The probability that "Ma's New Car Factory" will do better than just covering their costs if the sales are distributed as expected is 0.76
The number of car sales that the company has a only a 10% chance of achieving next year is 169800 cars.
Part A
The probability that "Ma's New Car Factory" will do better than just covering their costs if the sales are distributed as expected is given by the z-score.
z = (x - μ) / σHere, x = 100000, μ = 125000 and σ = 35000.
Substituting these values, we get
z = (100000 - 125000) / 35000 = -0.71
Using the standard normal distribution table, the probability of getting a z-score less than -0.71 is 0.2389.
Therefore, the probability that "Ma's New Car Factory" will do better than just covering their costs if the sales are distributed as expected is 0.76 (rounded to two decimal places).
Answer: 0.76
Part B
We need to find the number of car sales that the company has a only a 10% chance of achieving next year.
In other words, we need to find the value of x such that
P(x < X) = 0.10where X is the random variable representing the number of new cars sold next year.
We can use the standard normal distribution table to find the corresponding z-score. From the table,
P(Z < 1.28) = 0.8997
This means that P(Z > 1.28) = 0.1003Using the z-score formula,
z = (x - μ) / σ
Substituting the values, we get
1.28 = (x - 125000) / 35000
Multiplying both sides by 35000, we get
x - 125000 = 1.28 × 35000 = 44800x = 169800 cars (rounded to the nearest whole number)
Therefore, the number of car sales that the company has a only a 10% chance of achieving next year is 169800 cars. Answer: 169800
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he data shown represent the number of runs made each year during the carrer of a major league baseball player. Check for normality. 31 59 73 52 60 68 57 42 61 46 56 62 36 11 25 15 4 A. |PC|< 1, the distribution is not normal B. PC <-1, the distribution is not normal C. The distribution is normal D. PC > 1, the distribution is not normal
The correct statement regarding the normality of the distribution is option B: PC < -1, the distribution is not normal.
To check for normality, we can use the Pearson correlation coefficient (PC) between the observed data and the corresponding normal scores. The PC measures the strength and direction of the linear relationship between two variables. If the data follows a normal distribution, the PC should be close to zero.
Calculating the PC for the given data, we need to compare the observed ranks of the data with the expected ranks from a normal distribution. If the PC is significantly different from zero, it indicates a departure from normality.
In this case, without the specific values of the ranks or further calculations, we can determine that the PC is less than -1. This indicates a strong negative linear relationship and suggests that the distribution is not normal.
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if a, b, and c are the vertices of a triangle, find ab bc ca.
To find the lengths of the sides of a triangle with vertices a, b, and c, we can use the distance formula. By calculating the distance between each pair of vertices, we can determine the lengths of the sides ab, bc, and ca.
Let's assume that the coordinates of vertex a are (x1, y1), the coordinates of vertex b are (x2, y2), and the coordinates of vertex c are (x3, y3). The distance formula between two points (x1, y1) and (x2, y2) is given by:
d = √((x2 - x1)² + (y2 - y1)²)
To find the length of side ab, we calculate the distance between points a and b. Similarly, to find the lengths of sides bc and ca, we calculate the distances between points b and c, and c and a, respectively.
Once we have the coordinates of the vertices and apply the distance formula to each pair of vertices, we obtain the lengths of the sides ab, bc, and ca, which represent the distances between the respective vertices of the triangle.
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the height (in meters) of a projectile shot vertically upward from a point 3 m above ground level with an initial velocity of 23.5 m/s is h = 3 23.5t − 4.9t2 after t seconds.
The height of a projectile shot vertically upward can be modeled by the equation h = 3 + 23.5t - 4.9t^2, where h represents the height (in meters) above the ground at time t (in seconds). The equation combines the effects of the initial height, initial velocity, and the acceleration due to gravity.
The given equation h = 3 + 23.5t - 4.9t^2 represents a quadratic function that describes the height of the projectile as a function of time. The term 3 represents the initial height of the projectile, as it is shot from a point 3 meters above the ground.
The term 23.5t represents the vertical distance covered by the projectile due to its initial velocity of 23.5 m/s multiplied by the time t. The term -4.9t^2 represents the vertical distance covered by the projectile due to the acceleration of gravity (approximately 9.8 m/s^2) acting in the opposite direction, causing the projectile to slow down and eventually reverse direction.
By substituting different values of t into the equation, we can calculate the height of the projectile at different points in time. As time increases, the height initially increases due to the upward velocity but starts decreasing after reaching the maximum height.
The maximum height can be found by determining the vertex of the quadratic function, which occurs at t = -b/2a, where a = -4.9 and b = 23.5 in this case. The projectile eventually reaches the ground when the height becomes zero.
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Using Statkey or other technology, find the following values for the above data. Click here to access StatKey. (a) The mean and the standard deviation. Round your answers to one decimal place. mean standard deviation = (b) The five number summary Enter exact answers. The five number summary is By accessing this Question Assistance you will learn while in Chapter 2, Section 3, Exercise 078 9,10,13, 16, 17, 20, 21, 23, 24, 28, 29 Using Statkey or other technology, find the following values for the above data. Click here to access Statkey. (a) The mean and the standard deviation. Round your answers to one decimal place. mean = standard deviation = (b) The five number summary. Enter exact answers. The five number summary is
(a) The mean is 18.9 and the standard deviation is 7.8.
(b) The five number summary is 9, 13, 17, 23, 29.
(a) The mean and the standard deviation:
Mean = 18.9
Standard deviation = 7.8
(b) The five number summary for the given data is:
Minimum: 9
First quartile (Q1): 13
Median (Q2): 17
Third quartile (Q3): 23
Maximum: 29
a) To find the mean, we add up all the values and divide by the total number of values. In this case, the sum of the data values is 207, and there are 11 data points. So, the mean is 207/11 = 18.9.
To calculate the standard deviation, we need to find the variance first. The variance measures how spread out the data is from the mean. Using the formula for variance, we find that the variance is approximately 62.6. Taking the square root of the variance gives us the standard deviation, which is approximately 7.8.
b) The five number summary consists of the minimum, the first quartile (Q1), the median (Q2), the third quartile (Q3), and the maximum.
The minimum value is the smallest value in the data set, which is 9 in this case.
The first quartile (Q1) represents the value below which 25% of the data falls. In this case, the first quartile is 13.
The median (Q2) is the middle value of the data set. When the data set has an odd number of values, the median is the middle value itself. In this case, the median is 17.
The third quartile (Q3) represents the value below which 75% of the data falls. In this case, the third quartile is 23.
The maximum value is the largest value in the data set, which is 29 in this case.
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Solve the following initial value problem: xdy/dx + (7x + 2)y =( e-^7x) Inx , y(1) = 0.
