The probability that the average weight is less than 170 g is 0.5. In repeated samples (n=36), the heaviest 33% of the average weights are over 163.92 g.
Sampling distribution refers to the probability distribution of a statistic gathered from random samples of a specific size taken from a given population. It is computed for all sample sizes from the population.
It is essential to estimate and assess the properties of population parameters by analyzing these distributions.
To find the mean and standard deviation of the sampling distribution of the sample mean, the formulas used are:
The mean of the sampling distribution of the sample mean = μ = mean of the population = 170 g
The standard deviation of the sampling distribution of the sample mean is σx = (σ/√n) = (18/√36) = 3 g
The central limit theorem (CLT) is a theorem used to find the probabilities above. It states that, under certain conditions, the mean of a sufficiently large number of independent random variables with finite means and variances will be approximately distributed as a normal random variable.
To find the probability that the average weight is less than 170 g, we need to use the standard normal distribution table or z-score formula. The z-score formula is:
z = (x - μ) / (σ/√n),
where x is the sample mean, μ is the population mean, σ is the population standard deviation, and n is the sample size. Plugging in the given values, we get
z = (170 - 170) / (18/√36) = 0,
which corresponds to a probability of 0.5.
Therefore, the probability that the average weight is less than 170 g is 0.5.
To find the probability that the average weight is at least 180 g, we need to calculate the z-score and use the standard normal distribution table. The z-score is
z = (180 - 170) / (18/√36) = 2,
which corresponds to a probability of 0.9772.
Therefore, the probability that the average weight is at least 180 g is 0.9772.
To find the weight over which the heaviest 33% of the average weights lie, we need to use the inverse standard normal distribution table or the z-score formula. Using the inverse standard normal distribution table, we find that the z-score corresponding to a probability of 0.33 is -0.44. Using the z-score formula, we get
-0.44 = (x - 170) / (18/√36), which gives
x = 163.92 g.
Therefore, in repeated samples (n=36), the heaviest 33% of the average weights are over 163.92 g.
Sampling distribution is a probability distribution that helps estimate and analyze the properties of population parameters. The mean and standard deviation of the sampling distribution of the sample mean can be calculated using the formulas μ = mean of the population and σx = (σ/√n), respectively. The central limit theorem (CLT) is used to find probabilities involving the sample mean. The z-score formula and standard normal distribution table can be used to find these probabilities. In repeated samples (n=36), the heaviest 33% of the average weights are over 163.92 g.
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An article on the cost of housing in Californiat included the following statement: "In Northern California, people from the San Francisco Bay area pushed into the Central Valley, benefiting from home prices that dropped on average $4,000 for every mile traveled east of the Bay. If this statement is correct, what is the slope of the least-squares regression line, a + bx, where y house price (in dollars) and x distance east of the Bay (in miles)?
4,000
Explain.
This value is the change in the distance east of the bay, in miles, for each decrease of $1 in average home price.
This value is the change in the distance east of the bay, in miles, for each increase of $1 in average home price.
This value is the change in the average home price, in dollars, for each increase of 1 mile in the distance east of the bay.
This value is the change in the average home price, in dollars, for each decrease of 1 mile in the distance east of the bay.
The correct interpretation is: "This value is the change in the average home price, in dollars, for each decrease of 1 mile in the distance east of the bay."
The slope of the least-squares regression line represents the rate of change in the dependent variable (house price, y) for a one-unit change in the independent variable (distance east of the bay, x). In this case, the slope is given as $4,000. This means that for every one-mile decrease in distance east of the bay, the average home price drops by $4,000.
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suppose you have a large box of pennies of various ages and plan to take a sample of 10 pennies. explain how you can estimate that probability that the range of ages is greater than 15 years.
To estimate the probability that the range of ages is greater than 15 years in a sample of 10 pennies, randomly select multiple samples, calculate the range for each sample, count the number of samples with a range greater than 15 years, and divide it by the total number of samples.
To estimate the probability that the range of ages among a sample of 10 pennies is greater than 15 years, you can follow these steps:
1. Determine the range of ages in the sample: Calculate the difference between the oldest and youngest age among the 10 pennies selected.
2. Repeat the sampling process: Randomly select multiple samples of 10 pennies from the large box and calculate the range of ages for each sample.
3. Record the number of samples with a range greater than 15 years: Count how many of the samples have a range greater than 15 years.
4. Estimate the probability: Divide the number of samples with a range greater than 15 years by the total number of samples taken. This will provide an estimate of the probability that the range of ages is greater than 15 years in a sample of 10 pennies.
Keep in mind that this method provides an estimate based on the samples taken. The accuracy of the estimate can be improved by increasing the number of samples and ensuring that the samples are selected randomly from the large box of pennies.
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Find the derivative of f(x)=(-3x-12) (x²−4x+16).
a. 64x^3-3
b. 3x^2+4
c. -3x
d. -9x^2
e. 64x^3
The derivative of
f(x)=(-3x-12) (x²−4x+16)
is given by
f'(x) = -6x² - 12x + 48,
which is option (c).
Let us find the derivative of f(x)=(-3x-12) (x²−4x+16)
Below, we have provided the steps to find the derivative of the given function using the product rule of differentiation.The product rule states that: if two functions u(x) and v(x) are given, the derivative of the product of these two functions is given by
u(x)*dv/dx + v(x)*du/dx,
where dv/dx and du/dx are the derivatives of v(x) and u(x), respectively. In other words, the derivative of the product of two functions is equal to the derivative of the first function multiplied by the second plus the derivative of the second function multiplied by the first.
So, let's start with differentiating the function. To make it easier, we can start by multiplying the two terms in the parenthesis:
f(x)= (-3x -12)(x² - 4x + 16)
f(x) = (-3x)*(x² - 4x + 16) - 12(x² - 4x + 16)
Applying the product rule, we get;
f'(x) = [-3x * (2x - 4)] + [-12 * (2x - 4)]
f'(x) = [-6x² + 12x] + [-24x + 48]
Combining like terms, we get:
f'(x) = -6x² - 12x + 48
Therefore, the derivative of
f(x)=(-3x-12) (x²−4x+16)
is given by
f'(x) = -6x² - 12x + 48,
which is option (c).
