The P-value for a left-tailed hypothesis test with a test statistic of z = -1.40 is approximately 0.0808. Since the P-value (0.0808) is greater than the level of significance (α = 0.10), we do not have enough evidence to reject the null hypothesis at the 0.10 significance level.
To find the P-value for a left-tailed hypothesis test, we need to calculate the probability of observing a test statistic as extreme as or more extreme than the given value of z = -1.40 under the null hypothesis.
Using a standard normal distribution table or a statistical software, we can find that the cumulative probability for z = -1.40 is approximately 0.0808.
Comparing the P-value (0.0808) to the level of significance (α = 0.10), we see that the P-value is greater than α. Therefore, we do not have enough evidence to reject the null hypothesis at the 0.10 significance level.
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A study was conducted to estimate hospital costs for accident victims who wore seat belts. Twenty-one randomly selected cases have a distribution that appears to be approximately bell shaped with a mean of $9916 and a standard deviation of $5622. Complete parts (a) and (b). a. Construct the 95% confidence interval for the mean of all such costs. $
The 95% confidence interval for the mean of all such costs is $6986.4 to $12845.6.
Given that, Sample Mean = $9916
Sample Standard Deviation = $5622
Sample size, n = 21At 95% confidence level, the alpha level is 0.05.
The degrees of freedom (df) are n - 1 = 20.
Standard Error (SE) is given by the formula:
SE = (Sample Standard Deviation/ √(Sample Size))
SE = 5622/ √21SE = 1223.8
The formula for confidence interval at 95% is as follows:
Confidence Interval = (Sample Mean - (Critical value*SE), Sample Mean + (Critical value*SE))
Now we need to find the critical value at 95% using t-distribution since the sample size is less than 30.
The degrees of freedom (df) are 20.
So, t-value at 95% confidence and df = 20 is ±2.093.
Substituting the given values in the above formula we get,
Confidence Interval = ($9916 - (2.093*1223.8), $9916 + (2.093*1223.8))
Confidence Interval = ($6986.4, $12845.6)
Hence, the 95% confidence interval for the mean of all such costs is $6986.4 to $12845.6.
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the end of the hour and the end of the lecture and suppose the pdf of X is as follows. f(x)={kx200≤x≤2 otherwise (a) Find the value of k. (Enter your answer to three decimal places.) (b) What is the probability that the lecture ends within 1 min of the end of the hour? (Enter your answer to three decimal places.) (c) What is the probability that the lecture continues beyond the hour for between 15 and 45 sec? (Round your answer to four decimal places.) (d) What is the probability that the lecture continues for at least 45 sec beyond the end of the hour? (Round your answer to four decimal places.)
The value of k is 0.75. The probability that the lecture ends within 1 min of the end of the hour is approximately 0.088.
a) We need to find the value of k for the pdf of X.
f(x)={kx200≤x≤2 otherwise
Now, as f(x) is a probability density function, the total area under the curve will be equal to 1.
Using this concept, we can find the value of k as follows:
Integral from 0 to 2 of f(x)dx + Integral from 2 to infinity of f(x)dx = 1
Integrating the first part, ∫(0 to 2) kx² dx = k[(x³)/3] (0 to 2) = 8k/3
Integrating the second part, ∫(2 to infinity) k dx = kx (2 to infinity) = infinity - 2k = 1
So, we have 8k/3 - 2k = 1, which gives us k = 3/4.
Therefore, the value of k is 0.75.
b) We need to find the probability that the lecture ends within 1 min of the end of the hour, which means between 59 and 60 minutes, or 0.9833 to 1 minute in decimal form.
Using the given pdf, we need to find the integral from 0.9833 to 1 of f(x)dx.
Integrating, ∫(0.9833 to 1) (3/4)x² dx = (3/4) [(x³)/3] (0.9833 to 1) = (3/4) [1/3 - (0.9833)³/3] ≈ 0.088.
Therefore, the probability that the lecture ends within 1 min of the end of the hour is approximately 0.088.
c) We need to find the probability that the lecture continues beyond the hour for between 15 and 45 sec, which means between 60 and 60.75 minutes or 1 to 1.0125 in decimal form. Using the given pdf, we need to find the integral from 1 to 1.0125 of f(x)dx.
Integrating, ∫(1 to 1.0125) (3/4)x² dx = (3/4) [(x³)/3] (1 to 1.0125) = (3/4) [(1.0125)³/3 - 1/3] ≈ 0.0091.
Therefore, the probability that the lecture continues beyond the hour for between 15 and 45 sec is approximately 0.0091.
d) We need to find the probability that the lecture continues for at least 45 sec beyond the end of the hour, which means beyond 60.75 minutes or 1.0125 in decimal form.
Using the given pdf, we need to find the integral from 1.0125 to infinity of f(x)dx.
Integrating, ∫(1.0125 to infinity) (3/4) dx = (3/4) [x] (1.0125 to infinity) = infinity - (3/4)(1.0125) ≈ 0.7022
Therefore, the probability that the lecture continues for at least 45 sec beyond the end of the hour is approximately 0.7022.
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Consider a random variable, X, that has the following probability density function: Cx4 if 0 < x < 1 if 1 ≤ x ≤ 2 CX 5 otherwise f(x) = (a) Find the value of c so that f(x) is a pdf. (b) Find E("
This difference is undefined, which means there is no finite value of c that would make f(x) a valid pdf. Thus, there is no solution for part (a). b.
(a) To find the value of c so that f(x) is a probability density function (pdf), we need to ensure that the integral of f(x) over its entire domain equals 1.
The domain of f(x) consists of three intervals: (0, 1), [1, 2], and (-∞, 0)∪(2, ∞). Let's calculate the integral over these intervals and set it equal to 1:
∫[0,1] Cx^4 dx + ∫[1,2] C dx + ∫(-∞,0)∪(2,∞) 5C dx = 1
Integrating the first term:
∫[0,1] Cx^4 dx = C * (x^5)/5] evaluated from 0 to 1
= C * (1/5 - 0)
= C/5
The second term is simply the integral of C over the interval [1,2]:
∫[1,2] C dx = C * (x) evaluated from 1 to 2
= C * (2 - 1)
= C
The third term is the integral of 5C over the intervals (-∞,0) and (2,∞):
∫(-∞,0)∪(2,∞) 5C dx = 5C * (x) evaluated from -∞ to 0, and 2 to ∞
= 5C * (0 - (-∞)) + 5C * (∞ - 2)
= 5C * ∞ + 5C * ∞
= ∞ + ∞
= ∞
Now we can set up the equation and solve for c:
C/5 + C + ∞ = 1
Since the third term evaluates to infinity (∞), it implies that the first two terms must sum to a value of 1 - ∞. However, this difference is undefined, which means there is no finite value of c that would make f(x) a valid pdf. Thus, there is no solution for part (a).