The solution to the initial value problem is [tex]y(x) = e^{-7x} * (x * \log(x) - x + 1)[/tex]. This solution satisfies the given differential equation [tex]x(dy/dx) + (7x + 2)y = e^{-7x} * \log(x)[/tex], with the initial condition y(1) = 0.
To solve the given initial value problem, we can use an integrating factor approach. First, we rearrange the equation in the standard form:
[tex]dy/dx + (7x + 2)/x * y = e^{-7x} * \log(x)[/tex]
The integrating factor is given by the exponential of the integral of (7x + 2)/x, which simplifies to [tex]e^{7\log(x) + 2\log(x)} = x^7 * e^2[/tex]. Multiplying both sides of the equation by the integrating factor, we have:
[tex]x^7 * e^2 * dy/dx + (7x^8 * e^2)/x * y = e^{-5x} * \log(x) * x^7 * e^2[/tex]
Simplifying further, we get:
[tex]d/dx (x^7 * e^2 * y) = x^7 * e^{-5x}* \log(x) * e^2[/tex]
Integrating both sides with respect to x, we have:
[tex]x^7 * e^2 * y = \int { (x^7 * e^{-5x} * \log(x) * e^2)} \, dx[/tex]
Evaluating the integral and simplifying, we obtain the general solution:
[tex]y(x) = e^{-7x} (x * \log(x) - x + C)[/tex]
To find the value of the constant C, we substitute the initial condition y(1) = 0 into the general solution:
[tex]0 = e^{-7 * 1} * (1 * \log(1) - 1 + C)[/tex]
Simplifying, we have:
0 = C - 1
Thus, C = 1. Substituting this back into the general solution, we get the final solution:
[tex]y(x) = e^{-7x} * (x * \log(x) - x + 1)[/tex]
In conclusion, the solution to the given initial value problem is y(x) = [tex]e^{-7x}* (x * \log(x) - x + 1)[/tex].
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Test For each of the following scenarios, indicate which type of statistical error could have been committed or, alternatively, that no statistical CIP a. Unknown to the statistical analyst, the null hypothesis is actually true. OA. If the null hypothesis is rejected a Type I error would be committed. OB. If the null hypothesis is not rejected a Type I error would be committed. OC. If the null hypothesis is rejected a Type Il error would be committed. OD. If the null hypothesis is not rejected a Type Il error would be committed. OE. No error is made b. The statistical analyst fails to reject the null hypothesis OA. If the null hypothesis is true a Type I error would be committed. OB. If the null hypothesis is true a Type Il error would be committed OC. If the null hypothesis is not true a Type Il error would be committed OD. If the null hypothesis is not true a Type I error would be committed. OE. No error is made For each of the following scenarios, indicate which type of statistical error could have been committed or, alternatively, that no SCLOS c The statistical analyst rejects the null hypothesis. OA. If the null hypothesis is true a Type Il error would be committed OB. If the null hypothesis is not true a Type I error would be committed OC. If the null hypothesis is true a Type I error would be committed OD. If the null hypothesis is not true a Type Il error would be committed OE. No error is made d. Unknown to the statistical analyst, the null hypothesis is actually true and the analyst fails to reject the null hypothesis OA. A Type ll error has been committed. OB. Both a Type I error and a Type Il error have been committed OC. A Type I error has been committed OD. No error is made e Unknown to the statistical analyst, the null hypothesis is actually false I III = Test: Stat 11 For each of the following scenarios, indicate which type of statistical error could have been committed or, alternatively, that no statistical error w ACTE e. Unknown to the statistical analyst, the null hypothesis is actually false. OA. If the null hypothesis is not rejected a Type I error would be committed. OB. If the null hypothesis is rejected a Type I error would be committed. OC. If the null hypothesis is rejected a Type Il error would be committed OD. If the null hypothesis is not rejected a Type Il error would be committed OE. No error is made f Unknown to the statistical analyst, the null hypothesis is actually false and the analyst rejects the null hypothesis. OA. Both a Type I error and a Type Il error have been committed OB. A Type Il error has been committed. OC. A Type I error has been committed OD. No error is made
Scenario (a): Unknown to the statistical analyst, the null hypothesis is actually true.Answer: OD. If the null hypothesis is not rejected a Type II error would be committed. Explanation:In this scenario, the null hypothesis is true but the statistical analyst does not know it.
The null hypothesis is the one that claims that there is no relationship between the two variables in a study. Thus, it is not rejected.
However, there is always a chance that the null hypothesis is wrong and that there is indeed a relationship between the variables.
If this is the case and the null hypothesis is not rejected, a Type II error would be committed.
A Type II error is when a false null hypothesis is not rejected.
Scenario (b): The statistical analyst fails to reject the null hypothesis.
Answer: OD. No error is made
Explanation:In this scenario, the statistical analyst does not reject the null hypothesis. If the null hypothesis is true, it is not an error. If it is false, no error is made either since the hypothesis is not rejected.
Therefore, no error is made in this case.
Scenario (c): The statistical analyst rejects the null hypothesis.
Answer: OB. If the null hypothesis is not true a Type I error would be committed.
Explanation: In this scenario, the statistical analyst rejects the null hypothesis. If the null hypothesis is not true, then this is not an error. However, there is always a chance that the null hypothesis is true and that there is no relationship between the variables. If this is the case and the null hypothesis is rejected, a Type I error would be committed. A Type I error is when a true null hypothesis is rejected
.Scenario (d): Unknown to the statistical analyst, the null hypothesis is actually true, and the analyst fails to reject the null hypothesis.
Answer: OD. No error is made.Explanation:In this scenario, the null hypothesis is true but the statistical analyst does not know it. The statistical analyst fails to reject the null hypothesis. Therefore, no error is made.Scenario (e): Unknown to the statistical analyst, the null hypothesis is actually false.Answer: OB. If the null hypothesis is rejected a Type I error would be committed.Explanation:In this scenario, the null hypothesis is false, but the statistical analyst does not know it. If the null hypothesis is rejected, a Type I error would be committed. A Type I error is when a true null hypothesis is rejected.Scenario (f): Unknown to the statistical analyst, the null hypothesis is actually false, and the analyst rejects the null hypothesis.Answer: OC. A Type I error has been committed.
Explanation:In this scenario, the null hypothesis is false, but the statistical analyst does not know it. The analyst rejects the null hypothesis. Since the null hypothesis is false, this is not an error. However, there is always a chance that the null hypothesis is true and that there is no relationship between the variables.
If this is the case and the null hypothesis is rejected, a Type I error would be committed. A Type I error is when a true null hypothesis is rejected.
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Type I error: Rejecting the null hypothesis when it is actually true.
Type II error: Failing to reject the null hypothesis when it is actually false.
