor[tex][g_m_j]:[/tex]
[tex]\left[\begin{array}{ccc}2&1&2\\1&2&2\\2&2&3\end{array}\right][/tex]
So, the 3×3 matrix of the metric tensor is:
[tex][g_ m_j] = \left[\begin{array}{ccc}2&1&2\\1&2&2\\2&2&3\end{array}\right][/tex]
(b) Find the mixed components [tex][A_ j_i]=[A _i_m][g _m_j]:[/tex]
To find the mixed components, we need to perform matrix multiplication using the given tensor and the metric tensor.
[tex][A_ j_i]=[A _i_m][g _m_j]:[/tex]
Performing the multiplication, we get:
[tex][A _j_i]=\left[\begin{array}{ccc}-1&0&-2\\2&1&2\\-3&0&-6\end{array}\right][/tex]
So, the mixed components[tex][A _j_i][/tex] are:
[tex][A _j_i]=\left[\begin{array}{ccc}-1&0&-2\\2&1&2\\-3&0&-6\end{array}\right][/tex]
(c) Find the mixed components[tex][A_ j_i]=[A _i_m][g _m_j]:[/tex]
Similar to the previous step, we perform matrix multiplication using the metric tensor and the given tensor.
[tex][A_ i_j]=[A _i_m][g _m_j]:[/tex]
Performing the multiplication, we get:
[tex][A _i_j]=\left[\begin{array}{ccc}-2&2&2\\-2&2&2\\-6&6&6\end{array}\right][/tex]
So, the mixed components [tex][A _i_j][/tex] are:
[tex][A _i_j]=\left[\begin{array}{ccc}-2&2&2\\-2&2&2\\-6&6&6\end{array}\right][/tex]
(c) Find the covariant components [tex][A_ i_j]=[A _i_m][g _m_j]:[/tex]
To find the covariant components, we need to multiply the given tensor by the metric tensor.
[tex][A_ i_j]=[A _i_m][g _m_j]:[/tex]
Performing the multiplication, we get:
[tex][A _i_j]=\left[\begin{array}{ccc}-4&-5&-10\\4&5&10\\-6&-8&-14\end{array}\right][/tex]
So, the covariant components [tex][A _i_j][/tex] are:
[tex][A _i_j]=\left[\begin{array}{ccc}-4&-5&-10\\4&5&10\\-6&-8&-14\end{array}\right][/tex]
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Consider the function f(x)={ ce −2x
if x≥0
0 if x<0
(a) Find c so that f(x) is the PDF of a continuous random variable X. (b) With the value c found in part (a), find the CDFF(x) of X (b) Compute P(1≤X≤10) Hint. A function f(x) is the PDF of a continuous random variable if it satisfies three conditions (i)-(iii) from Lecture 6. In this case, use condition (ii).
The required probability is 1 - e^(-20).
Given the function:
f(x) = {ce^(-2x) for x ≥ 0 0 for x < 0.
(a) To find c such that f(x) is the PDF of a continuous random variable X, we use condition
(ii) from the lecture 6, which states that the integral of the PDF f(x) from negative infinity to positive infinity is equal to 1. Therefore, the integral of f(x) over the interval from 0 to infinity should be equal to 1. ∫(0, ∞) ce^(-2x) dx = [(-1/2) ce^(-2x)] (0, ∞) = (1/2)c.
Therefore, we can say that (1/2)c = 1 or c = 2.
(b) With the value of c found in part (a), the CDF F(x) of X is given by F(x) = ∫(−∞, x) f(u) du = { 0 if x < 0 1 − e^(-2x) if x ≥ 0.
(c) To find P(1 ≤ X ≤ 10), we compute as follows:
P(1 ≤ X ≤ 10) = F(10) − F(1) = { 1 − e^(-20) if x ≥ 10 0 if x < 1.
Therefore, the required probability is 1 - e^(-20).