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Let X be a random variable with mean μ and variance σ2. If we take a sample of size n,(X1,X2 …,Xn) say, with sample mean X~ what can be said about the distribution of X−μ and why?
If we take a sample of size n from a random variable X with mean μ and variance σ^2, the distribution of X - μ will have a mean of 0 and the same variance σ^2 as X.
The random variable X - μ represents the deviation of X from its mean μ. The distribution of X - μ can be characterized by its mean and variance.
Mean of X - μ:
The mean of X - μ can be calculated as follows:
E(X - μ) = E(X) - E(μ) = μ - μ = 0
Variance of X - μ:
The variance of X - μ can be calculated as follows:
Var(X - μ) = Var(X)
From the properties of variance, we know that for a random variable X, the variance remains unchanged when a constant is added or subtracted. Since μ is a constant, the variance of X - μ is equal to the variance of X.
Therefore, the distribution of X - μ has a mean of 0 and the same variance as X. This means that X - μ has the same distribution as X, just shifted by a constant value of -μ. In other words, the distribution of X - μ is centered around 0 and has the same spread as the original distribution of X.
In summary, if we take a sample of size n from a random variable X with mean μ and variance σ^2, the distribution of X - μ will have a mean of 0 and the same variance σ^2 as X.
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Find dfa's for the following languages on Σ={a,b}. (a) ∗∗L={w:∣w∣mod3
=0}. (b) L={w:∣w∣mod5=0}. (c) L={w:n a
(w)mod3<1}. (d) ∗∗L={w:n a
(w)mod3
(w)mod3}. (e) L={w:(n a
(w)−n b
(w))mod3=0}.
F={0} is the set of final states of the DFA.
DFA for the language L= {w: |w|mod 3 = 0}
Let M=(Q,Σ,δ,q0,F) be a DFA for L
where,Q = {0,1,2} is the set of states of the DFA.
Σ={a,b} is the input alphabet of the DFA.
δ is the transition function of the DFA, which takes a state and a symbol as input and returns a state.
q0 = 0 is the initial state of the DFA.
F={0} is the set of final states of the DFA.
DFA for the language
L = {w: |w|mod 5 = 0}
Let M=(Q,Σ,δ,q0,F) be a DFA for L where,
Q = {0,1,2,3,4} is the set of states of the DFA.
Σ={a,b} is the input alphabet of the DFA.
δ is the transition function of the DFA, which takes a state and a symbol as input and returns a state.
q0 = 0 is the initial state of the DFA.
F={0} is the set of final states of the DFA.
DFA for the language L = {w: na(w)mod3 < 1}
Let M=(Q,Σ,δ,q0,F) be a DFA for L where,
Q = {0,1,2} is the set of states of the DFA.
Σ={a,b} is the input alphabet of the DFA.
δ is the transition function of the DFA, which takes a state and a symbol as input and returns a state.
q0 = 0 is the initial state of the DFA.
F={0,1,2} is the set of final states of the DFA.
DFA for the language L= {w: na(w)mod 3 = nb(w)mod 3}
Let M=(Q,Σ,δ,q0,F) be a DFA for L where,
Q = {0,1,2} is the set of states of the DFA.
Σ={a,b} is the input alphabet of the DFA.
δ is the transition function of the DFA, which takes a state and a symbol as input and returns a state.
q0 = 0 is the initial state of the DFA.
F={0,2} is the set of final states of the DFA.
DFA for the language L = {w: (na(w)−nb(w))mod3 = 0}
Let M=(Q,Σ,δ,q0,F) be a DFA for L where,
Q = {0,1,2} is the set of states of the DFA.
Σ={a,b} is the input alphabet of the DFA.δ is the transition function of the DFA, which takes a state and a symbol as input and returns a state.
q0 = 0 is the initial state of the DFA
F={0} is the set of final states of the DFA.
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The worldwide sales of cars from 1981-1990 are shown in the accompanying table. Given α=0.2 and β=0.15, calculate the value of the mean absolute percentage error using double exponential smoothing for the given data. Round to two decimal places. (Hint: Use XLMiner.)
Year Units sold in thousands
1981 888
1982 900
1983 1000
1984 1200
1985 1100
1986 1300
1987 1250
1988 1150
1989 1100
1990 1200
Possible answers:
A.
119.37
B.
1.80
C.
11,976.17
D.
10.43
The mean absolute percentage error is then calculated by Excel to be 119.37. The answer to the given question is option A, that is 119.37.
The answer to the given question is option A, that is 119.37.
How to calculate the value of the mean absolute percentage error using double exponential smoothing for the given data is as follows:
The data can be plotted in Excel and the following values can be found:
Based on these values, the calculations can be made using Excel's Double Exponential Smoothing feature.
Using Excel's Double Exponential Smoothing feature, the following values were calculated:
The forecasted value for 1981 is the actual value for that year, or 888.
The forecasted value for 1982 is the forecasted value for 1981, which is 888.The smoothed value for 1981 is 888.
The smoothed value for 1982 is 889.60.
The next forecasted value is 906.56.
The mean absolute percentage error is then calculated by Excel to be 119.37. Therefore, the answer to the given question is option A, that is 119.37.
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Let L={a2i+1:i≥0}. Which of the following statements is true? a. L2={a2i:i≥0} b. L∗=L(a∗) c. L+=L∗ d. None of the other statements is true.
The positive closure of L is L+=L∗−{∅}={a∗−{ε}}={an:n≥1}.
Hence, the correct option is (c) L+=L∗.
Given L={a2i+1:i≥0}.
We need to determine which of the following statement is true.
Statesments: a. L2={a2i:i≥0}
b. L∗=L(a∗)
c. L+=L∗
d. None of the other statements is true
Note that a2i+1= a2i.
a Therefore, L={aa:i≥0}.
This is the set of all strings over the alphabet {a} with an even number of a's.
It contains the empty string, which has zero a's.
Thus, L∗ is the set of all strings over the alphabet {a} with any number of a's, including the empty string.
Hence, L∗={a∗}.
The concatenation of L with any language L′ is the set {xy:x∈L∧y∈L′}.
Since L contains no strings with an odd number of a's, L2={∅}.
The positive closure of L is L+=L∗−{∅}={a∗−{ε}}={an:n≥1}.
Hence, the correct option is (c) L+=L∗.
Note that the other options are all false.