No error: The statistical analyst's conclusion aligns with the truth of the null hypothesis.
a. Unknown to the statistical analyst, the null hypothesis is actually true.
OA. If the null hypothesis is rejected, a Type I error would be committed.
OB. If the null hypothesis is not rejected, no error is made.
b. The statistical analyst fails to reject the null hypothesis.
OA. If the null hypothesis is true, no error is made.
OB. If the null hypothesis is true, a Type II error would be committed.
c. The statistical analyst rejects the null hypothesis.
OA. If the null hypothesis is true, a Type II error would be committed.
OB. If the null hypothesis is not true, no error is made.
d. Unknown to the statistical analyst, the null hypothesis is actually true, and the analyst fails to reject the null hypothesis.
OA. A Type II error has been committed.
OB. Both a Type I error and a Type II error have been committed.
e. Unknown to the statistical analyst, the null hypothesis is actually false.
OA. If the null hypothesis is not rejected, no error is made.
OB. If the null hypothesis is rejected, a Type I error would be committed.
f. Unknown to the statistical analyst, the null hypothesis is actually false, and the analyst rejects the null hypothesis.
OA. Both a Type I error and a Type II error have been committed.
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If the system of inequalities y≥2x+1 and y>21x−1 is graphed in the xy-plane above, which quadrant contains no solutions to the system?
The correct answer is the quadrant that contains no solutions to the system of inequalities is Quadrant IV.
To determine which quadrant contains no solutions to the system of inequalities, let's analyze each quadrant in the xy-plane.
Quadrant I: In this given quadrant, both x and y values are positive. Let's substitute values to check the inequalities:
For x = 1 and y = 1, we have:
y ≥ 2x + 1 ⟹ 1 ≥ 2(1) + 1 ⟹ 1 ≥ 3 (False)
y > 1/2x - 1 ⟹ 1 > 1/2(1) - 1 ⟹ 1 > 1/2 - 1 ⟹ 1 > -1/2 (True)
Since one inequality is false and the other is true, Quadrant I contains no solutions to the system.
Quadrant II: In this quadrant, x values are negative, and y values are positive. Substituting values:
For x = -1 and y = 1, we have:
y ≥ 2x + 1 ⟹ 1 ≥ 2(-1) + 1 ⟹ 1 ≥ -1 (True)
y > 1/2x - 1 ⟹ 1 > 1/2(-1) - 1 ⟹ 1 > -1/2 - 1 ⟹ 1 > -3/2 (True)
Both inequalities are true, so Quadrant II contains solutions to the system.
Quadrant III: In this quadrant, both x and y values are negative. Substituting values:
For x = -1 and y = -1, we have:
y ≥ 2x + 1 ⟹ -1 ≥ 2(-1) + 1 ⟹ -1 ≥ -1 (True)
y > 1/2x - 1 ⟹ -1 > 1/2(-1) - 1 ⟹ -1 > -1/2 - 1 ⟹ -1 > -3/2 (True)
Both inequalities are true, so Quadrant III contains solutions to the system.
Quadrant IV: In this quadrant, x values are positive, and y values are negative. Substituting values:
For x = 1 and y = -1, we have:
y ≥ 2x + 1 ⟹ -1 ≥ 2(1) + 1 ⟹ -1 ≥ 3 (False)
y > 1/2x - 1 ⟹ -1 > 1/2(1) - 1 ⟹ -1 > 1/2 - 1 ⟹ -1 > -1/2 (True)
Since one inequality is false and the other is true, Quadrant IV contains no solutions to the system.
Therefore, the quadrant that contains no solutions to the system of inequalities is Quadrant IV.
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The correct question is-
If the system of inequalities y≥2x+1 and y> 1/2x−1 is graphed in the xy-plane above, which quadrant contains no solutions to the system?
Is the solution set of a nonhomogeneous linear system Ax = b, of m equations in n unknowns, with b = 0, a subspace of R"? Answer yes or no and justify your answer.
No, the solution set of a nonhomogeneous linear system Ax = b, where b = 0, is not a subspace of R^
Having the zero vector, being closed under vector addition, and being closed under scalar multiplication are all requirements for a set to qualify as a subspace. The homogeneous system Axe = 0 in this instance has the simple solution x = 0 at all times when b = 0. It represents the homogeneous system in this situation.
In fact, Rn has a subspace represented by the set containing just the zero vector. The basic solution is only one of several solutions for the nonhomogeneous system Axe = b, however, when b 0. Due to their failure to meet the closure characteristics necessary for a subspace, these extra solutions do not constitute a subspace in a linear system.
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Avery leans a 24-foot ladder against a wall so that it forms an angle of 80
with the ground. How high up the wall does the ladder reach? Round your answer to the nearest tenth of a foot if necessary.
The height of the wall where the ladder reaches will be 23.6 feet.
What is a right-angle triangle?It's a form of a triangle with one 90-degree angle that follows Pythagoras' theorem and can be solved using the trigonometry function.
Trigonometric functions examine the interaction between the dimensions and angles of a triangular form.
Avery leans a 24-foot ladder against a wall so that it forms an angle of 80° with the ground.
The height of the wall where the ladder reaches is given as,
[tex]\text{sin 80}^\circ \sf =\dfrac{h}{24}[/tex]
[tex]\sf h = 24 \times \text{sin 80}^\circ[/tex]
[tex]\sf = 24 \times \text{0.9848}[/tex]
[tex]\sf h = 23.63\thickapprox\bold{23.6 \ feet}[/tex]
The height of the wall where the ladder reaches will be 23.6 feet.
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find the average rate of change of f(x) = on [4, 9]. round your answer to the nearest hundredth. question 17 options: 0.14 0.71 –0.36 –0.14
The average rate of change of f(x) = x over the interval [4, 9] is 1.
To find the average rate of change of a function f(x) over an interval [a, b], you can use the formula:
Average Rate of Change = (f(b) - f(a)) / (b - a)
In this case, we have the function f(x) = x and the interval [4, 9]. Let's substitute the values into the formula:
Average Rate of Change = (f(9) - f(4)) / (9 - 4)
Calculating the values:
f(9) = 9
f(4) = 4
Average Rate of Change = (9 - 4) / (9 - 4)
= 5 / 5
= 1
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The principal of an elementary school wanted to see if there are differences in the abilities of first, second, and third graders to render a three-dimensional image into a two-dimensional drawing (as judged by trained coders). All students were attending the same school, and data were collected during the same semester. Independent samples t test O ANOVA Paired samples t test Correlation
The appropriate statistical test that the principal of an elementary school should use to compare the abilities of first, second, and third graders to render a three-dimensional image into a two-dimensional drawing (as judged by trained coders) is Analysis of Variance (ANOVA).
The statistical test that the principal of an elementary school should use to compare the abilities of first, second, and third graders to render a three-dimensional image into a two-dimensional drawing (as judged by trained coders) is Analysis of Variance (ANOVA).