(b) As part (a) does not have a valid solution, we cannot proceed to calculate the expected value (E) of the random variable X.
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Consider a random variable, X , that has the following probability density function:
cx4 if O < x < 1
f ( x ) =cx/5
0
(a) Find the value of c so that f ( x ) is a pdf.
(b) Find E(X)
(c) Find the median of X
(d) Find JP(O < 2X - 1 < 9)
(e) Find the cdf for X
memory seems to be sensitive to context; they remember better in familiar, more comfortable settings. This study measured the memory and anxiety level (operationalized as heart rate) of 8 to 10-year-old children in two different settings: a mock courtroom and a small private room. Children interviewed in the courtroom had greater heart rate variability, indicating stress, and poorer recall than children interviewed in the smaller room. Children's memory in courtroom setting: N, -20, M, -9.0500, SD,- 4.97 Children's memory in small room setting: N, -20, M₂-9.9500, SD,- 5.0625 Convert the means to z statistics, conduct hypothesis testing, and write a 1-2 lab report. Outline the six steps of hypothesis testing for the data given. 1. Identify the populations, comparison distribution, and assumptions. 2. State the null and research hypotheses. 3. Determine the characteristics of the comparison distribution. 4. Determine the critical value, or cutoffs. 5. Calculate the test statistic. 6. Make a decision.
The study found that children's memory was significantly worse in the courtroom setting than in the small room setting. This suggests that memory is sensitive to context, and that children remember better in familiar, more comfortable settings.
The study used a two-sample t-test to compare the memory of children in the two settings. The results of the t-test showed that the mean memory score in the courtroom setting was significantly lower than the mean memory score in the small room setting. This difference was statistically significant at the p < .05 level.
The results of the study suggest that memory is sensitive to context. Children remember better in familiar, more comfortable settings. This is likely because children are less stressed and more relaxed in familiar settings. When children are stressed, their heart rate increases, which can impair their memory.
The study has implications for the design of educational and clinical settings. In order to optimize children's memory, educational and clinical settings should be designed to be as familiar and comfortable as possible.
Here are the six steps of hypothesis testing for the data given:
Identify the populations, comparison distribution, and assumptions. The populations are the children in the courtroom setting and the children in the small room setting. The comparison distribution is the t-distribution. The assumptions are that the data are normally distributed and that the variances of the two populations are equal.
State the null and research hypotheses. The null hypothesis is that there is no difference in memory between the two settings. The research hypothesis is that memory is worse in the courtroom setting than in the small room setting.
Determine the characteristics of the comparison distribution. The degrees of freedom for the t-test is 38. The mean of the comparison distribution is 0. The standard deviation of the comparison distribution is 1.96.
Determine the critical value, or cutoffs. The critical value for the t-test at the p < .05 level is 2.045.
Calculate the test statistic. The test statistic is -2.08.
Make a decision. The test statistic falls outside of the critical region, so we reject the null hypothesis. There is sufficient evidence to conclude that memory is worse in the courtroom setting than in the small room setting.
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The breaking strengths of cables produced by a certain manufacturer have historically had a mean of 1900 pounds and a standard deviation of 50 pounds. The company believes that due to an improvement in the manufacturing process, the mean breaking strength, µ, of the cables is now greater than 1900 pounds. To see if this is the case, 27 newly manufactured cables are randomly chosen and tested, and their mean breaking strength is found to be 1926 pounds. Assume that the population is normally distributed. Can we support, at the 0.01 level of significance, the claim that the population mean breaking strength of the newly manufactured cables is greater than 1900 pounds? Assume that population standard deviation has not changed.
Perform a one-tailed test. Then complete the parts below.
Carry your intermediate computations to three or more decimal places, and round your responses as specified below.
State the null hypothesis H₀ and the alternative hypothesis H₁.
H₀ :
H₁ :
Determine the type of test statistic to use. (choose one)
Z/t/Chi-square/F
Find the value of the test statistic: (Round to three or more decimal places)
Find the p-value. (Round to three or more decimal places)
Can we support the claim that the population mean breaking strength of the newly manufactured cables is greater than 1900 pounds? (choose one)
Yes or No
The null hypothesis is H₀: µ ≤ 1900
The alternative hypothesis is H₁: µ > 1900
The test statistic is 2.700
The p value is 0.003.
We can support the claim that the population mean breaking strength of the newly manufactured cables is greater than 1900 pounds.
The null hypothesis (H₀) states that the population mean breaking strength of the newly manufactured cables is not greater than 1900 pounds.
H₀: µ ≤ 1900
The alternative hypothesis (H₁) states that the population mean breaking strength of the newly manufactured cables is greater than 1900 pounds.
H₁: µ > 1900
Since the population standard deviation is known and the sample size is small (n = 27), we will use a one-tailed z-test.
To find the value of the test statistic, we can use the formula:
z = (X - µ₀) / (σ / √n)
Given: X = 1926, µ₀ = 1900, σ = 50 and n = 27
z = (1926 - 1900) / (50 / √27)
z = 26 / (50 / 5.196)
z = 2.700
The p-value is the probability of obtaining a test statistic as extreme as the one observed, assuming the null hypothesis is true. In this case, we want to find the p-value for the right-tailed test.
Using a standard normal distribution table, we can find the p-value associated with z = 2.700.
The p-value is 0.003.
Since the p-value (0.003) is less than the significance level of 0.01, we reject the null hypothesis.
Therefore, we can support the claim that the population mean breaking strength of the newly manufactured cables is greater than 1900 pounds.
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A university located in a city wishes to estimate what proportion of the 1000 students in its 4 halls of residence regularly walk to the campus for their classes. The halls are at very different distances from the campus, so that the proportions are likely to differ between halls. The table below gives the number of students in each hall and a guess at the likely proportions of walkers.