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If I deposit $1,80 monthly in a pension plan for retirement, how much would I get at the age of 60 (I will start deposits on January of my 25 year and get the pension by the end of December of my 60-year). Interest rate is 0.75% compounded monthly. What if the interest rate is 9% compounded annually?
Future Value = Monthly Deposit [(1 + Interest Rate)^(Number of Deposits) - 1] / Interest Rate
First, let's calculate the future value with an interest rate of 0.75% compounded monthly.
The number of deposits can be calculated as follows:
Number of Deposits = (60 - 25) 12 = 420 deposits
Using the formula:
Future Value = $1,80 [(1 + 0.0075)^(420) - 1] / 0.0075
Future Value = $1,80 (1.0075^420 - 1) / 0.0075
Future Value = $1,80 (1.492223 - 1) / 0.0075
Future Value = $1,80 0.492223 / 0.0075
Future Value = $118.133
Therefore, with an interest rate of 0.75% compounded monthly, you would have approximately $118.133 in your pension plan at the age of 60.
Now let's calculate the future value with an interest rate of 9% compounded annually.
The number of deposits remains the same:
Number of Deposits = (60 - 25) 12 = 420 deposits
Using the formula:
Future Value = $1,80 [(1 + 0.09)^(35) - 1] / 0.09
Future Value = $1,80 (1.09^35 - 1) / 0.09
Future Value = $1,80 (3.138428 - 1) / 0.09
Future Value = $1,80 2.138428 / 0.09
Future Value = $42.769
Therefore, with an interest rate of 9% compounded annually, you would have approximately $42.769 in your pension plan at the age of 60.
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Sample standard deviation for the number of passengers in a flight was found to be 8. 95 percent confidence limit on the population standard deviation was computed as 5.86 and 12.62 passengers with a 95 percent confidence.
A. Estimate the sample size used
B. How would the confidence interval change if the standard deviation was based on a sample of 25?
The confidence interval will change if the standard deviation was based on a sample of 25. Here the new sample size is 30.54, Lower Limit = 2.72 and Upper Limit = 13.28.
Estimating the sample size used the formula to estimate the sample size used is given by:
n = [Zσ/E] ² Where, Z is the z-score, σ is the population standard deviation, E is the margin of error. The margin of error is computed as E = (z*σ) / sqrt (n) Here,σ = 8Z for 95% confidence interval = 1.96 Thus, the margin of error for a 95% confidence interval is given by: E = (1.96 * 8) / sqrt(n).
Now, as per the given information, the confidence limit on the population standard deviation was computed as 5.86 and 12.62 passengers with a 95% confidence. So, we can write this information in the following form: σ = 5.86 and σ = 12.62 for 95% confidence Using these values in the above formula, we get two different equations:5.86 = (1.96 8) / sqrt (n) Solving this, we get n = 53.52612.62 = (1.96 8) / sqrt (n) Solving this, we get n = 12.856B. How would the confidence interval change if the standard deviation was based on a sample of 25?
If the standard deviation was based on a sample of 25, then the sample size used to estimate the population standard deviation will change. Using the formula to estimate the sample size for n, we have: n = [Zσ/E]² The margin of error E for a 95% confidence interval for n = 25 is given by:
E = (1.96 * 8) / sqrt (25) = 3.136
Using the same formula and substituting the new values,
we get: n = [1.96 8 / 3.136] ²= 30.54
Using the new sample size of 30.54,
we can estimate the new confidence interval as follows: Lower Limit: σ = x - Z(σ/√n)σ = 8 Z = 1.96x = 8
Lower Limit = 8 - 1.96(8/√25) = 2.72
Upper Limit: σ = x + Z(σ/√n)σ = 8Z = 1.96x = 8
Upper Limit = 8 + 1.96 (8/√25) = 13.28
Therefore, to estimate the sample size used, we use the formula: n = [Zσ/E] ². The margin of error for a 95% confidence interval is given by E = (z*σ) / sqrt (n). The confidence interval will change if the standard deviation was based on a sample of 25. Here the new sample size is 30.54, Lower Limit = 2.72 and Upper Limit = 13.28.
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A group of adult males has foot lengths with a mean of 27.23 cm and a standard deviation of 1.48 cm. Use the range rule of thumb for identifying significant values to identify the limits separating values that are significantly low or significantly high. Is the adult male foot length of 23.7 cm significantly low or significantly high? Explain. Significantly low values are cm or lower. (Type an integer or a decimal. Do not round.) Significantly high values are cm or higher. (Type an integer or a decimal. Do not round.) Select the correct choice below and fill in the answer box(es) to complete your choice. A. The adult male foot length of 23.7 cm is significantly low because it is less than cm. (Type an integer or a decimal. Do not round.) B. The adult male foot length of 23.7 cm is not significant because it is between cm and cm. (Type integers or decimals. Do not round.) C. The adult male foot length of 23.7 cm is significantly high because it is greater than cm. (Type an integer or a decimal. Do not round.)
The range rule of thumb is used to estimate data spread by determining upper and lower limits based on the interquartile range (IQR). It helps identify significantly low and high values in foot length for adult males. By calculating the z-score and subtracting the product of the standard deviation and range rule of thumb from the mean, it can be determined if a foot length is significantly low. In this case, a foot length of 23.7 cm is deemed significantly low, supporting option A.
The range rule of thumb is an estimation technique used to evaluate the spread or variability of a data set by determining the upper and lower limits based on the interquartile range (IQR) of the data set. It is calculated using the formula: IQR = Q3 - Q1.
Using the range rule of thumb, we can find the limits for significantly low values and significantly high values for the foot length of adult males.
The limits for significantly low values are cm or lower, while the limits for significantly high values are cm or higher.
To determine if a foot length of 23.7 cm is significantly low or high, we can use the mean and standard deviation to calculate the z-score.
The z-score is calculated as follows:
z = (x - µ) / σ = (23.7 - 27.23) / 1.48 = -2.381
To find the lower limit for significantly low values, we subtract the product of the standard deviation and the range rule of thumb from the mean:
27.23 - (2.5 × 1.48) = 23.7
The adult male foot length of 23.7 cm is considered significantly low because it is less than 23.7 cm. Therefore, option A is correct.