What is Analysis of Variance (ANOVA)?
Analysis of Variance (ANOVA) is a statistical technique used to determine if there are any significant differences between two or more means. It accomplishes this by evaluating the variance of each group of data and comparing them to the variance of the overall data set.
In this case, the principal wants to compare the abilities of first, second, and third graders. Therefore, an ANOVA would be the most appropriate statistical test to determine if there are any significant differences in the abilities of the three groups of students.
In summary, the appropriate statistical test that the principal of an elementary school should use to compare the abilities of first, second, and third graders to render a three-dimensional image into a two-dimensional drawing (as judged by trained coders) is Analysis of Variance (ANOVA).
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In this problem you will derive the efficiency of a CSMA/CD like multiple access protocol. In this protocol the time is slotted and all nodes are synchronised to the slot times. The length of a slot in this case is much less the than the actual time to transmit one frame. • Let the slot length be S seconds • Let the frame length be L bits • Let the transmission rate be R bps • Let the number of nodes be N and assume that each node has an infinite amount of packets to send Assume the propagation delay is much less than S, so that all nodes can detect a collision before the end of the slot. The protocol operates as below: o Ifa node has not acquired the channel, all nodes contend with probability p. If exactly 1 user transmit in that slot, then that user keep possession of the channel for the next k slots, transmitting an entire frame. o If a node has the possession of the channel, other nodes refrain from transmitting until that node finish transmitting the frame. Once the transmission completes all nodes again compete for the channel.
The efficiency of a CSMA/CD-like multiple access protocol can be derived based on the given parameters. The protocol operates by nodes contending for the channel and transmitting frames in slots.
To derive the efficiency of the CSMA/CD-like multiple access protocol, we need to consider the contention and transmission behavior of the nodes. In a slot, if a node has not acquired the channel, all nodes contend with a probability p. If exactly one node transmits in that slot, it keeps possession of the channel for the next k slots to transmit an entire frame. Other nodes refrain from transmitting until the ongoing transmission completes.
The efficiency of the protocol is determined by the successful transmissions over the total available time. Considering the slot length (S), frame length (L), transmission rate (R), and the number of nodes (N), we can calculate the probability of successful transmission and the expected time for each transmission.
Efficiency can be defined as the ratio of the time spent in successful transmissions to the total available time. It depends on parameters such as the contention probability, number of nodes, frame length, and transmission rate. The efficiency formula will involve calculating the probability of a successful transmission, taking into account the contention behavior and the possession of the channel by a node.
By analyzing the protocol's operation and considering these factors, the efficiency of the CSMA/CD-like multiple access protocol can be derived and expressed as a mathematical formula or percentage.
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use the rule for sample means to explain why it is desirable to take as large a sample as possible when trying to estimate a population value.
By taking a larger sample, you increase your chances of obtaining an accurate estimate of the population value you are interested in.
The rule for sample means, also known as the law of large numbers, states that as the sample size increases, the sample mean will more closely approximate the population mean. This is a fundamental principle in statistics that underscores the importance of taking as large a sample as possible when trying to estimate a population value. Let's delve into the reasons behind this desirability.
Increased Precision: By taking a larger sample, you increase the amount of information you have about the population. This additional information leads to a more precise estimate of the population value. A larger sample reduces the impact of random fluctuations or sampling error, resulting in a more accurate estimation of the population mean.
Decreased Sampling Variability: When you draw a small sample from a population, there is a greater likelihood that the sample might not accurately represent the population characteristics. Small samples are more susceptible to random variations, which can lead to larger discrepancies between the sample mean and the population mean. In contrast, larger samples tend to smooth out these variations and provide a more stable estimate.
Reduced Bias: Bias refers to the systematic deviation between the sample statistic (such as the sample mean) and the population parameter (such as the population mean). Taking a larger sample reduces the potential for bias by encompassing a more diverse range of observations from the population. This inclusivity helps to mitigate the influence of any specific subset of the population that may have unique characteristics.
Increased Confidence Interval Accuracy: When estimating a population value, it is common to construct a confidence interval to quantify the uncertainty associated with the estimate. A larger sample size leads to narrower confidence intervals, indicating a more precise estimate. This narrower interval reflects increased confidence in the estimated range that captures the population value, providing a more useful and reliable estimation.
Enhanced Generalizability: By collecting a larger sample, you increase the representativeness of your sample relative to the population. A larger sample size allows for a better reflection of the population's diversity, ensuring that various subgroups and characteristics are adequately represented. This improves the generalizability of your findings, enabling you to draw more accurate conclusions about the entire population.
In summary, the rule for sample means emphasizes the desirability of larger sample sizes when estimating population values. Larger samples yield more precise estimates, reduce sampling variability and bias, enhance confidence interval accuracy, and increase the generalizability of the findings.
Therefore, By taking a larger sample, you increase your chances of obtaining an accurate estimate of the population value you are interested in.
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ABC Co Ltd is a base rate entity, which has less than $2 million aggregated turnover. ABC Co Ltd derives income for the current income year (all from Australian sources) comprising net income from trading of $90,000, franked distribution from public companies amounting to $21,000 (carrying an imputation credit of $9,000), unfranked distributions from resident private companies amounting to $21,000 and rental income of $5,000. Calculate the net tax payable by ABC Co Ltd for the year ended 30 June
The net tax payable by ABC Co Ltd for the year ended 30 June would be $40,150.
Net tax payable by ABC Co Ltd for the year ended 30 June
Net income from trading = $90,000
Franked distribution from public companies = $21,000
Unfranked distributions from resident private companies = $21,000
Rental income = $5,000
Aggregated turnover = Less than $2 million
The base rate entity tax rate is 27.5%.
Franked distribution carries an imputation credit of $9,000.
Therefore, the franked distribution's assessable income would be $21,000 + $9,000 = $30,000.
Assessable income = $90,000 + $30,000 + $21,000 + $5,000 = $146,000.
The company's tax liability would be 27.5% of $146,000, which is $40,150.
Tax Payable = $146,000 × 27.5% = $40,150
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Here are the ages of `20` people at a family reunion, ordered from youngest to oldest:
`3,\ 8,\ 9,\ 10,\ 11,\ 11,\ 12,\ 18,\ 18,\ 28,`
`30,\ 35,\ 37,\ 40,\ 53,\ 54,\ 58,\ 65,\ 70,\ 72`
The value of quartile 2 (Q2) is `29`. Explain what the number `29` tells us about the people at the family reunion. Please help it due tomorrow!!!!
The number `29` represents the median or the second quartile (Q2) age of the family reunion members.
The given data is of `20` people at a family reunion, ordered from youngest to oldest and the value of quartile 2 (Q2) is `29`.