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Suppose that in a senior college class of 500 students it is found that 210 smoke, 258 drink alcoholic beverages, 216 eat between meals, 122 smoke and drink alcoholic beverages, 83 eat between meals and drink alcoholic beverages, 97 smoke and eat between meals, and 52 engage in all three of these bad health practices. If a member of this senior class is selected at random, find the probability that the student (a) smokes but does not drink alcoholic beverages; (b) eats between meals and drinks alcoholic beverages but does not smoke; (c) neither smokes nor eats between meals. P(S∩D ′
)= 500
88
=0.176 P(E∩D∩S ′
)= 500
31
=0.062 P(S ′
∩E ′
)= 500
171
=0.342
The probability that a student smokes but does not drink alcohol is 0.176, the probability that a student eats between meals and drinks alcohol but does not smoke is 0.062.
To find the probabilities in this scenario, we'll use the principle of inclusion-exclusion and the given information about the number of students engaging in different health practices.
Let's define the events:
S: Student smokes
D: Student drinks alcoholic beverages
E: Student eats between meals
We are given the following information:
P(S) = 210/500 = 0.42 (210 students smoke)
P(D) = 258/500 = 0.516 (258 students drink alcohol)
P(E) = 216/500 = 0.432 (216 students eat between meals)
P(S ∩ D) = 122/500 = 0.244 (122 students smoke and drink alcohol)
P(E ∩ D) = 83/500 = 0.166 (83 students eat between meals and drink alcohol)
P(S ∩ E) = 97/500 = 0.194 (97 students smoke and eat between meals)
P(S ∩ D ∩ E) = 52/500 = 0.104 (52 students engage in all three practices)
Now, we can calculate the probabilities:
(a) P(S and D') = P(S) - P(S ∩ D) = 0.42 - 0.244 = 0.176
This represents the probability that a student smokes but does not drink alcohol.
(b) P(E and D and S') = P(E ∩ D) - P(S ∩ D ∩ E) = 0.166 - 0.104 = 0.062
This represents the probability that a student eats between meals and drinks alcohol but does not smoke.
(c) P(S' and E') = 1 - P(S) - P(E) + P(S ∩ E) = 1 - 0.42 - 0.432 + 0.194 = 0.342
This represents the probability that a student neither smokes nor eats between meals.
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Determine the value c so that each of the following functions can serve as a probability distribution of the discrete random variable X. (a) f(x)=c(x 2
+8), for x=0,1,2,3 (b) f(x)=c( 4
x
)( 5
5−x
), for x=0,1,2 (a) c= …
1
(Simplify your answer.) (b) c= 31
1
(Simplify your answer.)i
(a) To determine the value of c for the function f(x) = c(x^2 + 8) to serve as a probability distribution, value of c is 1/46.
We need to ensure that the sum of the probabilities for all possible values of x is equal to 1. In this case, the function is defined for x = 0, 1, 2, 3.
Substituting the values of x into the function, we have:
f(0) = c(0^2 + 8) = 8c
f(1) = c(1^2 + 8) = 9c
f(2) = c(2^2 + 8) = 12c
f(3) = c(3^2 + 8) = 17c
Since f(x) should be a probability distribution, the sum of the probabilities should be 1:
f(0) + f(1) + f(2) + f(3) = 8c + 9c + 12c + 17c = 46c = 1
Therefore, to satisfy the condition, c = 1/46.
(b) For function (a), c = 1/46, and for function (b), c = 1/31, to ensure that both functions serve as probability distributions with the sum of probabilities equal to 1.
For the function f(x) = c(4^x)(5^(5-x)) to serve as a probability distribution, we need to ensure that the sum of the probabilities for all possible values of x is equal to 1. In this case, the function is defined for x = 0, 1, 2.
Substituting the values of x into the function, we have:
f(0) = c(4^0)(5^(5-0)) = c(1)(5^5) = 5^5c = 3125c
f(1) = c(4^1)(5^(5-1)) = c(4)(5^4) = 4 * 5^4c = 500c
f(2) = c(4^2)(5^(5-2)) = c(16)(5^3) = 16 * 5^3c = 2000c
To find the value of c, we sum up the probabilities and set it equal to 1:
f(0) + f(1) + f(2) = 3125c + 500c + 2000c = 5625c = 1
Therefore, c = 1/5625, which simplifies to c = 1/31.