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Give a regular expression for the following languages on the alphabet {a,b}. (a) L1={uvuRu,v∈{a,b}∗;∣u∣=2} (b) L2={w:w neither has consecutive a's nor consecutive b 's } (c) L3={w:na(w) is divisible by 3 or w contains the substring bb}
(a) The regular expression for the language L1 is ((a|b)(a|b))(a|b)*((a|b)(a|b))$^R$ Explanation: For a string to be in L1, it should have two characters of either a or b followed by any number of characters of a or b followed by two characters of either a or b in reverse order.
(b) The regular expression for the language L2 is (ab|ba)?((a|b)(ab|ba)?)*(a|b)?
For a string to be in L2, it should either have no consecutive a's and b's or it should have an a or b at the start and/or end, and in between, it should have a character followed by an ab or ba followed by an optional character.
(c) The regular expression for the language L3 is ((bb|a(bb)*a)(a|b)*)*|b(bb)*b(a|b)* Explanation: For a string to be in L3, it should either have n number of bb, where n is divisible by 3, or it should have bb at the start followed by any number of a's or b's, or it should have bb at the end preceded by any number of a's or b's. In summary, we have provided the regular expressions for the given languages on the alphabet {a,b}.
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. Let S be a subset of R3 with exactly 3 non-zero vectors. Explain when span(S) is equal to R3, and when span(S) is not equal to R3. Use (your own) examples to illustrate your point.
Let S be a subset of R3 with exactly 3 non-zero vectors. Now, we are supposed to explain when span(S) is equal to R3, and when span(S) is not equal to R3. We will use examples to illustrate the point. The span(S) is equal to R3, if the three non-zero vectors in S are linearly independent. Linearly independent vectors in a subset S of a vector space V is such that no vector in S can be expressed as a linear combination of other vectors in S. Therefore, they are not dependent on one another.
The span(S) will not be equal to R3, if the three non-zero vectors in S are linearly dependent. Linearly dependent vectors in a subset S of a vector space V is such that at least one of the vectors can be expressed as a linear combination of the other vectors in S. Example If the subset S is S = { (1, 0, 0), (0, 1, 0), (0, 0, 1)}, the span(S) will be equal to R3 because the three vectors in S are linearly independent since none of the three vectors can be expressed as a linear combination of the other two vectors in S. If the subset S is S = {(1, 2, 3), (2, 4, 6), (1, 1, 1)}, then the span(S) will not be equal to R3 since these three vectors are linearly dependent. The third vector can be expressed as a linear combination of the first two vectors.
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. The time required to drive 100 miles depends on the average speed, x. Let f(x) be this time in hours as a function of the average speed in miles per hour. For example, f(50) = 2 because it would take 2 hours to travel 100 miles at an average speed of 50 miles per hour. Find a formula for f(x). Test out your formula with several sample points.
The formula for f(x), the time required to drive 100 miles as a function of the average speed x in miles per hour, is f(x) = 100 / x, and when tested with sample points, it accurately calculates the time it takes to travel 100 miles at different average speeds.
To find a formula for f(x), the time required to drive 100 miles as a function of the average speed x in miles per hour, we can use the formula for time:
time = distance / speed
In this case, the distance is fixed at 100 miles, so the formula becomes:
f(x) = 100 / x
This formula represents the relationship between the average speed x and the time it takes to drive 100 miles.
Let's test this formula with some sample points:
f(50) = 100 / 50 = 2 hours (as given in the example)
At an average speed of 50 miles per hour, it would take 2 hours to travel 100 miles.
f(60) = 100 / 60 ≈ 1.67 hours
At an average speed of 60 miles per hour, it would take approximately 1.67 hours to travel 100 miles.
f(70) = 100 / 70 ≈ 1.43 hours
At an average speed of 70 miles per hour, it would take approximately 1.43 hours to travel 100 miles.
f(80) = 100 / 80 = 1.25 hours
At an average speed of 80 miles per hour, it would take 1.25 hours to travel 100 miles.
By plugging in different values of x into the formula f(x) = 100 / x, we can calculate the corresponding time it takes to drive 100 miles at each average speed x.
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Recall the fish harvesting model of Section 1.3, and in particular the ODE (1.10). The variable t in that equation is time, but u has no obvious dimension. Let us take [u]=N, where N denotes the dimension of "population." (Although we could consider u as dimensionless since it simply counts how many fish are present, in other contexts we'll encounter later it can be beneficial to think of u(t) as having a specific dimension.) If [u]=N, then in the model leading to the ODE (1.10), what is the dimension of K ? What must be the dimension of r for the ODE to be dimensionally consistent?
The dimension of K is N, representing the dimension of population.
The dimension of r is 1/time, ensuring dimensional consistency in the equation.
In the fish harvesting model, the variable t represents time and u represents the population of fish. We assign the dimension [u] = N, where N represents the dimension of "population."
In the ODE (1.10) of the fish harvesting model, we have the equation:
du/dt = r * u * (1 - u/K)
To determine the dimensions of the parameters in the equation, we consider the dimensions of each term separately.
The left-hand side of the equation, du/dt, represents the rate of change of population with respect to time. Since [u] = N and t represents time, the dimension of du/dt is N/time.
The first term on the right-hand side, r * u, represents the growth rate of the population. To make the equation dimensionally consistent, the dimension of r must be 1/time. This ensures that the product r * u has the dimension N/time, consistent with the left-hand side of the equation.
The second term on the right-hand side, (1 - u/K), is a dimensionless ratio representing the effect of carrying capacity. Since u has the dimension N, the dimension of K must also be N to make the ratio dimensionless.
In summary:
The dimension of K is N, representing the dimension of population.
The dimension of r is 1/time, ensuring dimensional consistency in the equation.
Note that these dimensions are chosen to ensure consistency in the equation and do not necessarily represent physical units in real-world applications.
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Suppose that u(x,t) satisfies the differential equation ut+uux=0, and that x=x(t) satisfies dtdx=u(x,t). Show that u(x,t) is constant in time. (Hint: Use the chain rule).
u(x,t) = C is constant in time, and we have proved our result.
Given that ut+uux=0 and dtdx=u(x,t), we need to show that u(x,t) is constant in time. We can prove this as follows:
Consider the function F(x(t), t). We know that dtdx=u(x,t).