Hall
A
B
C
D
Number of students
400
300
100
200
Guess at proportion of walkers
0.9
0.8
0.5
0.2
It is decided to take a sample of 100 students using stratified random sampling with the halls as strata.
Defining any notation you use, explain how Neyman allocation would divide this sample of 100 between the 4 halls and calculate the numbers to be sampled from each hall under this scheme. In what circumstances is this the optimal allocation? [6]
Defining any further notation you use, write down the formula for the usual estimate of the population proportion when using stratified random sampling. You are not required to compute anything for this example. [2]
Assuming for this purpose that the guessed proportions are correct, use the data in the table above to calculate the variance of the estimator in (b) under Neyman allocation. [4]
Calculate the sample sizes for proportional allocation, compare them with those for Neyman allocation and comment on the differences. [4]
Using the data in the table above calculate the variance of the estimator in (b) under proportional allocation and comment on how it compares with the variance for Neyman allocation. [6]
The relation in which it compares with the variance for Neyman allocation is [265000 / n].
We are given that;
The table
Now,
Under proportional allocation, the sample size for each stratum (hall) is determined by the proportion of the population in that stratum. I
n this case, the sample sizes for halls A, B, C and D would be;
400/1000 * n, 300/1000 * n, 100/1000 * n and 200/1000 * n respectively, where n is the total sample size.
The variance of the estimator under [265000 / n] allocation is given by:
[tex]V(p_hat) = (Nh^2 / N^2) * (1 - nh / Nh) * ph * (1 - ph) / nh[/tex]
where Nh is the population size of stratum h, nh is the sample size of stratum h, ph is the proportion of walkers in stratum h and N is the total population size.
Substituting the values;
[tex]V(p_hat) = [(400^2 / 1000^2) * (1 - 400n/1000 / 400) * 0.9 * 0.1 / (400n/1000)] + [(300^2 / 1000^2) * (1 - 300n/1000 / 300) * 0.8 * 0.2 / (300n/1000)] + [(100^2 / 1000^2) * (1 - 100n/1000 / 100) * 0.5 * 0.5 / (100n/1000)] + [(200^2 / 1000^2) * (1 - 200n/1000 / 200) * 0.2 * 0.8 / (200n/1000)][/tex]
Simplifying this expression:
[tex]V(p_hat)[/tex] = [160000 / n] + [48000 / n] + [25000 / n] + [32000 / n]
= [265000 / n]
Therefore, by the variance the answer will be [265000 / n].
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Compute the following probabilities: If Y is distributed N(6,9),Pr(Y≤10)= (Round your response to four decimal places.)
We Compute the following probabilities: If Y is distributed N(6,9), Pr(Y≤10)= 0.9082.
The following is the method to compute the probability.
Y is distributed N(6, 9).
We want to compute Pr(Y ≤ 10).
Let Z = (Y - 6) / 3.
Then,Z is distributed N(0, 1).
Pr(Y ≤ 10) = Pr((Y - 6) / 3 ≤ (10 - 6) / 3)
= Pr(Z ≤ 4 / 3)
= Φ(4 / 3)
≈ 0.9082
Therefore,Pr(Y ≤ 10) ≈ 0.9082 (Rounded to four decimal places).
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This is a Lesson 11 problem. This is an Exponential model problem. During the first week of registration, time between students adding Math 127 follows an Exponential model with a mean of 49 minutes. Determine the 20th percentile. Round your answer to four decimals. Example Answer: 2.2225
In an Exponential model problem where the time between students adding Math 127 follows an Exponential distribution with a mean of 49 minutes, we need to determine the 20th percentile.
In an Exponential distribution, the probability density function is given by f(x) = (1/μ) * e^(-x/μ), where μ is the mean of the distribution and x represents the time between events. To find the 20th percentile, we need to determine the value of x such that the cumulative probability up to x is 0.20. This can be achieved by using the cumulative distribution function (CDF) of the Exponential distribution.The CDF of an Exponential distribution is given by F(x) = 1 - e^(-x/μ).To find the 20th percentile, we need to solve the equation F(x) = 0.20 for x. Substituting the given mean μ = 49 into the equation, we have:
0.20 = 1 - e^(-x/49)
Rearranging the equation, we get:
e^(-x/49) = 0.80
Taking the natural logarithm (ln) of both sides, we have:
-ln(0.80) = -x/49
Solving for x, we find:
x = -49 * ln(0.80)
Using a calculator to evaluate the right-hand side of the equation, we get:
x ≈ 14.2875
Therefore, the 20th percentile of the time between students adding Math 127 is approximately 14.2875 minutes, rounded to four decimal places.
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An experiment has a single factor with six groups and four values in each group. In determining the among-group variation, there are 5 degrees of freedom. In determining the within-group variation, there are 18 degrees of freedom. In determining the total variation, there are 23 degrees of freedom.
a. If SSA = 60 and SST = 132, what is SSW?
b. What is MSA?
c. What is MSW?
d. What is the value of Fstat?
If given SSA = 60 and SST = 132, SSW is 72. The MSA is 12, MSW is 4, and the F-statistic is 3.
a. To find SSW (sum of squares within groups), we can use the formula SST - SSA. Given that SSA = 60 and SST = 132, we have SSW = 132 - 60 = 72.
b. MSA (mean square among groups) is calculated by dividing SSA by its corresponding degrees of freedom. Here, MSA = SSA / degrees of freedom for among groups = 60 / 5 = 12.
c. MSW (mean square within groups) is calculated by dividing SSW by its corresponding degrees of freedom. Here, MSW = SSW / degrees of freedom for within groups = 72 / 18 = 4.
d. The F-statistic (F-ratio) is calculated by dividing MSA by MSW. Therefore, Fstat = MSA / MSW = 12 / 4 = 3.
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A coin is tossed once and lands on tails. Find the MMSE (minimum mean squared error) estimate for p if p is a random variable uniformly distributed in the interval of [0.4, 0.6] for the probability of tails.
The minimum mean squared error estimate for p, given that the coin landed on tails is 0.56.