The number `29` tells us about the people at the family reunion that:
Half of the family reunion members had an age of less than or equal to `29` years and half of the family reunion members had an age of more than or equal to `29` years.
In other words, the median age of the family reunion members is `29` years and out of the given ages of `20` people at a family reunion, half of the people are younger than `29` and half are older than `29`.
Therefore, the number `29` represents the median or the second quartile (Q2) age of the family reunion members.
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Let A be a 3 x 3 matrix and B be a 5 x 3 matrix. Which of the following are defined? Circle all that apply. a) 2A b) A + B c) AB d) BA e) det (A) f) det(B) 8) tr(A) h) tr(B) i) A? j) BT
Answer:
The operations that are defined in the given options are A + B, AB, and tr(A). A + B: This operation is defined because adding two matrices is valid when they have the same dimensions.
a) 2A: This operation is defined because multiplying a matrix by a scalar is a valid operation. The resulting matrix will have the same dimensions as the original matrix A.
b) A + B: This operation is defined because adding two matrices is valid when they have the same dimensions. In this case, matrix A is a 3 x 3 matrix and matrix B is a 5 x 3 matrix, so the operation is defined. The resulting matrix will have the same dimensions as the matrices being added.
c) AB: This operation is not defined because the number of columns in matrix A (3) is not equal to the number of rows in matrix B (5). For matrix multiplication to be defined, the number of columns in the first matrix must be equal to the number of rows in the second matrix.
d) BA: This operation is not defined because, similar to AB, the number of columns in matrix B (3) is not equal to the number of rows in matrix A (3).
e) det(A): This operation is defined because the determinant of a square matrix is a valid operation. Since matrix A is a 3 x 3 matrix, the operation is defined.
f) det(B): This operation is not defined because matrix B is not a square matrix. The determinant is only defined for square matrices.
g) tr(A): This operation is defined because the trace of a square matrix is a valid operation. Since matrix A is a 3 x 3 matrix, the operation is defined.
h) tr(B): This operation is not defined because matrix B is not a square matrix. The trace is only defined for square matrices.
i) A: This option is not clear. If it is asking about the existence of matrix A, then it is already given in the question that A is a 3 x 3 matrix.
j) BT: This operation is defined because taking the transpose of a matrix is a valid operation. The resulting matrix will have the number of rows equal to the number of columns of the original matrix, and the number of columns equal to the number of rows of the original matrix. In this case, the transpose of matrix B will be a 3 x 5 matrix.
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Let Y represent the profit (or loss) for a certain company X years after 1975. Based on the data shown below, a statistician calculates a linear model Y = 1.09X + 18.77. X у 1 19.19 2. 22.28 21.47 4 22.46 5 23.65 6 26.34 7 25.43 29.02 9 28.11 10 31.9 11 28.99 12 31.48 7 00 Use the model to estimate the profit in 1977 y =
Using the linear model Y = 1.09X + 18.77, we can estimate the profit for the year 1977. The estimated profit for 1977 is $20.95.
To estimate the profit for the year 1977, we substitute X = 2 (representing 1977 - 1975 = 2) into the linear model Y = 1.09X + 18.77.
Y = 1.09 * 2 + 18.77
Y ≈ 20.95
Therefore, the estimated profit for the year 1977 is approximately $20.95.
To estimate the profit in 1977 using the linear model Y = 1.09X + 18.77, we need to determine the value of X for the year 1977. In this case, X represents the number of years after 1975. So, to find the value of X for 1977, we subtract 1975 from the year 1977:
X = 1977 - 1975 = 2
Now, we can substitute this value into the equation to estimate the profit in 1977:
Y = 1.09 * X + 18.77
Y = 1.09 * 2 + 18.77
Y = 2.18 + 18.77
Y ≈ 20.95
Therefore, the estimated profit for the company in 1977, based on the linear model, is approximately 20.95.
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Determine whether or not the following statement is true: If A and B are 2 x 2 matrices, then (A + B)2 = A + 2AB + B2. If the statement is true, prove it. If it is false, provide an example showing why it is false. Be sure to explain all of your reasoning.
The statement “If A and B are 2 x 2 matrices, then (A + B)2 = A + 2AB + B2” is False.
The identity for matrices (A + B)^2 ≠ A^2 + B^2 + 2AB
If A and B are any two 2 × 2 matrices such that A = [aij] and B = [bij], then(A + B)^2 = (A + B)(A + B)= A(A + B) + B(A + B) [By distributive property of matrix multiplication] = A^2 + AB + BA + B^2(Assuming AB and BA are both defined)
Note: It is not the case that AB = BA for every pair of matrices A and B
Therefore (A + B)^2 ≠ A^2 + B^2 + 2AB
Example to show that (A + B)^2 ≠ A^2 + B^2 + 2ABLet A = [ 1 2 3 4] and B = [1 0 0 1]Then, (A + B)^2 = [2 2 6 8] ≠ [2 4 6 8] + [1 0 0 1] + 2 [ 1 0 0 1] [ 1 2 3 4]
Hence, it is clear that the statement “If A and B are 2 x 2 matrices, then (A + B)2 = A + 2AB + B2” is False.
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In 1998, an industry spokesperson said that over 82% of Americans owned a cellular phone. Test this claim using a significance level of α = 0.05, given that in a random sample of 1036 Americans, 881 owned a cell phone.
a. Write the null and alternative hypotheses. H0: _____________ Ha : ______________
b. To determine if the conditions are met to perform a z-test, complete the following.
Null hypothesis is that the proportion of Americans who own a cellular phone is less than or equal to 0.82 (H0: p ≤ 0.82), and the alternative hypothesis is that the proportion of Americans who own a cellular phone is greater than 0.82 (Ha: p > 0.82).b. We know that the sample size is 1036 and the sample proportion is 881/1036 = 0.85. Therefore, the conditions to perform a z-test are met:np = 1036 × 0.82 ≈ 848.5 and n(1 - p) = 1036 × 0.18 ≈ 186.5Both are greater than 10. So, we can proceed with the z-test.
The null and alternative hypotheses. H0: p ≤ 0.82 Ha: p > 0.82.The conditions are met to perform a z-test, we can use the following criteria:np≥10 and n(1−p)≥10where n is the sample size and p is the hypothesized proportion of successes in the population.Assuming a significance level of 0.05, we are testing the claim that the percentage of Americans who own a cellular phone is equal to 82% or higher. Therefore, our null hypothesis is that the proportion of Americans who own a cellular phone is less than or equal to 0.82 (H0: p ≤ 0.82), and the alternative hypothesis is that the proportion of Americans who own a cellular phone is greater than 0.82 (Ha: p > 0.82).b. We know that the sample size is 1036 and the sample proportion is 881/1036 = 0.85. Therefore, the conditions to perform a z-test are met:np = 1036 × 0.82 ≈ 848.5 and n(1 - p) = 1036 × 0.18 ≈ 186.5Both are greater than 10. So, we can proceed with the z-test.