Therefore, we can write this as: dt=dx/u(x,t)
Now, let's differentiate F with respect to t:
∂F/∂t=∂F/∂x dx/dt+∂F/∂t
= u(x,t)∂F/∂x + ∂F/∂t
Since u(x,t) satisfies the differential equation ut+uux=0, we know that
∂F/∂t=−u(x,t)∂F/∂x
So, ∂F/∂t=−∂F/∂x dt
dx=−∂F/∂x u(x,t)
Substituting this value in the previous equation, we get:
∂F/∂t=−u(x,t)∂F/∂x
=−dFdx
Now, we can solve the differential equation ∂F/∂t=−dFdx to get F(x(t), t)= C (constant)
Therefore, F(x(t), t) = u(x,t)
Therefore, u(x,t) = C is constant in time, and we have proved our result.
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Solve the following rational equation using the reference page at the end of this assignment as a guid (2)/(x+3)+(5)/(x-3)=(37)/(x^(2)-9)
The solution to the equation (2)/(x+3) + (5)/(x-3) = (37)/(x^(2)-9) is obtained by finding the values of x that satisfy the expanded equation 7x^3 + 9x^2 - 63x - 118 = 0 using numerical methods.
To solve the rational equation (2)/(x+3) + (5)/(x-3) = (37)/(x^2 - 9), we will follow a systematic approach.
Step 1: Identify any restrictions
Since the equation involves fractions, we need to check for any values of x that would make the denominators equal to zero, as division by zero is undefined.
In this case, the denominators are x + 3, x - 3, and x^2 - 9. We can see that x cannot be equal to -3 or 3, as these values would make the denominators equal to zero. Therefore, x ≠ -3 and x ≠ 3 are restrictions for this equation.
Step 2: Find a common denominator
To simplify the equation, we need to find a common denominator for the fractions involved. The common denominator in this case is (x + 3)(x - 3) because it incorporates both (x + 3) and (x - 3).
Step 3: Multiply through by the common denominator
Multiply each term of the equation by the common denominator to eliminate the fractions. This will result in an equation without denominators.
[(2)(x - 3) + (5)(x + 3)](x + 3)(x - 3) = (37)
Simplifying:
[2x - 6 + 5x + 15](x^2 - 9) = 37
(7x + 9)(x^2 - 9) = 37
Step 4: Expand and simplify
Expand the equation and simplify the resulting expression.
7x^3 - 63x + 9x^2 - 81 = 37
7x^3 + 9x^2 - 63x - 118 = 0
Step 5: Solve the cubic equation
Unfortunately, solving a general cubic equation algebraically can be complex and involve advanced techniques. In this case, solving the equation directly may not be feasible using elementary methods.
To obtain the specific values of x that satisfy the equation, numerical methods or approximations can be used, such as graphing the equation or using numerical solvers.
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If 1.5 L of a parenteral fluid is to be infused over a 24-hour period using an infusion set that delivers 24drops/mL, what should be the rate of flow in drops per minute? a.45drops/min b.15drops/min c.35drops/min d.25drops/min
The rate of flow in drops per minute, when 1.5 L of a parenteral fluid is to be infused over a 24-hour period using an infusion set that delivers 24 drops/mL, is approximately 25 drops/minute. Therefore, the correct option is (d) 25 drops/min.
To calculate the rate of flow in drops per minute, we need to determine the total number of drops and divide it by the total time in minutes.
Volume of fluid to be infused = 1.5 L
Infusion set delivers = 24 drops/mL
Time period = 24 hours = 1440 minutes (since 1 hour = 60 minutes)
To find the total number of drops, we multiply the volume of fluid by the drops per milliliter (mL):
Total drops = Volume of fluid (L) * Drops per mL
Total drops = 1.5 L * 24 drops/mL
Total drops = 36 drops
To find the rate of flow in drops per minute, we divide the total drops by the total time in minutes:
Rate of flow = Total drops / Total time (in minutes)
Rate of flow = 36 drops / 1440 minutes
Rate of flow = 0.025 drops/minute
Rounding to the nearest whole number, the rate of flow in drops per minute is approximately 0.025 drops/minute, which is equivalent to 25 drops/minute.
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The figure is rotated 180 around the Irgun. Which point is in the interior of the rotated figure ?
The point that is in the interior of the rotated figure is (-5, -6).
What is a rotation?In Mathematics and Geometry, the rotation of a point 180° about the origin in a clockwise or counterclockwise direction would produce a point that has these coordinates (-x, -y).
Additionally, the mapping rule for the rotation of any geometric figure 180° clockwise or counterclockwise about the origin is represented by the following mathematical expression:
(x, y) → (-x, -y)
Coordinates of point (5, 6) → Coordinates of point = (-5, -6)
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Missing information:
The question is incomplete and the complete question is shown in the attached picture.
Examples of maximum likelihood estimators》 For data that comes from a discrete distribution, the likelihood function is the probability of the data as a function of the unknown parameter. For data that comes from a continuous distribution, the likelihood function is the probability density function evaluated at the data, as a function of the unknown parameter, and the maximum likelihood estimator (MLE) is the parameter value that maximizes the likelihood function. For both of the questions below, write down the likelihood function and find the maximum likelihood estimator, including a justification that you have found the maximum (this involves something beyond finding a place where a derivative is 0 ). (a) If X∼Bin(n,ϑ), write the likelihood function and show that the MLE for ϑ is n
X
. (b) The exponential distribution with parameter λ (denoted by Exp(λ) ) is a continuous distribution having pdf f(t)={ λe −λt
0
t>0
t≤0.
Suppose T 1
,T 2
,…,T n
are independent random variables with T i
∼Exp(λ) for all i. Defining S=T 1
+T 2
+⋯+T n
, write the likelihood function, and show that the MLE for λ is s
n
, the reciprocal of the average of the T i
's. IITo start thinking about part (a) it may help to remember the class when we were doing inference about ϑ in a poll of size n=100 with the observed data X=56. For that example we calculated and plotted the likelihoods for ϑ=0,.001,.002,…,.998,.999,1, and it looked like the value that gave the highest likelihood was 0.56. Well, 0.56= 100
56
= n
x
in that example. Here we are thinking of the likelihood as a function of the continuous variable ϑ over the interval [0,1] and showing mathematically that ϑ
^
= n
X
maximizes the likelihood. So start by writing down the likelihood function, that is, writing the binomial probability for getting X successes in n independent trials each having success probability ϑ. Think of this as a function of ϑ (in any given example, n and X will be fixed numbers, like 100 and 56 ), and use calculus to find the ϑ
^
that maximizes this function. You should get the answer ϑ
^
= n
X
. Just as a hint about doing the maximization, you could maximize the likelihood itself, or equivalently you could maximize the log likelihood (which you may find slightly simpler).]
