which of the following measures can be calculated for qualitative data only?
a. mean
b. median
c. mode
d. none of these

a) Only the first statement is true. As the sample size N becomes large, the sample mean of IID random samples from a population becomes more precise and approaches the true population mean.

However, there is no direct relationship between the sample size and the distribution of the sample mean.
The second statement is only true if the population is normally distributed. If the population is not normal, the distribution of the sample mean may not be normal, and the central limit theorem may not apply. Therefore, option c) is not the correct answer. Option d) is also not correct as the first statement is true.

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a. Number who are salespersons and have some college education = 145

b. Number who are factory workers or graduates high school = 345

c. Number who are technical workers or some college = 435

d. Number who are high school graduates or attended some college = 610

e. Number who are technical workers or salespersons and have graduated high school or attended some college: 625

f. Number who are high school graduates or have attended some college: 610

g. Number who are salespersons or technical workers:  585

The given table provides the data of job placement and education level of 1030 workers. We need to calculate the number of workers who meet the listed criteria.

a. The number of workers who are salespersons and have some college education can be found from the intersection of the second row (some college) and second column (salesperson), which is 145.

b. The number of workers who are high school graduates or have attended some college can be found by adding the first row (high school graduates) and the second row (some college), which is 650.

c. The number of workers who are technical workers or salespersons and have graduated high school or attended some college can be found by adding the intersection of the first row and third column (technical workers) and the intersection of the second row and second column (salespersons), which is 315.

d. The number of workers who have attended some college can be found by adding the second row and the third row, which is 555.

e. The number of workers who are technical workers or salespersons and have graduated high school or attended some college can be found by adding the intersection of the first row and third column (technical workers) and the intersection of the second row and second column (salespersons) and the intersection of the first row and second column (high school graduates), which is 465.

f. The number of workers who are high school graduates or have attended some college can be found by adding the first row (high school graduates) and the second row (some college), which is 650.

g. The number of workers who are salespersons or technical workers can be found by adding the intersection of the second row and second column (salespersons) and the intersection of the first row and third column (technical workers), which is 275.

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To communicate information about the public in broadly understandable terms, researchers and pollsters often rely on aggregated statistical data. By compiling and analyzing large sets of information, they can identify patterns and trends that can be presented in a way that is easy for people to understand.

For example, they may use graphs, charts, or other visual aids to convey complex information in a clear and concise manner. This can be particularly important when trying to share findings with the general public or with policymakers who may not have a background in statistics or research methodology. By using aggregated statistical data, researchers and pollsters can help ensure that important information is communicated effectively and accurately.

This allows them to present complex information in a more accessible and easily digestible format for a wider audience.

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The probability of getting a z-score of -356.5625 or lower is essentially 0. Therefore, the likelihood of the diameter being within 1-0.03 inches is extremely low (close to 0%).

To answer your question, we can use the normal distribution since we have a sample mean and sample standard deviation. We can assume that the diameter measurements follow a normal distribution with a mean of 24 inches and a standard deviation of 0.064 inches.

To find the likelihood of the diameter being within 1-0.03 inches, we need to standardize the values using the formula:

z = (x - μ) / σ

where x is the value we want to find the likelihood for (in this case, 1-0.03 = 0.97 inches), μ is the mean (24 inches), and σ is the standard deviation (0.064 inches).

So, plugging in the values:

z = (0.97 - 24) / 0.064 = -356.5625

This gives us a z-score of -356.5625. We can use a standard normal distribution table or calculator to find the probability of getting a z-score of -356.5625 or lower.

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The area to the right of z = -2.71 and the area to the right of z = 2.71 under the standard normal curve are both approximately 0.0034.

To find the area under the standard normal curve to the right of z = -2.71, we need to calculate the area between z = -2.71 and z = infinity. This can be done using a standard normal distribution table or calculator, which will give us an area of approximately 0.0034.

To find the area under the standard normal curve to the right of z = 2.71, we can use the same approach but this time we need to calculate the area between z = 2.71 and z = infinity. Again, using a standard normal distribution table or calculator, we can find this area to be approximately 0.0034.

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The correct prime numbers x that make this inequality true is,

⇒ x = 7, 11, 13, 17, 19

We have to given that;

The expression is,

''5 is less than x and x is less than or equal to 19.''

Now, We can formulate;

⇒ 5 < x ≤ 19

Hence, Possible prime numbers that make this inequality true are,

⇒ x = 7, 11, 13, 17, 19

Thus, The correct prime numbers x that make this inequality true is,

⇒ x = 7, 11, 13, 17, 19

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The ordinary number form of the mentioned scientific notation form of the number is 0.00604.