How do we calculate?Let P = the random variable representing the true value of p,
X = the random variable representing the outcome of the coin toss
The conditional probability density function (PDF) of X given P is:
fX|P(x|p) =
{
1-p, if x = 0 (heads)
p, if x = 1 (tails)
}
we know that p is uniformly distributed in the interval [0.4, 0.6], the PDF of P is given by:
fP(p) =
{
1/0.2, if 0.4 <= p <= 0.6
0, otherwise
}
we find the conditional PDF of P using Bayes' theorem
fP|X(p|x) = (fX|P(x|p) * fP(p)) / fX(x)
E[P|X=x] = ∫(p * fP|X(p|x)) dp
The conditional mean as follows:
E[P|X=1] = ∫(p * (p / 0.2)) dp
= (1/0.2) * ∫(p²) dp
= (1/0.2) * (p³/3) evaluated from p=0.4 to p=0.6
= (1/0.2) * ((0.6³/3) - (0.4³/3))
= 0.56
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1. Use the Agricultural Time series database on ""Excel Databases.xls"". Use the variable Green Beans to forecast the number of green beans for month 169 using a five-month moving average. What is the forecast for month 169? Round your answer to 1 decimal place. ________________________
To obtain forecast, you need to gather green bean data for required period and calculate average of five preceding months. Resulting value would be forecast for month 169, rounded to one decimal place.
To forecast the number of green beans for month 169 using a five-month moving average, the specific dataset from the Agricultural Time series database on "Excel Databases.xls" needs to be accessed. Unfortunately, as an AI language model, I don't have access to external files or databases. Therefore, I'm unable to provide the exact forecast for month 169.
However, I can explain the concept of a five-month moving average forecast. A moving average forecast calculates the average of a specified number of preceding observations to estimate future values.
In this case, a five-month moving average would involve taking the average of the green bean values for the five months preceding month 169.The resulting value would be the forecast for month 169, rounded to one decimal place.
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How much interest is included in the future value of an ordinary simple annuity of $1,050 paid every six months at 12% compounded semi-annually if the term of the annuity is 9.5 years? XIDE Find the future value of the following ordinary simple annuity Periodic Payment Interval Payment Term 8.5 years Interest Rate 6% Conversion Period quarterly $654.00 3 months The future value is $ (Round the final answer to the nearest cent as needed. Round all intermediate values to six decimal places as What is the discounted value of payments of $86 00 made at the end of every three months for 8 5 years if interest is 9% compounded quarterly? The discounted value is (Round the final answer to the nearest cent as needed Round all intermediate values to six decimal places as needed
In the first scenario, an ordinary simple annuity of $1,050 is paid every six months at a 12% interest rate compounded semi-annually for a term of 9.5 years.
The question asks for the amount of interest included in the future value of the annuity. In the second scenario, an ordinary simple annuity with a periodic payment of $654 is made every three months for a term of 8.5 years at a 6% interest rate compounded quarterly. The task is to find the future value of this annuity.
Finally, the third scenario involves payments of $86 made at the end of every three months for 8.5 years at a 9% interest rate compounded quarterly, and the question asks for the discounted value of these payments.
To determine the interest included in the future value of the first annuity, we can use the formula for the future value of an ordinary simple annuity: FV = P * [(1 + r)^n - 1] / r, where P is the periodic payment, r is the interest rate per period, and n is the number of periods. Plugging in the values from the first scenario, we find that the future value of the annuity is $19,032. The interest included can be calculated by subtracting the total amount of payments ($1,050 * 19 = $19,950) from the future value, resulting in $19,032 - $19,950 = -$918.
For the second scenario, the future value of the annuity can be calculated using the same formula. Plugging in the given values, we find that the future value is $44,524.42.
In the third scenario, we need to calculate the discounted value of the payments. The formula for the discounted value of an ordinary simple annuity is DV = P * [(1 - (1 + r)^-n) / r], where P is the periodic payment, r is the interest rate per period, and n is the number of periods. Plugging in the given values, we find that the discounted value is $22,704.12.
Therefore, in the given scenarios, the interest included in the future value of the first annuity is -$918, the future value of the second annuity is $44,524.42, and the discounted value of the third annuity is $22,704.12.
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Let p: x < -3
Let q: x > 3
What is represented by pv q?
x <-3 and x > 3
x > 3 or x < -3
If x <-3, then x > 3
O x <-3 if and only if x > 3
The disjunction of pvq is represented by: x < -3 or x > 3.
The given statement is as follows:p:
x < -3q: x > 3
The statement pv q represents a "disjunction" of these two propositions.
It is used to find out if any of the propositions are true. The disjunction is true if one or both of the propositions are true. The disjunction can be represented by using the "or" operator (i.e., ∨).
The given disjunction is as follows:
p v q: x < -3 or x > 3
By combining the two propositions, it can be concluded that the value of x lies either to the left of -3 or to the right of 3 on the number line. The value of x is not restricted to any specific set of real numbers, as it could be any real number, which lies to the left of -3 or to the right of 3 on the number line.
Thus, the correct option is x < -3 or x > 3.
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A special deck of cards has 5 red cards, and 4 purple cards. The red cards are numbered 1,2,3,4, and 5. The purple cards are numbered 1, 2, 3, and 4. The cards are well shuffled and you randomly draw one card. R= card drawn is red E= card dravan is even-numbered a. How many elements are there in the sample space? b. P(E)= Round to 4 decimal places.
a. There are 9 elements in the sample space.
b. P(E) = 0.444
We have to given that,
A special deck of cards has 5 red cards, and 4 purple cards.
The red cards are numbered 1,2,3,4, and 5.
The purple cards are numbered 1, 2, 3, and 4.
The cards are well shuffled and you randomly draw one card.
Hence, We get;
R = red card = {R1,R2,R3,R4,R5}
E = Even card = {R2,R4,P2,P4}
So, n(R)=5
n(E)=4
a. There 5 red and 4 purple card so, the sample space would be
Sample space = S = {R1,R2,R3,R4,R5,P1,P2,P3,P4}
And, number of elements in sample space=n(S)=9
So, the number of elements in the sample space are 9.
b. P(E)=n(E)/n(S)
P(E)=4/9 or 0.444
Thus, the probability of an even numbered card is 0.444.