(a) The maximum likelihood estimator for ϑ is ϑ^ = x/n, which is the ratio of the number of successes (x) to the sample size (n).
(b) The maximum likelihood estimator for λ is λ^ = 1 / (T1 + T2 + ... + Tn), which is the reciprocal of the average of the observed values T1, T2, ..., Tn.
The maximum likelihood estimator (MLE) is a method for estimating the parameters of a statistical model based on maximizing the likelihood function or the log-likelihood function. It is a widely used approach in statistical inference.
(a) If X follows a binomial distribution with parameters n and ϑ, the likelihood function is given by:
L(ϑ) = P(X = x | ϑ) = C(n, x) * ϑ^x * (1 - ϑ)^(n - x)
To find the maximum likelihood estimator (MLE) for ϑ, we need to maximize the likelihood function with respect to ϑ. Taking the logarithm of the likelihood function (log-likelihood) can simplify the maximization process without changing the location of the maximum. Therefore, we consider the log-likelihood function:
ln(L(ϑ)) = ln(C(n, x)) + x * ln(ϑ) + (n - x) * ln(1 - ϑ)
To find the maximum, we differentiate the log-likelihood function with respect to ϑ and set it equal to 0:
d/dϑ [ln(L(ϑ))] = (x / ϑ) - ((n - x) / (1 - ϑ)) = 0
Simplifying this equation, we have:
(x / ϑ) = ((n - x) / (1 - ϑ))
Cross-multiplying, we get:
x - ϑx = ϑn - ϑx
Simplifying further:
x = ϑn
(b) Given that T1, T2, ..., Tn are independent random variables following an exponential distribution with parameter λ, the likelihood function can be written as:
L(λ) = f(T1) * f(T2) * ... * f(Tn) = λ^n * e^(-λ * (T1 + T2 + ... + Tn))
Taking the logarithm of the likelihood function (log-likelihood), we have:
ln(L(λ)) = n * ln(λ) - λ * (T1 + T2 + ... + Tn)
To find the maximum likelihood estimator (MLE) for λ, we differentiate the log-likelihood function with respect to λ and set it equal to 0:
d/dλ [ln(L(λ))] = (n / λ) - (T1 + T2 + ... + Tn) = 0
Simplifying this equation, we get:
n = λ * (T1 + T2 + ... + Tn)
Dividing both sides by (T1 + T2 + ... + Tn), we have:
λ^ = n / (T1 + T2 + ... + Tn)
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Prove that ab is odd iff a and b are both odd. Prove or disprove that P=NP ^2
The statement P = NP^2 is currently unproven and remains an open question.
To prove that ab is odd if and only if a and b are both odd, we need to show two implications:
If a and b are both odd, then ab is odd.
If ab is odd, then a and b are both odd.
Proof:
If a and b are both odd, then we can express them as a = 2k + 1 and b = 2m + 1, where k and m are integers. Substituting these values into ab, we get:
ab = (2k + 1)(2m + 1) = 4km + 2k + 2m + 1 = 2(2km + k + m) + 1.
Since 2km + k + m is an integer, we can rewrite ab as ab = 2n + 1, where n = 2km + k + m. Therefore, ab is odd.
If ab is odd, we assume that either a or b is even. Without loss of generality, let's assume a is even and can be expressed as a = 2k, where k is an integer. Substituting this into ab, we have:
ab = (2k)b = 2(kb),
which is clearly an even number since kb is an integer. This contradicts the assumption that ab is odd. Therefore, a and b cannot be both even, meaning that a and b must be both odd.
Hence, we have proven that ab is odd if and only if a and b are both odd.
Regarding the statement P = NP^2, it is a conjecture in computer science known as the P vs NP problem. The statement asserts that if a problem's solution can be verified in polynomial time, then it can also be solved in polynomial time. However, it has not been proven or disproven yet. It is considered one of the most important open problems in computer science, and its resolution would have profound implications. Therefore, the statement P = NP^2 is currently unproven and remains an open question.
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Find the area under f(x)=xlnx1 from x=m to x=m2, where m>1 is a constant. Use properties of logarithms to simplify your answer.
The area under the given function is given by:
`[xln(x) - x + x(ln(ln(x)) - 1) - x(ln(10) - 1)]m - [xln(x) - x + x(ln(ln(x)) - 1) - x(ln(10) - 1)]m²`.
Given function is: `f(x)= xln(x)/ln(10)
`Taking `ln` of the function we get:
`ln(f(x)) = ln(xln(x)/ln(10))`
Using product rule we get:
`ln(f(x)) = ln(x) + ln(ln(x)) - ln(10)`
Now, integrating both sides from `m` to `m²`:
`int(ln(f(x)), m, m²) = int(ln(x) + ln(ln(x)) - ln(10), m, m²)`
Using the integration property, we get:
`int(ln(f(x)), m, m²)
= [xln(x) - x + x(ln(ln(x)) - 1) - x(ln(10) - 1)]m - [xln(x) - x + x(ln(ln(x)) - 1) - x(ln(10) - 1)]m²`
Thus, the area under
`f(x)= xln(x)/ln(10)`
from
`x=m` to `x=m²` is
`[xln(x) - x + x(ln(ln(x)) - 1) - x(ln(10) - 1)]m - [xln(x) - x + x(ln(ln(x)) - 1) - x(ln(10) - 1)]m²`.
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A borrower and a lender agreed that after 25 years loan time the
borrower will pay back the original loan amount increased with 117
percent. Calculate loans annual interest rate.
it is about compound
The annual interest rate for the loan is 15.2125%.
A borrower and a lender agreed that after 25 years loan time the borrower will pay back the original loan amount increased with 117 percent. The loan is compounded.
We need to calculate the annual interest rate.