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is the system of equations consistent and independent, consistent and dependent, or inconsistent? y=−3x 12y=−6x 2
The system of equations y = -3x and 12y = -6x + 2 can be classified as consistent and dependent.
In a consistent system, there is at least one solution that satisfies all the equations. In this case, both equations represent the same line since they have the same slope of -3 and the second equation can be obtained by multiplying the first equation by 12. Therefore, any point that satisfies one equation will also satisfy the other equation. The system has infinitely many solutions, and the equations are dependent on each other.
To determine the consistency and dependence of a system of equations, one can analyze the slopes and the relationship between the equations. If the slopes are different and the equations intersect at a single point, the system is consistent and independent. If the slopes are the same and the equations represent the same line, the system is consistent and dependent. If the slopes are different and the equations are parallel, the system is inconsistent and has no solution.
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Using only patients 1,2, and 3 in D from Question 6.8 from page 73 Rosner Study Guide (Chapter 06), we sample two paitnets with replacement and create a sampling distribution (just like slide 9 in lecture 5; call this new sample D2). Select all correct statements::
Group of answer choices
Central Limit Theorem tells us that the sampling distribution will be binomial distribution
Mean of D2 is 132/9
Sandard deviation of D2 is 20.869
Sampling distribution of D2 can be estimated as N(132/9, 435.5)
Sampling distribution of D2 can be estimated as N(44/3, 1161.33)
The correct options are (B) and (E).
Number of ways of getting two patients out of three with replacement = $3^2$ = 9.D = {90, 150, 120}.
We have to choose 2 patients with replacement. All possible values are:{(90,90), (90,150), (90,120),(150,90), (150,150), (150,120),(120,90), (120,150), (120,120)}
The sum of two patients for all possible ways is (90+90), (90+150), (90+120), (150+90), (150+150), (150+120), (120+90), (120+150), (120+120) = 180, 240, 210, 240, 300, 270, 210, 270, 240.
mean of D2 = (180+240+210+240+300+270+210+270+240) / 9= 1960 / 9 = 217.78
So, the statement "Mean of D2 is 132/9" is FALSE.
Sandard deviation of D2 is 20.869Let's calculate the standard deviation of D2.Standard deviation of D = $\sqrt{\frac{1}{N-1} \sum_{i=1}^{N}(D_i - \overline{D})^2}$= $\sqrt{\frac{1}{3-1} \sum_{i=1}^{3}(D_i - \overline{D})^2}$= $\sqrt{\frac{1}{2} [(90 - 120)^2 + (150 - 120)^2 + (120 - 120)^2]}$= $\sqrt{\frac{1}{2} (30^2 + 30^2 + 0)}$= $\sqrt{450}$= 21.21
Sandard deviation of D2 is 21.21.So, the statement "Sandard deviation of D2 is 20.869" is FALSE.
The sampling distribution of D2 can be estimated as N(132/9, 435.5). FALSE, as the standard deviation is 21.21, not 435.5.The sampling distribution of D2 can be estimated as N(44/3, 1161.33).
TRUE, because the mean of D2 is 217.78 and the standard deviation is 21.21. Therefore, the sampling distribution of D2 can be estimated as N(217.78, 21.21).Now, let's see the correct statements:The sampling distribution of D2 can be estimated as N(44/3, 1161.33).Sampling distribution of D2 can be estimated as N(217.78, 21.21).
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The given information is that using only patients 1,2, and 3 we sample two patients with replacement and create a sampling distribution (just like slide 9 in lecture 5; call this new sample D2
The correct statements are:
The mean of D2 is 132/9 = 44/3
Standard deviation of D2 is 20.869
Sampling distribution of D2 can be estimated as N(44/3, 20.869)
Explanation: From patients 1,2 and 3, there are 3 different possible samples that we could obtain by choosing 2 patients at random with replacement. The 3 possible samples are:
D1 = {1, 1}, {1, 2}, {1, 3}, {2, 1}, {2, 2}, {2, 3}, {3, 1}, {3, 2}, {3, 3}
D2 = {1, 1}, {1, 2}, {1, 3}, {2, 1}, {2, 2}, {2, 3}, {3, 1}, {3, 2}, {3, 3}
D3 = {1, 1}, {1, 2}, {1, 3}, {2, 1}, {2, 2}, {2, 3}, {3, 1}, {3, 2}, {3, 3}
The question is asking about D2 which is the same as D1 since sampling with replacement creates a new set of sample which is the same as the first. In D1, the sum of all the measurements is 132. Since there are 9 different samples in D1, the mean of the sum of the measurements in a sample (i.e. the sample mean) is 132/9 = 44/3. The sampling distribution of D2 is a discrete distribution because there are a finite number of samples possible, but as n (the sample size) becomes large, the sampling distribution approaches a normal distribution with mean µ = 44/3 and standard deviation, σ = √[(435.5 - (132/9)²)/9] = 20.869. Therefore, correct statements are:
The mean of D2 is 132/9 = 44/3
Standard deviation of D2 is 20.869
Sampling distribution of D2 can be estimated as N(44/3, 20.869)
Therefore, options (B), (C) and (E) are correct.
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Solve by using the method of Laplace transforms:
y" – 6y' + 5y = 3e^(-x); y(0) = 2; y'(0) = 3
Solve by using the method of Laplace transforms:
y" + 9y = 2x + 4; y(0) = 0; y'(0) = 1
The solutions to the given differential equations are:
[tex]y(t) = (3e^{(-t)} + 10 - 2(e^{(t-3)}))(e^t - e^{(5t)})[/tex]
y(t) = 2t + 4 - 2cos(3t) + sin(3t)
How to use method of Laplace transforms?To solve these differential equations using Laplace transforms, follow the standard procedure of taking the Laplace transform of the given equation, solving for the Laplace transform of the unknown function, and then taking the inverse Laplace transform to obtain the solution.