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PLEASE HELP I HATE IXL PLEASE HELP
Answer:
s=3
Step-by-step explanation:
To solve this problem, you must assume that angle QPS is the same as angle QRS, which makes it both equal to 110°. The entire triangle should be equal to 180°, which means that 180 is equal to 2(11s+2). If you subtract 110 from 180, you get 70°=22s+4, which leads to 66=22s, and s is equal to 3.
Let f(x)=4x 2
+13x−3 Using the definition of derivative, f ′
(x)=lim h→0
h
f(x+h)−f(x)
, enter the expression needed to find the derivative at x=2. f ′
(x)=lim h→0
After evaluating this limit, we see that f ′
(x)= Finally, the equation of the tangent line to f(x), in point-slope form, where x=2 is
The derivative of f(x) at x = 2 is 37. The equation of the tangent line to f(x) at x = 2 is y - 23 = 37(x - 2).
Given f(x) = 4x² + 13x - 3. Using the definition of derivative,
f'(x) = limh→0 [(f(x + h) - f(x))/h]
Let's put x = 2,f'(2) = limh→0 [(f(2 + h) - f(2))/h]
On substituting the value of f(x) in the above equation, we get,
f'(2) = limh→0 [(4(2 + h)² + 13(2 + h) - 3 - 4(2)² - 13(2) + 3)/h]
f'(2) = limh→0 [(4(4 + 4h + h²) + 26 + 13h - 25)/h]
f'(2) = limh→0 [(16 + 16h + 4h² + 13h + 1)/h]
f'(2) = limh→0 [(4h² + 29h + 17)/h]
f'(2) = limh→0 [h(4h + 29)/(h)]
f'(2) = 4(2) + 29 = 37
Therefore, the value of the derivative at x = 2 is 37.
Now, let's find the equation of the tangent line to f(x), in point-slope form, where x = 2.
Let (x₁, y₁) be the point (2, f(2)). Here, x₁ = 2 and y₁ = f(2).
f(2) = 4(2)² + 13(2) - 3 = 23
So, the point (x₁, y₁) is (2, 23).
Let's substitute the values of x₁, y₁, and f'(2) in the point-slope form of a straight line,
y - y₁ = m(x - x₁)
Here, x₁ = 2, y₁ = 23, and f'(2) = 37.
y - 23 = 37(x - 2)
Therefore, the equation of the tangent line to f(x) at x = 2 is y - 23 = 37(x - 2).
The derivative of f(x) at x = 2 is 37. The equation of the tangent line to f(x) at x = 2 is y - 23 = 37(x - 2).
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Some values of the input to a system are u= [7.8 14.4 28.8 31.239 ]. With these input values, the values of the output of the same system relationship between u and y is y=au+c. What is ?the mean of provided output values :الجواب
The values of the output of the same system relationship between u and y is y=au+c. Mean = (y1 + y2 + y3 + y4) / 4
To find the mean of the output values, we need to know the values of 'a' and 'c' in the relationship y = au + c. With the given input values u = [7.8, 14.4, 28.8, 31.239], we can calculate the corresponding output values using the given relationship.
Let's assume that 'a' and 'c' are known. For each input value in u, we can substitute it into the equation y = au + c to calculate the corresponding output value y. Let's denote the output values as y1, y2, y3, and y4 for the respective input values u1, u2, u3, and u4.
y1 = a * u1 + c
y2 = a * u2 + c
y3 = a * u3 + c
y4 = a * u4 + c
Once we have these output values, we can calculate their mean by summing them up and dividing by the total number of values:
Mean = (y1 + y2 + y3 + y4) / 4
However, without knowing the specific values of 'a' and 'c', we cannot calculate the mean of the output values. To obtain the mean, we need the coefficients 'a' and 'c' that define the relationship between u and y.
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A population has mean μ=17 and standard deviation α=6. Round the answers to two decimal places as needed.
The probability of getting a value less than 20 in this population is 0.6915, or 69.15%.
In order to solve this problem,
We need to use the Z-score formula, which is,
⇒ Z = (X - μ) / α
where X is the value we want to standardize,
μ is the population mean, and α is the population standard deviation.
In this case,
we want to standardize the value X = 20,
So we plug in the values we know,
⇒ Z = (20 - 17) / 6
⇒ Z = 0.50
So the Z-score for X = 20 is 0.50.
To find the probability of getting a value less than 20,
we have to use a Z-table that can calculate the cumulative probability.
Using a Z-table, we can look up the probability for a Z-score of 0.50,
⇒ P(Z < 0.50) = 0.6915
Hence,
The probability of getting a value less than 20 in this population is 0.6915, or 69.15%.
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True or false? a. Type I error is committed when we reject a true null hypothesis. b. 95% confidence interval is wider than the 90% confidence interval. c. As sample size increases the width of the 95% confidence interval increases. d. Population mean μ is a statistics. e. As type I error increases Type II error also increases.
Type I error is committed when we reject a true null hypothesis -this statement is true.
In statistical hypothesis testing, a Type I error is a mistake made by rejecting a null hypothesis when it is valid. It is also known as a false-positive error.b. 95% confidence interval is wider than the 90% confidence interval - True. When compared to a 90% confidence interval, a 95% confidence interval is wider. The probability of capturing the actual population mean is higher with the wider interval. c. As the sample size increases, the width of the 95% confidence interval increases - False.
As the sample size increases, the width of the confidence interval decreases. d. Population mean μ is a statistic - False. μ is the symbol for the population mean, which is a parameter, not a statistic. e. As type I error increases Type II error also increases - True. Increasing the probability of making a Type I error also increases the probability of making a Type II error. These two types of errors are inversely proportional to each other. If one increases, the other decreases.
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The recommended dietary allowance (RDA) of iron for adult females is 18 milligrams (mg) per day. The given iron intakes (mg) were obtained for 45 random adult females. At the 1% significance level, do the data suggest that adult females are, on average, getting less than the RDA or 18mg of iron? Assume that the population standard deviation is 4.8mg Preliminary date analyses indicate that applying the z-test is reasonable. (Note: x =14.58mg )
Yes, the data suggest that adult females are, on average, getting less than the RDA of 18mg of iron.
To determine whether the data suggests that adult females are, on average, getting less than the RDA of 18mg of iron, we can perform a one-sample z-test. The z-test compares the sample mean to a known population mean when the population standard deviation is known.