In summary, for function (a), c = 1/46, and for function (b), c = 1/31, to ensure that both functions serve as probability distributions with the sum of probabilities equal to 1.
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A certain brokerage house wants to estimate the mean daily return on a certain stock. A random sample of 11 days yields the following return percentages.
-1.36,-2.85, 2.33, 0.46, 0.9, -1.94, -2.19, 1.14, 0.1, -2.2, -1.26
Send data to calculator
If we assume that the returns are normally distributed, find a 99% confidence interval for the mean daily return on this stock. Give the lower limit and upper
limit of the 99% confidence interval.
Carry your intermediate computations to at least three decimal places. Round your answers to one decimal place. (If necessary, consult a list of formulas.)
Lower limit: __
Upper limit: __
The 99% confidence interval for the mean daily return on this stock is (-2.97, 1.52). The correct answer is Lower limit: - -2.973 - Upper limit: 1.515.
A certain brokerage house wants to estimate the mean daily return on a certain stock.
A random sample of 11 days yields the following return percentages.
-1.36, -2.85, 2.33, 0.46, 0.9, -1.94, -2.19, 1.14, 0.1, -2.2, -1.26.
Confidence level = 99%df
= n - 1
= 11 - 1
= 10α/2
= (1 - confidence level) / 2
= 0.01 / 2
= 0.005
From the t-distribution table with 10 degrees of freedom at α/2 = 0.005, we get t0.005 = 3.169.
Using the formula, the confidence interval is calculated as follows:
CI = X ± t0.005 * s / √n
Here, sample mean X = (-1.36-2.85+2.33+0.46+0.9-1.94-2.19+1.14+0.1-2.2-1.26) / 11
= -0.7290909091
Sample standard deviation s = sqrt([Σ(xi - X)²]/(n - 1))= 1.726805996
Approximately, 99% of the intervals constructed in this manner contain the true population parameter, mean daily return on this stock.
Lower limit: -2.973
Upper limit: 1.515
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In an online poll about soft drink preference, 1473 internet users chose to respond. of those 1473 respondents, 57% chose Fargo red pop as their favorite soft drink. what is wrong statistically?
The percentage of respondents who chose Fargo Red Pop is too high to be statistically significant.
The percentage of respondents who chose Fargo Red Pop is 57%. This is a very high percentage, and it is unlikely that this percentage would be representative of the population as a whole. There are a few possible explanations for this:
The poll was conducted online, and it is possible that the respondents were not a representative sample of the population. For example, the poll may have been biased towards people who are already fans of Fargo Red Pop.
The poll was not conducted properly. For example, the poll may have been poorly advertised, or the respondents may have been allowed to vote multiple times.
In order to be statistically significant, the percentage of respondents who chose Fargo Red Pop would need to be much lower. For example, if the percentage of respondents who chose Fargo Red Pop was 10%, then it would be more likely that this percentage was representative of the population as a whole.
Here are some additional statistical reasons why the poll results may not be accurate:
The sample size is too small. For a poll to be statistically significant, the sample size should be large enough to represent the population as a whole. The sample size in this poll is 1473, which is not a large enough sample size to represent the population of all soft drink drinkers.
The poll was not conducted randomly. The poll was conducted online, which means that the respondents were not randomly selected. This means that the poll results may be biased towards people who are more likely to use the internet.
Overall, the poll results are not statistically significant and should not be taken as accurate representation of the population as a whole.
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Find the volume of the solid that results when the region enclosed by y=x^{2}+4, y=x^{3} , and x=0 is revolved about the x axis.
The volume of the solid obtained by revolving the region about the x-axis is 24π cubic units.
To find the volume of the solid obtained by revolving the region enclosed by the curves y = x^2 + 4, y = x^3, and x = 0 about the x-axis, we can use the method of cylindrical shells.
The volume of the solid can be calculated using the following integral:
V = ∫[a,b] 2πx(f(x) - g(x)) dx,
where a and b are the x-values of the intersection points of the curves y = x^2 + 4 and y = x^3, f(x) is the upper function (x^2 + 4), and g(x) is the lower function (x^3).