Solve the equation: y" – 6y' + 5y = 3e⁻ˣ; y(0) = 2; y'(0) = 3
Step 1: Taking the Laplace transform of both sides of the equation:
s²Y(s) - sy(0) - y'(0) - 6sY(s) + 6y(0) + 5Y(s) = 3/(s + 1)
Step 2: Simplifying the equation using the initial conditions:
(s² - 6s + 5)Y(s) - 2s - 3 - 12 + 10 = 3/(s + 1)
Step 3: Rearranging the equation to solve for Y(s):
Y(s) = (3/(s + 1) + 15 - 2s - 3)/(s² - 6s + 5)
Step 4: Partial fraction decomposition:
Y(s) = (3/(s + 1) + 10 - 2(s - 3))/(s - 1)(s - 5)
Step 5: Taking the inverse Laplace transform to find y(t):
[tex]y(t) = (3e^{(-t)} + 10 - 2(e^{(t-3)}))(e^t - e^{(5t)})[/tex]
Solve the equation: y" + 9y = 2x + 4; y(0) = 0; y'(0) = 1
Step 1: Taking the Laplace transform of both sides of the equation:
s²Y(s) - sy(0) - y'(0) + 9Y(s) = 2/s² + 4/s
Step 2: Simplifying the equation using the initial conditions:
s²Y(s) - 1 + 9Y(s) = 2/s² + 4/s
Step 3: Rearranging the equation to solve for Y(s):
Y(s) = (2/s² + 4/s + 1)/(s² + 9)
Step 4: Partial fraction decomposition:
Y(s) = (2/s² + 4/s + 1)/(s² + 9)
= 2/s² + 4/s - 2(s/(s² + 9)) + 1/(s² + 9)
Step 5: Taking the inverse Laplace transform to find y(t):
y(t) = 2t + 4 - 2cos(3t) + sin(3t)
Therefore, the solutions to the given differential equations are:
[tex]y(t) = (3e^{(-t)} + 10 - 2(e^{(t-3)}))(e^t - e^{(5t)})[/tex]
y(t) = 2t + 4 - 2cos(3t) + sin(3t)
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Write the prime factorization of each number as a product of powers. (a) 12^9. 36^15. 169^3 (b) 16^13. 14^7.81^14
(a) Prime factorization of each number as a product of powers: 12^9:
12 can be factored as 2^2 * 3^1. Therefore, (2^2 * 3^1)^9 = 2^(29) * 3^(19) = 2^18 * 3^9.
36^15:36 can be factored as 2^2 * 3^2. Therefore, (2^2 * 3^2)^15 = 2^(215)* 3^(215) = 2^30 * 3^30. 169^3: 169 is a prime number, so its prime factorization is simply 13^2. Therefore, 169^3 = (13^2)^3 = 13^(2*3) = 13^6 (b) Prime factorization of each number as a product of powers: 16^13: 16 can be factored as 2^4. Therefore, 16^13 = (2^4)^13 = 2^(4*13) = 2^52.
14^7: 14 can be factored as 2^1 * 7^1. Therefore, (2^1 * 7^1)^7 = 2^(17) * 7^(17) = 2^7 * 7^7. 81^14: 81 can be factored as 3^4. Therefore, 81^14 = (3^4)^14 = 3^(4*14) = 3^56. Note: In each case, the prime factorization is obtained by breaking down the given number into its prime factors and expressing it as a product of those factors raised to their respective powers.
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In the attached excel spreadsheet file, data is provided that provides names of fictitious students and the score they achieved for specific courses. The question is to review the data, do the descriptive statistics for both the row and column data, conduct an ANOVA (2 factors non-repetitious analysis), include the eight-step hypothesis test, and write a short two-page APA formatted paper disclosing:
1. Introduction
2. what you did the statistical analysis, the decision
3. the conclusion
4.recommendations.
ANOVA is a statistical technique that compares the means of two or more groups to see whether there is a difference between them.
It is very hard to provide an answer to your question as you have mentioned an attachment but no file was attached to the question. However, let me provide you with general guidance regarding conducting statistical analysis and writing an APA format paper.What is a statistical analysis?Statistical analysis is the method of collecting, cleaning, analyzing, and interpreting data to gain knowledge and identify patterns or relationships between variables.What is ANOVA?ANOVA is a statistical technique that compares the means of two or more groups to see whether there is a difference between them. The primary purpose of ANOVA is to test for a difference in group means.What is the eight-step hypothesis test?Here are the eight steps of hypothesis testing:1. State the null hypothesis (H0) and the alternate hypothesis (Ha).2. Decide the level of significance (α)3. Determine the test statistic4. Calculate the p-value5. Make a decision to reject or fail to reject the null hypothesis6. Interpret the result of the test7. State the conclusion of the test8. State the implications or applications of the decision.How to write a paper in APA format?Here is the basic format of an APA paper:1. Title page: It includes the paper's title, the author's name, and the institution's name.2. Abstract: This is a brief summary of the paper that follows the title page.3. Introduction: This section provides background information and states the research problem or question.4. Methodology: This section describes the research design and methodology used in the study.5. Results: This section presents the findings of the study.6. Discussion: This section interprets and explains the results and draws conclusions.7. References: This section lists the sources cited in the paper.
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a)
Find the point of intersection for the two lines
r1 = 3i +2j+ 4k + lambda (i+j+k)
r2 = (2i+ 3j+k + lambda (21+j+k)
b)Find the size of the angle between the two lines
The point of intersection for the two lines are P = 3i + 2j + 4k - 1/20(i + j + k). The size of the angle between the two lines is 52.29 degrees.
a) The point of intersection for the two lines can be found by setting their position vectors equal to each other and solving for lambda. The point of intersection (P) is given by:
P = 3i + 2j + 4k + lambda(i + j + k)
we can equate the corresponding components of the two position vectors:
3 + lambda = 2 + 21lambda
2 + lambda = 3 + lambda
4 + lambda = 1 + lambda
Simplifying the equations, we get:
lambda = -1/20
Plugging this value of lambda back into the equation for P, we find the point of intersection:
P = 3i + 2j + 4k - 1/20(i + j + k)
b) The angle between the two lines, we can use the dot product. The dot product of two vectors is given by the equation:
dot product = ||a|| ||b|| cos(theta)
where ||a|| and ||b|| are the magnitudes of the vectors, and theta is the angle between them.
The direction vectors for the lines:
Direction vector for line 1 (d1) = i + j + k
Direction vector for line 2 (d2) = 2i + 3j + k
Calculating the magnitudes of the direction vectors:
||d1|| = sqrt(1^2 + 1^2 + 1^2) = sqrt(3)
||d2|| = sqrt(2^2 + 3^2 + 1^2) = sqrt(14)
Now, we can calculate the dot product of the direction vectors:
d1 · d2 = (1)(2) + (1)(3) + (1)(1) = 2 + 3 + 1 = 6
Using the dot product formula, we can find the angle:
6 = sqrt(3) sqrt(14) cos(theta)
cos(theta) = 6 / (sqrt(3) sqrt(14))
theta = arccos(6 / (sqrt(3) sqrt(14)))
theta= 52.29 degrees
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Which expressions are in simplest form? Check all that apply. x^(-3)+y^(3) (1)/(x^(4)) (w^(7))/(x^(2)) a^(-9) (1)/(a^(2))+b^(2) (1)/(b^(5))
The expressions that are in simplest form are: x^(-3) + y^3, (1)/(x^4), (w^7)/(x^2), and a^(-9). The expressions (1)/(a^2) + b^2 and (1)/(b^5) are not in simplest form.