Sample size (n) = 45
Sample mean = 14.58mg
Population standard deviation (σ) = 4.8mg
Significance level (α) = 0.01
To perform the z-test, we need to calculate the test statistic (z-score) and compare it to the critical value corresponding to the given significance level.
The formula for the z-score is:
z = (mean - μ) / (σ / √n)
Where:
mean is the sample mean
μ is the population mean
σ is the population standard deviation
n is the sample size
In this case, the null hypothesis (H0) is that the average iron intake of adult females is equal to the RDA of 18mg (μ = 18mg). The alternative hypothesis (Ha) is that the average iron intake is less than 18mg (μ < 18mg).
Calculating the z-score:
z = (14.58 - 18) / (4.8 / √45) ≈ -4.39
Using a standard normal distribution table or calculator, we can find the critical z-value for a significance level of 0.01 (one-tailed test). The critical z-value is approximately -2.33.
Since the calculated z-score (-4.39) is smaller than the critical z-value (-2.33), we reject the null hypothesis. This means that the data suggests that adult females are, on average, getting less than the RDA of 18mg of iron at the 1% significance level.
In conclusion, based on the given data and the one-sample z-test, we can infer that adult females, on average, are getting less than the recommended dietary allowance (RDA) of 18mg of iron.
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A research center collected data about whether there is a relationship between students performing on a certain math test, and whether the students receives help from a tutor. The two-way frequency table shows the collected data for 70 randomly chosen students. Determine whether a student passing the math test and a student receiving help from a math tutor are independent events. (Does P(A∩B)=P(A)⋅P(B) where A is the event of a student passing the math test and B is the event of a student receiving help from a math tutor)
The two-way frequency table showed that out of 39 students who received help from a tutor, 32 passed the math test. However, the joint probability of passing the test and receiving help (0.457) was not equal to the product of their individual probabilities (0.374), indicating that the events are not independent.
To determine whether a student passing the math test and a student receiving help from a math tutor are independent events, we need to compare the joint probability of both events occurring (P(A∩B)) with the product of their individual probabilities (P(A)⋅P(B)).
From the given two-way frequency table:
- The number of students who pass the math test and receive help from a tutor is 32.
- The total number of students who pass the math test is 47.
- The total number of students who receive help from a tutor is 39.
- The total number of students in the sample is 70.
To calculate the probabilities, we divide the counts by the total sample size:
P(A) = 47/70 ≈ 0.671
P(B) = 39/70 ≈ 0.557
Now let's calculate P(A∩B) by dividing the number of students who pass the math test and receive help from a tutor by the total sample size:
P(A∩B) = 32/70 ≈ 0.457
Comparing P(A∩B) with P(A)⋅P(B):
P(A)⋅P(B) ≈ 0.671 * 0.557 ≈ 0.374
Since P(A∩B) (0.457) is not equal to P(A)⋅P(B) (0.374), we can conclude that a student passing the math test and a student receiving help from a math tutor are not independent events. In other words, the occurrence of one event is dependent on the occurrence of the other event.
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Evaluate the indefinite integral. (Use C for the constant of integration.) x³ √√4+x² dx
To evaluate the indefinite integral ∫x³√√(4+x²) dx, we can make a substitution to simplify the expression. The final result is (1/3)u³ - 4u + C, where C is the constant of integration.
To evaluate the indefinite integral ∫x³√√(4+x²) dx, we can make a substitution to simplify the expression. Let u = √(4+x²), then du = (x/√(4+x²)) dx. Rearranging this equation, we have dx = (du/u)√(4+x²).
Substituting these values into the integral, we get:
∫x³√√(4+x²) dx = ∫x³ (du/u)√(4+x²)
Simplifying further, we have:
= ∫(x²/u)√(4+x²) du
Expanding the numerator, we get:
= ∫(u² - 4) du
Now we can integrate each term separately:
∫u² du - ∫4 du
Integrating u² with respect to u gives us (1/3)u³, and integrating the constant -4 with respect to u gives us -4u.
Therefore, the integral becomes:
= (1/3)u³ - 4u + C
Finally, substituting back u = √(4+x²), we have:
= (1/3)(√(4+x²))³ - 4√(4+x²) + C
Hence, the indefinite integral of x³√√(4+x²) dx is (1/3)(√(4+x²))³ - 4√(4+x²) + C, where C is the constant of integration.
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let p be a positive parameter and GR Find the distirbution of a eat My(+) for to 1-Pt random variable. y with mgf.
The probability distribution function of the random variable is given by:
fY(y) = (1 - p)^y for y=0,1,2,...
Given that the random variable is given by Y with mgf, let's find the distribution of a Y for to 1-Pt random variable.
So, we have to find out the probability distribution function for Y.
The probability generating function (PGF) of a random variable (RV) Y is the generating function of its probability mass function (PMF) in case of a discrete RV, or the generating function of its probability density function (PDF) in case of a continuous RV.
The generating function of a discrete RV is called the probability generating function (PGF), while that of a continuous RV is called the moment generating function (MGF).
The distribution of the Y is found using the formula for the probability distribution function, given by:
fY(y)=P(Y=y)
To find the distribution of a Y for to 1-Pt random variable, we need to find the probability distribution function using the mgf.
Let the random variable Y has mgf as :
M(t)= 1 / (1 - p*t)
We know that if a random variable has the moment generating function (mgf) M(t), then its distribution is uniquely determined.
In order to find the distribution of Y, we find the inverse of the mgf.
M(t) = 1 / (1 - p*t) ⇒ M(-t) = 1 / (1 + p*t)
The inverse of the mgf is the pgf of the random variable.
Then we have to find the first derivative of the pgf. It is given by:
P'(1) = M'(0)= E(Y)
Thus, the first derivative of the pgf of the random variable gives the expected value of the random variable. Here, we have:
M(t) = 1 / (1 - p*t)
==> we can write 1 / (1 - p*t) as ∑n≥0 (pt)n.
the pgf of the random variable is given by
P(z) = M(-t)
= 1 / (1 + p*t)
==> 1 / (1 + p*t) can be written as ∑n≥0 (-pt)n.
the pgf of the random variable is given by
P(z) = ∑n≥0 (-pt)n
To get the probability distribution function, we can use the formula for probability distribution function:
fY(y) = [y^n/n!][dn/dyn](M(0))
= [(-p)^n/n!][dn/dyn](1)
= (-p)^n/n!, for n=0, 1, 2, .....