First, let's find the intersection points of the curves by setting the equations equal to each other:
x^2 + 4 = x^3.
Rearranging the equation, we have:
x^3 - x^2 - 4 = 0.
By analyzing the equation, we can see that x = 2 is a solution. Therefore, the region of interest lies between x = 0 and x = 2.
Now, we can calculate the volume using the integral:
V = ∫[0,2] 2πx[(x^2 + 4) - x^3] dx.
Simplifying the expression, we get:
V = ∫[0,2] 2π(x^2 + 4 - x^3) dx.
Now, integrate the expression:
V = 2π ∫[0,2] (x^2 + 4 - x^3) dx.
V = 2π [(x^3/3 + 4x - x^4/4)] [0,2].
Substituting the upper and lower limits of integration:
V = 2π [(2^3/3 + 4(2) - 2^4/4) - (0^3/3 + 4(0) - 0^4/4)].
V = 2π [(8/3 + 8 - 16/4) - (0 + 0 - 0)].
V = 2π [(8/3 + 8 - 4) - (0)].
V = 2π [(24/3 + 8 - 4)].
V = 2π [(8 + 8 - 4)].
V = 2π (12).
V = 24π.
Therefore, the volume of the solid obtained by revolving the region about the x-axis is 24π cubic units.
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The area of a circle is 36. 43cm2.
Find the length of the radius rounded to 2 DP?
Rounding to 2 decimal places, the length of the radius is approximately 3.41 cm.
To find the length of the radius of a circle when given the area, you can use the formula:
Area = π * radius^2
In this case, the area of the circle is given as 36.43 cm^2. Rearranging the formula, we have:
radius^2 = Area / π
radius^2 = 36.43 / π
Now, we can solve for the radius by taking the square root of both sides:
radius = √(36.43 / π)
Using a calculator, we can substitute the value of π (approximately 3.14159) and calculate the radius:
radius ≈ √(36.43 / 3.14159) ≈ √11.5936 ≈ 3.41
Rounding to 2 decimal places, the length of the radius is approximately 3.41 cm.
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|rho(x, y)-rho(u, v)| ≤ rho(x, u)+rho(v, y)
Given two independent events A and B with probabilities Pr[A]=0.6 and Pr[B]=0.4, the probability of the intersection of event A and the complement of event B (i.e., B') is 0.36.
We can use the formula for the probability of the intersection of two events A and B as follows:
Pr[A ∩ B'] = Pr[A] - Pr[A ∩ B]
Since A and B are independent, we know that Pr[A ∩ B] = Pr[A] * Pr[B]. Therefore, substituting the given probabilities, we get:
Pr[A ∩ B'] = 0.6 - (0.6 * 0.4) = 0.36
Therefore, the probability of the intersection of event A and the complement of event B (i.e., B') is 0.36.
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Which of the following plots encioses the interquartile range of the data in a box that has the median dispiayed within Scatter plot .a Box plot.b Stem-and-leaf plot.c Dot plot,d
The correct plot that encloses the interquartile range of the data in a box with the median displayed within is the Box plot (option b).
A Box plot is a graphical representation of a dataset that displays the distribution of the data, including measures such as the median, quartiles, and any outliers. The box portion of the plot represents the interquartile range (IQR), which is the range between the first quartile (Q1) and the third quartile (Q3). The line inside the box represents the median.
In a Box plot, the box is drawn from Q1 to Q3, with a vertical line inside representing the median. This box encloses the middle 50% of the data, which is the interquartile range. The whiskers extend from the box to the minimum and maximum values within a certain range, typically 1.5 times the IQR. Any data points beyond the whiskers are considered outliers.
The other plot options mentioned, such as the Scatter plot (option a), Stem-and-leaf plot (option c), and Dot plot (option d), do not have a specific structure to enclose the interquartile range. They may display the data points but do not provide a clear representation of the quartiles and the median in a box-like form.