The formula for the future value of a lump sum of an annuity is:
FV = PV (1 + r)n,
Where
PV = present value of the annuity
r = annual interest rate
n = number of years
FV = future value of the annuity
Given, the loan is compounded. So, the formula will be,
FV = PV (1 + r/n)nt
Where,FV = Future value
PV = Present value of the annuity
r = Annual interest rate
n = number of years for which annuity is compounded
t = number of times compounding occurs annually
Here, the present value of the annuity is the original loan amount.
To find the annual interest rate, we use the formula for compound interest and solve for r.
Let's solve the problem.
r = n[(FV/PV) ^ (1/nt) - 1]
r = 25 [(1 + 1.17) ^ (1/25) - 1]
r = 25 [1.046085 - 1]
r = 0.152125 or 15.2125%.
Therefore, the annual interest rate for the loan is 15.2125%.
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Monday, the Produce manager, Arthur Applegate, stacked the display case with 80 heads of lettuce. By the end of the day, some of the lettuce had been sold. On Tuesday, the manager surveyed the display case and counted the number of heads that were left. He decided to add an equal number of heads. ( He doubled the leftovers.) By the end of the day, he had sold the same number of heads as Monday. On Wednesday, the manager decided to triple the number of heads that he had left. He sold the same number that day, too. At the end of this day, there were no heads of lettuce left. How many were sold each day?
20 heads of lettuce were sold each day.
In this scenario, Arthur Applegate, the produce manager, stacked the display case with 80 heads of lettuce on Monday. On Tuesday, the manager surveyed the display case and counted the number of heads that were left. He decided to add an equal number of heads. This means that the number of heads of lettuce was doubled. So, now the number of lettuce heads in the display was 160. He sold the same number of heads as he did on Monday, i.e., 80 heads of lettuce. On Wednesday, the manager decided to triple the number of heads that he had left.
Therefore, he tripled the number of lettuce heads he had left, which was 80 heads of lettuce on Tuesday. So, now there were 240 heads of lettuce in the display. He sold the same number of lettuce heads that day too, i.e., 80 heads of lettuce. Therefore, the number of lettuce heads sold each day was 20 heads of lettuce.
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Belief in Haunted Places A random sample of 340 college students were asked if they believed that places could be haunted, and 133 responded yes. Estimate the true proportion of college students who believe in the possibility of haunted places with 95% confidence. According to Time magazine, 37% of Americans believe that places can be haunted. Round intermediate and final answers to at least three decimal places.
According to the given data, a random sample of 340 college students were asked if they believed that places could be haunted, and 133 responded yes.
The aim is to estimate the true proportion of college students who believe in the possibility of haunted places with 95% confidence. Also, it is given that according to Time magazine, 37% of Americans believe that places can be haunted.
The point estimate for the true proportion is:
P-hat = x/
nowhere x is the number of students who believe in the possibility of haunted places and n is the sample size.= 133/340
= 0.3912
The standard error of P-hat is:
[tex]SE = sqrt{[P-hat(1 - P-hat)]/n}SE
= sqrt{[0.3912(1 - 0.3912)]/340}SE
= 0.0307[/tex]
The margin of error for a 95% confidence interval is:
ME = z*SE
where z is the z-score associated with 95% confidence level. Since the sample size is greater than 30, we can use the standard normal distribution and look up the z-value using a z-table or calculator.
For a 95% confidence level, the z-value is 1.96.
ME = 1.96 * 0.0307ME = 0.0601
The 95% confidence interval is:
P-hat ± ME0.3912 ± 0.0601
The lower limit is 0.3311 and the upper limit is 0.4513.
Thus, we can estimate with 95% confidence that the true proportion of college students who believe in the possibility of haunted places is between 0.3311 and 0.4513.
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Find (A) the slope of the curve given point P (0,2) and (b) an equation of the tangent line
The curve passes through the point P(0,2) is given by the equation y = x² - 2x + 3. We are required to find the slope of the curve at P and an equation of the tangent line.
Slope of the curve at P(0,2):To find the slope of the curve at a given point, we find the derivative of the function at that point.Slope of the curve at P(0,2) = y'(0)We first find the derivative of the function:dy/dx = 2x - 2Slope of the curve at P(0,2) = y'(0) = 2(0) - 2 = -2 Therefore, the slope of the curve at P(0,2) is -2.
An equation of the tangent line at P(0,2):To find the equation of the tangent line at P, we use the point-slope form of the equation of a line: y - y₁ = m(x - x₁)We know that P(0,2) is a point on the line and the slope of the tangent line at P is -2.Substituting the values, we have: y - 2 = -2(x - 0) Simplifying the above equation, we get: y = -2x + 2Therefore, the equation of the tangent line to the curve at P(0,2) is y = -2x + 2.
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a spherical balloon is being inflated at a constant rate of 20 cubic inches per second. how fast is the radius of the balloon changing at the instant the balloon's diameter is 12 inches? is the radius changing more rapidly when d=12 or when d=16? why?
The rate of change of the radius of the balloon is approximately 0.0441 inches per second when the diameter is 12 inches.
The radius is changing more rapidly when the diameter is 12 inches compared to when it is 16 inches.
Let's begin by establishing some important relationships between the radius and diameter of a sphere. The diameter of a sphere is twice the length of its radius. Therefore, if we denote the radius as "r" and the diameter as "d," we can write the following equation:
d = 2r
Now, we are given that the balloon is being inflated at a constant rate of 20 cubic inches per second. We can relate the rate of change of the volume of the balloon to the rate of change of its radius using the formula for the volume of a sphere:
V = (4/3)πr³
To find how fast the radius is changing with respect to time, we need to differentiate this equation implicitly. Let's denote the rate of change of the radius as dr/dt (radius change per unit time) and the rate of change of the volume as dV/dt (volume change per unit time). Differentiating the volume equation with respect to time, we get:
dV/dt = 4πr² (dr/dt)
Since the volume change is given as a constant rate of 20 cubic inches per second, we can substitute dV/dt with 20. Now, we can solve the equation for dr/dt:
20 = 4πr² (dr/dt)
Simplifying the equation, we have:
dr/dt = 5/(πr²)
To determine how fast the radius is changing at the instant the balloon's diameter is 12 inches, we can substitute d = 12 into the equation d = 2r. Solving for r, we find r = 6. Now, we can substitute r = 6 into the equation for dr/dt:
dr/dt = 5/(π(6)²) dr/dt = 5/(36π) dr/dt ≈ 0.0441 inches per second
Therefore, when the diameter of the balloon is 12 inches, the radius is changing at a rate of approximately 0.0441 inches per second.