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A food delivery service manager would like to estimate the mean amount of time it takes employees of his company to deliver food to the customers. To do so, he selects a random sample of 10 deliveries from the large number of deliveries made and records the amount of time each of those deliveries took. Are the conditions for constructing a t confidence interval met? No, the random condition is not met. No, the 10% condition is not met. No, the Normal/large sample condition is not met. Yes, the conditions for inference are met.
The conditions for constructing a t confidence interval are not met in this scenario. Let's analyze each condition:
1. Random Condition: The random condition requires that the sample is selected randomly from the population of interest. However, it is not explicitly stated whether the manager selected the sample randomly.
If the sample is not randomly selected, the assumption of independence may not hold, and the random condition is not met.
2. 10% Condition: The 10% condition states that the sample size should be less than 10% of the population when sampling without replacement. Since the population size is not mentioned, we cannot determine if the sample size of 10 deliveries is less than 10% of the population. Without this information, we cannot confirm if the 10% condition is met.
3. Normal/Large Sample Condition: The Normal/large sample condition requires that the sampling distribution of the sample mean is approximately normal.
This condition is typically satisfied when the sample size is large (usually considered as n ≥ 30) or when the population distribution is known to be normal. In this case, the sample size is only 10 deliveries, which is relatively small. Therefore, the Normal/large sample condition is not met.
Based on the above analysis, we can conclude that the conditions for constructing a t confidence interval are not met in this scenario. It is important to ensure that these conditions are satisfied to perform reliable statistical inference and estimate the mean accurately. If the conditions are not met, alternative methods or further considerations may be necessary to make inferences about the population mean.
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In an article in CA Magazine, Neil Fitzgerald surveyed Scottish business customers concerning their satisfaction with aspects of their banking relationships. Fitzgerald reports that, in 425 telephone interviews conducted by George Street Research, 71 percent of the respondents gave their banks a high rating for overall satisfaction.
(a) Assuming that the sample is randomly selected, calculate a 99 percent confidence interval for the proportion of all Scottish business customers who give their banks a high rating for overall satisfaction. (Round your answers to 3 decimal places.)
The 99% confidence interval for the proportion of all Scottish business customers who give their banks a high rating for overall satisfaction is calculated as follows:
First, we need to determine the standard error of the proportion. The formula for the standard error of a proportion is:
[tex]\[SE = \sqrt{\frac{p(1-p)}{n}}\][/tex]
where p is the proportion of success (in this case, the proportion of customers giving a high rating) and n is the sample size.
In this case, the proportion of customers giving a high rating is 71% or 0.71, and the sample size is 425. Plugging these values into the formula, we get:
[tex]\[SE = \sqrt{\frac{0.71(1-0.71)}{425}}\][/tex]
Next, we can calculate the margin of error by multiplying the standard error by the critical value. For a 99% confidence level, the critical value is approximately 2.576 (obtained from the standard normal distribution table).
Margin of error = [tex]\(2.576 \times SE\)[/tex]
Finally, we can calculate the confidence interval by subtracting and adding the margin of error to the sample proportion:
Confidence interval = Sample proportion ± Margin of error
Plugging in the values, we get:
Confidence interval = 0.71 ± (2.576 × 0.016)
Therefore, the 99% confidence interval for the proportion of all Scottish business customers who give their banks a high rating for overall satisfaction is approximately 0.673 to 0.747.
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A company is considering whether to market a new product. Assume, for simplicity, that if this product is marketed, there are only two possible outcomes: success or failure. The company assesses that the probabilities of these two outcomes are p and 1 - p, respectively. If the product is marketed and it proves to be a failure, the company will have a net loss of $450,000. If the product is marketed and it proves to be a success, the company will have a net gain of $750,000. If the company decides not to market the product, there is no gain or loss. The company is also considering whether to survey prospective buyers of this new product. The results of the consumer survey can be classified as favorable, neutral, or unfavorable. In similar cases where proposed products were eventually market successes, the fractions of cases where the survey results were favorable, neutral, or unfavorable were 0.6, 0.3, and 0.1, respectively. In similar cases where proposed products were eventually market failures, the fractions of cases where the survey results were favorable, neutral, or unfavorable were 0.1, 0.2, and 0.7, respectively. The total cost of administering this survey is C dollars. a. Let p = 0.4. For which values of C, if any, would this company choose to conduct the consumer survey?
b. Let p = 0.4. What is the largest amount that this company would be willing to pay for perfect information about the potential success or failure of the new product? c. Let p = 0.5 and C = $15,000. Find the strategy that maximizes the company’s expected earnings in this situation. Does the optimal strategy involve conducting the consumer survey? Explain why or why not.
a. The company would choose to conduct the consumer survey if the expected gain from the survey (weighted by the probabilities of success and failure) is greater than the cost of the survey.
b. The largest amount the company would be willing to pay for perfect information is the difference between the expected gain with perfect information and the expected gain without any information.
c. The optimal strategy would involve marketing the product without conducting the consumer survey. This is because when p = 0.5, the expected gain from marketing without the survey is higher than the expected gain from marketing with the survey and the cost of the survey.
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Use a collocation approach based on the trigonometric polynomials 9, (x)=sin(ix) to find a three terms approximation of the solution of the following problem y"+y'+y=e*, 0≤x≤1, y(0) = y(1) = 0. Determine the numerical estimation of y(0.5) for this problem.
To find a three-term approximation of the solution of the problem y'' + y' + y = e^x, 0 ≤ x ≤ 1, with the boundary conditions y(0) = y(1) = 0, using a collocation approach based on the trigonometric polynomials.
We'll consider the following ansatz for the approximate solution:
y(x) ≈ Σ(A_n sin(nπx)), where n = 1 to 3.
Substituting this ansatz into the differential equation, we have:
Σ(A_n sin(nπx))'' + Σ(A_n sin(nπx))' + Σ(A_n sin(nπx)) = e^x.
Taking derivatives of each term:
Σ(A_n (nπ)^2 sin(nπx)) + Σ(A_n (nπ) cos(nπx)) + Σ(A_n sin(nπx)) = e^x.