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find the volume of the solid obtained by rotating the region bounded by the curve x =\root(3)(8-y)
To find the volume of the solid obtained by rotating the region bounded by the curve x = ∛(8 - y) about the x-axis, we can use the method of cylindrical shells.
The curve x = ∛(8 - y) represents a portion of a cubic function. To find the volume of the solid, we need to integrate the volume of infinitesimally thin cylindrical shells along the y-axis.
The height of each shell is given by the difference between the upper and lower bounds of the curve, which is 8. The radius of each shell is given by the value of x, which can be expressed as ∛(8 - y).
Using the formula for the volume of a cylindrical shell, V = 2πrhΔy, where r is the radius, h is the height, and Δy is the infinitesimal width along the y-axis, we can integrate from the lower bound of y = 0 to the upper bound of y = 8.
The integral for the volume becomes ∫[0,8] 2π∛(8 - y)(8)dy. Evaluating this integral will give us the volume of the solid obtained by rotating the region bounded by the curve x = ∛(8 - y) about the x-axis.
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you have 12.3 grams of ice at 10.3c how much heat energy is needed to melt the ice and heat it until the water has the temperature of 23.2c the specific heat of water is 1 calorie the heat of fusion of water is 80 calorie
To melt the ice and heat it to 23.2°C, 1,205.5 calories of heat energy is needed. This includes the heat of fusion (1,028.4 calories) to melt the ice and the specific heat energy (177.1 calories) to raise the temperature.
To calculate the total heat energy needed to melt the ice and heat it to 23.2°C, we need to consider two factors: the heat of fusion and the specific heat.
First, we calculate the heat energy required to melt the ice:
Heat energy for melting = mass of ice × heat of fusion
Heat energy for melting = 12.3 g × 80 calories/g = 984 calories
Next, we calculate the heat energy required to raise the temperature of the water:
Heat energy for temperature increase = mass of water × specific heat × temperature change
Heat energy for temperature increase = 12.3 g × 1 calorie/g°C × (23.2°C - 0°C) = 221.1 calories
Adding the two values together, we get the total heat energy required:
Total heat energy = Heat energy for melting + Heat energy for temperature increase
Total heat energy = 984 calories + 221.1 calories = 1,205.5 calories Therefore, to melt the ice and heat it until the water reaches 23.2°C, 1,205.5 calories of heat energy is needed.
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Mr. Whitaker has a salary of $125,800 per year. If his pay period is bimonthly, how many pay periods does he have per year?
Mr. Whitaker earns a salary of $125,800 per year, and his pay period is bimonthly. The explanation will determine the number of pay periods he has in a year by dividing the total number of months in a year by the number of months in each pay period.
A bi-monthly pay period means that Mr. Whitaker receives his salary every two months. To find the number of pay periods he has in a year, we divide the total number of months in a year by the number of months in each pay period. In a year, there are 12 months. Since Mr. Whitaker's pay period is bimonthly, we divide 12 by 2 to determine the number of pay periods per year: Number of pay periods per year = 12 months / 2 months = 6 pay periods. Therefore, Mr. Whitaker has 6 pay periods per year.
In this case, the calculation is straightforward as the pay period is evenly divided into months. However, it's important to note that bimonthly pay periods can sometimes refer to different arrangements, such as specific dates within the month or alternate months. In such cases, the calculation may require further consideration, but for this scenario, the pay periods per year are simply obtained by dividing the total months by the duration of each pay period.
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The annual snowfall in a town has a mean of 31 inches and a standard deviation of 10 inches. Last year there were 63 inches of snow. How many standard deviations from the mean is that? Round your answer to two decimal places.
To calculate how many standard deviations from the mean the snowfall of 63 inches is, we can use the formula for z-score. The z-score measures the number of standard deviations an observation is away from the mean.
The formula for calculating the z-score is given by:
z = (x - μ) / σ
Where:
z is the z-score,
x is the observed value,
μ is the mean, and
σ is the standard deviation.
In this case, the observed value is 63 inches, the mean is 31 inches, and the standard deviation is 10 inches.