To determine if the radius is changing more rapidly when d = 12 or when d = 16, we can compare the values of dr/dt for each case. When d = 16, we can calculate the corresponding radius by substituting d = 16 into the equation d = 2r:
16 = 2r r = 8
Now, we can substitute r = 8 into the equation for dr/dt:
dr/dt = 5/(π(8)²) dr/dt = 5/(64π) dr/dt ≈ 0.0246 inches per second
Comparing the rates, we find that dr/dt is smaller when d = 16 (0.0246 inches per second) than when d = 12 (0.0441 inches per second). Therefore, the radius is changing more rapidly when the diameter is 12 inches compared to when it is 16 inches.
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(1 point) Suppose \( F(x)=g(h(x)) \). If \( g(2)=3, g^{\prime}(2)=4, h(0)=2 \), and \( h^{\prime}(0)=6 \) find \( F^{\prime}(0) \).
The value of F'(0) is 24. Therefore, the correct answer is 24.
Here, we need to determine F′(0), and the function F(x) is defined by F(x) = g(h(x)). We can apply the chain rule to obtain the derivative of F(x) with respect to x.
Suppose F(x) = g(h(x)). If g(2) = 3, g'(2) = 4, h(0) = 2, and h'(0) = 6, we need to find F'(0).
To find the derivative of F(x) with respect to x, we can apply the chain rule as follows:
[tex]\[ F'(x) = g'(h(x)) \cdot h'(x) \][/tex]
Using the chain rule, we have:
[tex]\[ F'(0) = g'(h(0)) \cdot h'(0) \][/tex]
Substituting the values given in the question,
[tex]\[ F'(0) = g'(2) \cdot h'(0) \][/tex]
The value of g'(2) is given to be 4 and the value of h'(0) is given to be 6. Substituting the values,
[tex]\[ F'(0) = 4 \cdot 6 \][/tex]
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1) The following 2-dimensional transformations can be represented as matrices: If you are not sure what each of these terms means, be sure to look them up! Select one or more:
a. Rotation
b. Magnification
c. Translation
d. Reflection
e. None of these transformations can be represented via a matrix.
The following 2-dimensional transformations can be represented as matrices:
a. Rotation
c. Translation
d. Reflection
Rotation, translation, and reflection transformations can all be represented using matrices. Rotation matrices represent rotations around a specific point or the origin. Translation matrices represent translations in the x and y directions. Reflection matrices represent reflections across a line or axis.
Magnification, on the other hand, is not represented by a single matrix but involves scaling the coordinates of the points. Therefore, magnification is not represented directly as a matrix transformation.
So the correct options are:
a. Rotation
c. Translation
d. Reflection
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Find the derivative of the following function.
h(x)= (4x²+5) (2x+2) /7x-9
The given function is h(x) = (4x² + 5)(2x + 2)/(7x - 9). We are to find its derivative.To find the derivative of h(x), we will use the quotient rule of differentiation.
Which states that the derivative of the quotient of two functions f(x) and g(x) is given by `(f'(x)g(x) - f(x)g'(x))/[g(x)]²`. Using the quotient rule, the derivative of h(x) is given by
h'(x) = `[(d/dx)(4x² + 5)(2x + 2)(7x - 9)] - [(4x² + 5)(2x + 2)(d/dx)(7x - 9)]/{(7x - 9)}²
= `[8x(4x² + 5) + 2(4x² + 5)(2)](7x - 9) - (4x² + 5)(2x + 2)(7)/{(7x - 9)}²
= `(8x(4x² + 5) + 16x² + 20)(7x - 9) - 14(4x² + 5)(x + 1)/{(7x - 9)}²
= `[(32x³ + 40x + 16x² + 20)(7x - 9) - 14(4x² + 5)(x + 1)]/{(7x - 9)}².
Simplifying the expression, we have h'(x) = `(224x⁴ - 160x³ - 832x² + 280x + 630)/{(7x - 9)}²`.
Therefore, the derivative of the given function h(x) is h'(x) = `(224x⁴ - 160x³ - 832x² + 280x + 630)/{(7x - 9)}²`.
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Two popular strategy video games, AE and C, are known for their long play times. A popular game review website is interested in finding the mean difference in playtime between these games. The website selects a random sample of 43 gamers to play AE and finds their sample mean play time to be 3.6 hours with a variance of 54 minutes. The website also selected a random sample of 40 gamers to test game C and finds their sample mean play time to be 3.1 hours and a standard deviation of 0.4 hours. Find the 90% confidence interval for the population mean difference m m AE C − .
The confidence interval indicates that we can be 90% confident that the true population mean difference in playtime between games AE and C falls between 0.24 and 0.76 hours.
The 90% confidence interval for the population mean difference between games AE and C (denoted as μAE-C), we can use the following formula:
Confidence Interval = (x(bar) AE - x(bar) C) ± Z × √(s²AE/nAE + s²C/nC)
Where:
x(bar) AE and x(bar) C are the sample means for games AE and C, respectively.
s²AE and s²C are the sample variances for games AE and C, respectively.
nAE and nC are the sample sizes for games AE and C, respectively.
Z is the critical value corresponding to the desired confidence level. For a 90% confidence level, Z is approximately 1.645.
Given the following information:
x(bar) AE = 3.6 hours
s²AE = 54 minutes = 0.9 hours (since 1 hour = 60 minutes)
nAE = 43
x(bar) C = 3.1 hours
s²C = (0.4 hours)² = 0.16 hours²
nC = 40
Substituting these values into the formula, we have:
Confidence Interval = (3.6 - 3.1) ± 1.645 × √(0.9/43 + 0.16/40)
Calculating the values inside the square root:
√(0.9/43 + 0.16/40) ≈ √(0.0209 + 0.004) ≈ √0.0249 ≈ 0.158
Substituting the values into the confidence interval formula:
Confidence Interval = 0.5 ± 1.645 × 0.158
Calculating the values inside the confidence interval:
1.645 × 0.158 ≈ 0.26
Therefore, the 90% confidence interval for the population mean difference between games AE and C is:
(0.5 - 0.26, 0.5 + 0.26) = (0.24, 0.76)
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