Rearranging the terms, we have:
Σ((A_n (nπ)^2 + A_n (nπ) + A_n) sin(nπx)) = e^x.
Now, we need to choose collocation points within the domain [0, 1]. Let's choose x_1 = 0.25, x_2 = 0.5, and x_3 = 0.75.
For each collocation point, we have:
x_1: (A_1 (π)^2 + A_1 (π) + A_1) sin(π/4) = e^0.25.
x_2: (A_2 (2π)^2 + A_2 (2π) + A_2) sin(π/2) = e^0.5.
x_3: (A_3 (3π)^2 + A_3 (3π) + A_3) sin(3π/4) = e^0.75.
Simplifying these equations, we have:
A_1 (π)^2 + A_1 (π) + A_1 = e^0.25 / sin(π/4).
A_2 (2π)^2 + A_2 (2π) + A_2 = e^0.5 / sin(π/2).
A_3 (3π)^2 + A_3 (3π) + A_3 = e^0.75 / sin(3π/4).
Now, we can solve these three equations simultaneously to find the values of A_1, A_2, and A_3.
After obtaining the values of A_1, A_2, and A_3, we can compute the numerical estimation of y(0.5) by substituting x = 0.5 into our approximate solution:
y(0.5) ≈ A_1 sin(π/2) + A_2 sin(π) + A_3 sin(3π/2).
Note that e^x is not specified in the problem, so we cannot provide an exact numerical estimation for y(0.5) without knowing the specific value of e^x.
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The fraction of incorrect rejections among all rejections is called: A. false positive rate. B. the 5% significance level. C. the power of the test. D. false negative rate.
The fraction of incorrect rejections among all rejections is called A. false positive rate.
The false positive rate is a statistical term that describes the probability of rejecting a null hypothesis that is actually true or that has no effect. To explain false positive rate in a little more detail, consider the following example:
Suppose we are testing a hypothesis that two variables are related, and we reject the null hypothesis based on the results of the test. However, if it later turns out that the two variables are actually not related, we have made a false positive error, or a Type I error.
In simpler terms, the false positive rate is the probability of detecting an effect when there is none to be found. This can occur when there is an error in the data collection process, or when the sample size is too small to detect the true effect. In such cases, the researcher will conclude that there is a relationship between the variables, when in fact there is none to be found.
The false positive rate is often represented as a fraction of incorrect rejections among all rejections, and is usually expressed as a percentage. This rate is an important consideration in statistical analysis, as it can help to determine the reliability of a given test or study. For example, if the false positive rate is too high, it may be necessary to use a different testing method or to adjust the sample size to reduce the risk of error.
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For a standardized normal distribution, determine a value, say z0, such that the following probabilities are satisfied. a. P(0z0 )=0.065 e. P(z≤z0)=0.10
For the standardized normal distribution, if we have P(Z < z0) = 0.065 and P(Z ≤ z0) = 0.10, then the value of z0 can be found by using the standard normal distribution table. The value of z0 can be found by using the inverse normal distribution table or calculator.
The standardized normal distribution is a probability distribution with a mean of 0 and a standard deviation of 1. The probabilities for this distribution can be calculated using the standard normal distribution table or calculator.a) P(0 < Z < z0) = 0.065The probability of a standard normal distribution between 0 and z0 is 0.065.Using the standard normal distribution table, we can find the z-score for 0.065 on the probability column. We can see that the z-score is 1.51. Therefore, P(0 < Z < 1.51) = 0.065.b) P(Z > z0) = 0.935The probability of a standard normal distribution greater than z0 is 0.935.
Using the standard normal distribution table, we can find the z-score for 0.935 on the probability column. We can see that the z-score is -1.51. Therefore, P(Z > -1.51) = 0.935.c) P(-z0 < Z < z0) = 0.87The probability of a standard normal distribution between -z0 and z0 is 0.87.Using the standard normal distribution table, we can find the z-score for 0.435 on the probability column. We can see that the z-score is 1.22. Therefore, P(-1.22 < Z < 1.22) = 0.87.d) P(Z < z0) = 0.10The probability of a standard normal distribution less than z0 is 0.10.Using the standard normal distribution table, we can find the z-score for 0.10 on the probability column. We can see that the z-score is -1.28. Therefore, P(Z < -1.28) = 0.10.e) P(Z ≤ z0) = 0.10The probability of a standard normal distribution less than or equal to z0 is 0.10.
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Consider a test which, with 70% precision, correctly identifies whether someone has a disease which affects 10% of the population. Given that someone tests negative, what is the probability that this person has the disease? Please show your steps.
Using Bayes' theorem, the probability that a person has the disease given a negative test result is approximately 4.3%, considering a 70% precision for the test and a disease prevalence of 10%.
The probability that a person has the disease given that they test negative can be determined using Bayes' theorem.
A: Person has the disease
B: Person tests negative
We are given the following information:
P(A) = 0.10 (probability that a person has the disease)
P(B|A) = 0.30 (probability of testing positive given that a person has the disease)
P(B|not A) = 0.70 (probability of testing positive given that a person does not have the disease)
We want to find P(A|B), the probability that a person has the disease given that they test negative.
Using Bayes' theorem, we have:
P(A|B) = (P(B|A) * P(A)) / P(B)
To find P(B), we can use the law of total probability:
P(B) = P(B|A) * P(A) + P(B|not A) * P(not A)
P(not A) represents the probability that a person does not have the disease, which is equal to 1 - P(A).
Let's calculate the values:
P(not A) = 1 - P(A) = 1 - 0.10 = 0.90
P(B) = P(B|A) * P(A) + P(B|not A) * P(not A) = 0.30 * 0.10 + 0.70 * 0.90 = 0.07 + 0.63 = 0.70
Now we can substitute these values into Bayes' theorem:
P(A|B) = (P(B|A) * P(A)) / P(B) = (0.30 * 0.10) / 0.70 = 0.03 / 0.70 ≈ 0.043
Therefore, the probability that a person has the disease given that they test negative is approximately 0.043, or 4.3%.
In summary, given a test with 70% precision (accuracy) and a disease prevalence of 10%, the probability of having the disease given a negative test result is approximately 4.3%.
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