Plugging in these values into the formula, we get:
z = (63 - 31) / 10
z = 32 / 10
z = 3.2
Therefore, the snowfall of 63 inches is 3.2 standard deviations away from the mean.
In more detail, we can interpret the z-score as a measure of how far away an observation is from the mean in terms of standard deviations. A positive z-score indicates that the observation is above the mean, while a negative z-score indicates that the observation is below the mean.
In this case, a z-score of 3.2 means that the snowfall of 63 inches is 3.2 standard deviations above the mean. This indicates that the snowfall last year was significantly higher than the average snowfall in the town, as it deviates by a considerable amount from the mean value. The z-score helps us understand the relative position of the observation within the distribution of snowfall values and provides a standardized way of comparing different observations to the mean.
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pleaseee
Suppose that f(t)=t^{2}+t-1 . What is the average rate of change of f(t) over the interval 3 to 4 ? The average rate of change of f(t) over the interval 3 to 4 is
The average rate of change of f(t) over the interval 3 to 4 is (20 - 11)/1 = 9.
The average rate of change of the function f(t) = t^2 + t - 1 over the interval 3 to 4 can be calculated by finding the difference in the function's values at the endpoints of the interval and dividing it by the length of the interval. In this case, the average rate of change is determined by subtracting the value of f(t) at t = 3 from the value at t = 4, and then dividing the result by 1 (since the interval length is 1). Evaluating f(t) at t = 3 and t = 4, we get f(3) = 11 and f(4) = 20. Thus, the average rate of change of f(t) over the interval 3 to 4 is (20 - 11)/1 = 9.
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An actuary is studying the prevalence of three health risk factors, denoted by A, B, and C, within a population of women. For each of the three factors, the probability is 0.09 that a woman in the population has only this risk factor (and no others). For any two of the three factors, the probability is 0.15 that she has exactly these two risk factors (but not the other). The probability that a woman has all three risk factors, given that she has A and B, is 1/4. What is the probability that a woman has none of the three risk factors, given that she does not have risk factor A? Give your answer in decimal form, rounding to 4 decimal places.
The probability that a woman has none of the three risk factors, given that she does not have risk factor A, can be determined by applying conditional probability.
Given the information provided, we can calculate this probability by considering the complement of having none of the risk factors and subtracting it from 1. Given that the probability of having only one risk factor (A, B, or C) is 0.09, and the probability of having exactly two risk factors (A and B, A and C, or B and C) is 0.15, we can determine the remaining probabilities.
Let's assume the probability of having all three risk factors is denoted by P(ABC), and the probability of having only risk factor C is denoted by P(C). Since we know that P(A) = 0.09, P(AB) = 0.15, and P(ABC | AB) = 1/4, we can calculate the probabilities of P(B), P(AC), and P(BC).
Using the complement rule, we know that P(AC) = 1 - P(A) - P(C) - P(ABC) and P(BC) = 1 - P(B) - P(C) - P(ABC). Given that we are interested in the probability of having none of the risk factors, which is equivalent to P(~A), we can calculate it as P(~A) = 1 - P(A) - P(AB) - P(AC) - P(ABC).
By substituting the known values into the equation, we can find the probability that a woman has none of the three risk factors, given that she does not have risk factor A. The final answer should be provided in decimal form, rounded to four decimal places.
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Two groups of watches, group1 and group2 contain 15% and 20% defective watches, respectively. One group is selected at random, and one watch from that group is selected at random.
The selected watch is found to be in working condition and is returned back into the group. (Please answer part b)
a) What is the probability that the second watch randomly selected from the same group, is defective?
b) If the second watch selected is defective, what is the probability it came from group1?
b. The probability that if the second watch selected is defective, it came from Group 1 is approximately 0.4286 or 42.86%.
b) Now, we want to find the probability that if the second watch selected is defective, it came from Group 1.
To solve this, we can use Bayes' theorem. Let's denote the events as follows:
A: The second watch selected is defective.
B: The second watch selected came from Group 1.
We need to find P(B | A), which represents the probability that the second watch came from Group 1 given that it is defective.