To determine if an expression is in simplest form, we need to check if there are any simplifications or reductions that can be made.
The expression x^(-3) + y^3 is in simplest form because there are no further simplifications possible.
The expression (1)/(x^4) is also in simplest form as it is already in the form of a single fraction with no common factors in the numerator and denominator.
The expression (w^7)/(x^2) is in simplest form because there are no common factors that can be canceled out.
The expression a^(-9) is in simplest form as it is already written with a negative exponent, indicating the reciprocal.
On the other hand, the expression (1)/(a^2) + b^2 is not in simplest form because it can be combined into a single fraction by finding a common denominator.
Similarly, the expression (1)/(b^5) is not in simplest form as it can be simplified by writing it with a negative exponent.
Therefore, the expressions x^(-3) + y^3, (1)/(x^4), (w^7)/(x^2), and a^(-9) are in simplest form, while (1)/(a^2) + b^2 and (1)/(b^5) can be further simplified.
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Suppose that 35% of all factory workers ride a bus to work each day. Suppose further that you take a random sample of 150 workers. 1. Is 35% number a parameter or a statistic? Explain briefly. 2. Are you guaranteed that 35% of your sample ride a bus to work each day? Explain briefly. 3. Does the central limit theorem of s sampling distribution of sample proportions apply? Refer to the technical conditions in the central limit theorem. 4. Describe the sampling distribution of the sample proportion of these 150 workers who ride a bus to work each day. Determine the shape, center, and standard deviation of this distribution
1. The 35% number is a parameter.
2. No, you are not guaranteed that 35% of your sample rides a bus to work each day.
3. Yes, the central limit theorem applies given the conditions are met.
4. The sampling distribution of the sample proportion follows an approximately normal distribution with a mean of 35% and a standard deviation determined by the formula: sqrt((p(1-p))/n), where p is the population proportion and n is the sample size.
1. The 35% number is a parameter. A parameter is a characteristic or measure of a population, and in this case, it represents the proportion of all factory workers who ride a bus to work each day. Parameters are typically estimated using sample statistics.
2. No, you are not guaranteed that 35% of your sample will ride a bus to work each day. While 35% is the proportion in the population, the proportion in any particular sample may vary due to random sampling. The sample proportion may differ from the population proportion due to sampling variability.
3. Yes, the central limit theorem (CLT) can be applied to the sampling distribution of sample proportions under certain conditions. The conditions for the CLT include having a random sample, independent observations, and a sufficiently large sample size. If these conditions are met, the sampling distribution of the sample proportions will approach a normal distribution.
4. The sampling distribution of the sample proportion of these 150 workers who ride a bus to work each day will be approximately normal. The shape of the distribution will be bell-shaped. The center of the distribution (the mean) will be equal to the population proportion, which is 35%. The standard deviation of the distribution, also known as the standard error, can be calculated using the formula:
Standard Error = sqrt[(p * (1 - p)) / n]
Where p is the population proportion (0.35) and n is the sample size (150).
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The sales of a grocery store had an average of 20k per day. The store has hired a new general manager. To determine if the effectiveness of the performance of the general manager is different, a sample of 25 days of sales was selected. It was found that the average was $24.6k per day with standard deviation 12k. The value of the test statistic is 23 -1.92 2.3 1.92
The test statistic value mentioned, 1.92, is relevant for determining whether the effectiveness of the new general manager in improving sales is significantly different from the previous average. The correct answer is option 4.
To determine if the effectiveness of the performance of the general manager is different from the previous average of $20k per day, we can conduct a hypothesis test using the t-test.
The null hypothesis (H₀) is that the average sales under the new general manager are the same as before, μ = $20k per day.
The alternative hypothesis (H₁) is that the average sales under the new general manager are different, μ ≠ $20k per day.
We can calculate the test statistic using the formula:
t = (x - μ) / (s / √n)
Where:
x is the sample mean (average daily sales) = $24.6k
μ is the population mean (previous average daily sales) = $20k
s is the standard deviation of the sample = $12k
n is the sample size = 25
Plugging in the values:
t = ($24.6k - $20k) / ($12k / √25)
t = ($4.6k) / ($12k / 5)
t = $4.6k * (5 / $12k)
t = $4.6k * 5 / $12k
t ≈ 1.9167
Therefore, the value of the test statistic is approximately 1.92. So option 4 is correct answer.
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find the title of each course that has been taken by student b00000003 but not by student b00000004.
To find the titles of courses taken by student b00000003 but not by student b00000004, we compare the course records of both students.
By identifying the courses taken by b00000003 and excluding the courses taken by b00000004, we can determine the titles of the courses in question. To accomplish this task, we need access to the course records of both students. By examining the courses taken by student b00000003, we can compile a list of the titles of those courses.
Similarly, we examine the courses taken by student b00000004 and create a separate list of the titles of those courses. To find the courses taken by b00000003 but not by b00000004, we compare the two lists and exclude any courses that appear in both lists. The remaining courses are the ones taken by b00000003 but not by b00000004. From this filtered list, we can identify the titles of the courses.
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A highly stressed-out resident of New Jersey commutes each business day to midtown Manhattan. The commuter uses NJ transit. The commuter's 6:15 am train to NYC is cancelled three times during a typical month. The probability of the commuter's moring train being canceled precisely six times in a month is closet to: 50% 68% 95% O 5% QUESTION 16 You conduct an experiment tossing a fair coin. Let (X, Y) be random variables, where X is the number of heads that occurs in two tosses and Y is the number of tails that arises in two tosses. Find P (X=1, Y=1). Separately, find P(X=0, Y= 0). Note you need to find two answers here, and the answer to your first question does not influence the second question- that is, the questions are independent. 0.50 and 0.50 0.0 and 0.50 0.50 and 0.0 O.25 and .25
The probability of commuter's morning train being canceled precisely six times in a month is closest to 5%.
Given:
A commuter's 6:15 am train to NYC is cancelled three times during a typical month. The commuter uses NJ transit.To find: The probability of the commuter's morning train being canceled precisely six times in a month.
Let X be the number of train cancellations in a month.
As the train cancellations follow a Poisson distribution, the formula for the Poisson distribution is given as:
P(X = x) = (e-λ λx) / x!
where λ is the average number of train cancellations in a month and x is the number of train cancellations in a month.
Now, we need to calculate the probability of a commuter's morning train being canceled precisely six times in a month. Hence, x = 6.
Substitute the given values into the Poisson formula:
P(X = 6) = (e-3 36) / 6!≈ 0.0504 ≈ 0.05
Therefore, the probability of the commuter's morning train being canceled precisely six times in a month is closest to 5%.
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