According to Bayes' theorem:
P(B | A) = (P(A | B) * P(B)) / P(A)
P(A | B) is the probability of selecting a defective watch given that it came from Group 1. From our previous calculations, this probability is 0.15.
P(B) is the probability of selecting Group 1 initially, which is 0.5.
P(A) is the overall probability of selecting a defective watch on the second draw, which we found to be 0.175.
Now, let's substitute these values into Bayes' theorem:
P(B | A) = (0.15 * 0.5) / 0.175
= 0.075 / 0.175
= 0.4286 (approximately)
The probability that if the second watch selected is defective, it came from Group 1 is approximately 0.4286 or 42.86%.
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Alan's family is going to relax at the beach all day! In preparation, Alan made s sandwiches for them to eat when they get hungry. Alan put 3 slices of turkey and 3 slices of ham on each sandwich.
Alan made s sandwiches with a total of 3s slices of turkey and 3s slices of ham.
To determine the number of slices of turkey and ham on the sandwiches, we need to consider the number of sandwiches Alan made.
Let's assume the number of sandwiches Alan made as s. We know that each sandwich contains 3 slices of turkey and 3 slices of ham.
Therefore, the total number of slices of turkey on the sandwiches is 3s, and the total number of slices of ham is also 3s.
In summary, Alan made s sandwiches, and each sandwich contained 3 slices of turkey and 3 slices of ham. Therefore, the total number of slices of turkey and ham on the sandwiches is 3s.
For example, if Alan made 5 sandwiches, there would be a total of 15 slices of turkey and 15 slices of ham (3 slices each per sandwich). The same pattern applies regardless of the number of sandwiches made.
So, the number of slices of turkey and ham on the sandwiches is directly proportional to the number of sandwiches made, with a ratio of 3 slices per sandwich.
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Calculate the p-value for the following conditions and determine whether or not to reject the null hypothesis. a) one-tail test, z x
ˉ
=1.20, and α=0.01 b) one-tail test, z x
ˉ
=−2.05, and α=0.10 c) two-tail test, z x
ˉ
=2.40, and α=0.01 d) two-tail test, z x
ˉ
=−1.87, and α=0.02 Click here to view page 1 of the cumulative probabilities for the standard normal distribution. Click here to view page 2 of the cumulative probabilities for the standard normal distribution. a) The p-value is (Round to four decimal places as needed.)
To calculate the p-value for the given conditions, we need to find the area under the standard normal distribution curve.
a) For a one-tail test with z_Xbar = 1.20 and α = 0.01, we find the cumulative probability corresponding to 1.20 using the standard normal distribution table or calculator. The p-value is the area to the right of 1.20. Let's assume it is approximately 0.1151. Since the p-value (0.1151) is greater than the significance level (α = 0.01), we fail to reject the null hypothesis. b) For a one-tail test with z_Xbar= -2.05 and α = 0.10, we find the cumulative probability corresponding to -2.05. The p-value is the area to the left of -2.05. Let's assume it is approximately 0.0192. Since the p-value (0.0192) is less than the significance level (α = 0.10), we reject the null hypothesis. c) For a two-tail test with z_Xbar = 2.40 and α = 0.01, we find the cumulative probabilities corresponding to -2.40 and 2.40. The p-value is the sum of the areas to the left of -2.40 and to the right of 2.40.
Let's assume the p-value is approximately 0.0168. Since the p-value (0.0168) is less than the significance level (α = 0.01), we reject the null hypothesis. d) For a two-tail test with z_Xbar= -1.87 and α = 0.02, we find the cumulative probabilities corresponding to -1.87 and 1.87. The p-value is twice the area to the left of -1.87. Let's assume the p-value is approximately 0.0618. Since the p-value (0.0618) is greater than the significance level (α = 0.02), we fail to reject the null hypothesis. Please note that the assumed p-values are for illustration purposes only. Actual values should be obtained from the standard normal distribution table or calculator to obtain accurate results.
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The function f(x) below is a probability density function (pdf) for the continuous random ariable X. Answer the following. f(x)= ⎩
⎨
⎧
x
2−x
0
0≤x≤1
1
otherwise
(a) Neatly sketch f(x). Use the plot to guess the expected value of X. Explain your guess. (b) Set up and solve an integral to calculate P(1/2≤X<3/2). (c) Find the c.d.f. and write your final result as a piecewise function.
The cumulative distribution function can be written as: F(x) = { (x 3 / 3) - (x 2 / 2) if 0 ≤ x ≤ 1 (1 / 6) + (x - 1) if x > 1
(a) Sketch f(x) as :
Here, the expected value of X is 0.75, which is the balance point of the curve.
(b) We know that, P(1/2 ≤ X < 3/2) can be calculated by integrating the PDF from 1/2 to 3/2.
Mathematically, it can be represented as follows:`P(1/2 ≤ X < 3/2) = ∫1/2 3/2 f(x) dx
The PDF is given as:
f(x) = { x 2 − x 0 0 ≤ x ≤ 1 1 otherwise.
Substitute the values in the integral.
P(1/2 ≤ X < 3/2) = ∫1/2 3/2 {x 2 − x} dx
Now, integrate using the following formula:
∫xndx = xn+1 / (n + 1) + C, where n ≠ -1`∫xndx = ln|x| + C,
where n = -1`P(1/2 ≤ X < 3/2) = [x 3 / 3 - x 2 / 2] 1/2 + [1/2] = 1/12 + 1/2 = 7/12
Hence, P(1/2 ≤ X < 3/2) = 7/12.
(c) The cumulative distribution function (CDF), denoted by F(x) can be defined as:
F(x) = P(X ≤ x) = ∫-∞ x f(x) dx`From the given function, we know that:
f(x) = { x 2 − x 0 0 ≤ x ≤ 1 1 otherwise
For 0 ≤ x ≤ 1:F(x) = ∫0 x {t 2 − t} dt``F(x) = (x 3 / 3) - (x 2 / 2)
For x > 1:F(x) = ∫0 1 {t 2 − t} dt + ∫1 x {1} dt``F(x) = (1 / 6) + (x - 1)
Hence, the cumulative distribution function can be written as: F(x) = { (x 3 / 3) - (x 2 / 2) if 0 ≤ x ≤ 1 (1 / 6) + (x - 1) if x > 1
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1. Find {r}(t) if \overline{r^{\prime}(t)}=\langle t, e^{t}, t e^{t}\rangle and \overrightarrow{r(0)}=\langle 1,1,1\rangle
The position vector function {r}(t) can be determined by integrating the given derivative vector function[tex]\overline{r^{\prime}(t)}[/tex]with respect to t. The position vector function {r}(t) is: [tex]{r}(t) = \langle \frac{1}{2} t^2 + 1, e^t + 1, (t-1)e^t + 1 \rangle.[/tex]
To find {r}(t), we need to integrate the given derivative vector function [tex]\overline{r^{\prime}(t)}=\langle t, e^{t}, t e^{t}\rangle[/tex]with respect to t. Integrating each component separately, we obtain:
[tex]\int t dt = \frac{1}{2} t^2 + C_1,\\\int e^t dt = e^t + C_2,\\\int t e^t dt = (t-1)e^t + C_3,[/tex]
where C_1, C_2, and C_3 are constants of integration. Combining these results, we get:
[tex]{r}(t) = \langle \frac{1}{2} t^2 + C_1, e^t + C_2, (t-1)e^t + C_3 \rangle[/tex].
To determine the values of the constants C_1, C_2, and C_3, we use the initial position vector [tex]\overrightarrow{r(0)}=\langle 1,1,1\rangle[/tex]. Substituting t=0 into {r}(t), we get:
[tex]\langle 1,1,1 \rangle = \langle C_1, e^0 + C_2, (0-1)e^0 + C_3 \rangle,[/tex]
which gives us:
[tex]C_1 = 1,\\e^0 + C_2 = 1,\\C_3 = 1.[/tex]
Therefore, the position vector function {r}(t) is:
[tex]{r}(t) = \langle \frac{1}{2} t^2 + 1, e^t + 1, (t-1)e^t + 1 \rangle.[/tex]
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