Scientific notation of representation of a number refers to converting a number to its readable form. It is applicable on both small and large numbers, where value of zeroes are represented in exponential form for easy interpretation.

The exponent of -3 is interpreted as three zeroes in the denominator. The division with zero will further shorten the number by adding zeroes to left hand side of the digit after decimal. Hence, the ordinary form of the number will be 0.00604.

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The APR (annual percentage rate) for a $2,000 loan paid off in 12 equal monthly payments with a stated annual interest rate of 8% is 14.452%.

The annual percentage rate (APR) can be determined using an online finance calculator as follows:

The APR is the total cost of borrowing money, reflecting not only the interest rate but also other loan fees.

N (# of periods) = 12 months

PV (Present Value) = $2,000

PMT (Periodic Payment) = $-180

FV (Future Value) = $-0

Results:

I/Y = 14.452% if interest compounds 12 times per year (APR)

I/Y = 15.449% if interest compounds once per year (APY)

I/period = 1.204% interest per period

Sum of all periodic payments = $-2,160.00 ($180 x 12)

Total Interest = $160.00 ($2,000 x 8%)

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The Empirical Rule is a statistical principle that applies to normally distributed data. It states that for a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, 95% of the data falls within two standard deviations of the mean, and 99.7% of the data falls within three standard deviations of the mean.

In this case, the mean triglyceride level for the residents of the assisted living facility is 200, with a standard deviation of 50. If samples of 100 residents are taken, the sample mean triglyceride level will also be normally distributed, with a mean of 200 and a standard deviation of 5 (calculated as 50 divided by the square root of 100).

To find the range within which 95% of all the sample means will fall, we need to look at two standard deviations above and below the mean. Two standard deviations above the mean are 210 (calculated as 200 + 2*50), and two standard deviations below the mean are 190 (calculated as 200 - 2*50).

Therefore, we can conclude that 95% of all sample means will fall between 190 and 210. So the lower value is 190, and the upper value is 210.

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Each guy-wire is approximately 315.08 feet long.

We can use trigonometry to find the length of the guy-wires. Let's call the length of each guy-wire "x".

First, we can use the tangent function to find the height of the triangle formed by the first guy-wire and the antenna:

tan(0.33) = height/x

Rearranging, we get:

height = x * tan(0.33)

Similarly, we can use the tangent function to find the height of the triangle formed by the second guy-wire and the antenna:

tan(0.38) = height/x

Again, rearranging, we get:

height = x * tan(0.38)

Since both of these triangles share the same height, we can set the two expressions for height equal to each other:

x * tan(0.33) = x * tan(0.38)

Dividing both sides by x gives:

tan(0.33) = tan(0.38)

This equation is not true for all values of alpha and beta, but we are given that it holds for this particular case. Using this equation, we can solve for x:

x = 160 / tan(0.33)

x ≈ 315.08 feet

Therefore, each guy-wire is approximately 315.08 feet long.

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Answer:

19

Step-by-step explanation:

Total number of ways of choosing a student from the gym class = 29 (since there are 29 students in total).

Number of ways of choosing a seventh grader = 10 (since there are 10 seventh graders).

Number of ways of not choosing a seventh grader = Total number of ways of choosing a student - Number of ways of choosing a seventh grader = 29 - 10 = 19

In descriptive statistics, the interquartile range tells you the spread of the middle half of your distribution. Quartiles segment any distribution that's ordered from low to high into four equal parts. The interquartile range (IQR) contains the second and third quartiles, or the middle half of your data set.

The tabled value for a x^2 variable based on 50 degrees of freedom with an area of 0.025 to its right is x² = 69.34 (rounded to two decimal places).

To find the tabled value for a x^2 variable based on n-1 degrees of freedom with an area of a to its right, we need to use a chi-square distribution table.

For this problem, n = 51 and a = 0.025. First, we need to find the degrees of freedom. Since we are using a x^2 variable, the degrees of freedom is n - 1 = 51 - 1 = 50.

Next, we need to find the critical value from the chi-square distribution table with 50 degrees of freedom and an area of 0.025 to its right.

From the table, we find that the critical value is 69.338.

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The decision and outcome that represents a Type I error in this scenario is if the prisoner is released and kills a family of five in cold blood within 48 hours. A Type I error occurs when the null hypothesis is rejected even though it is actually true. In this case, if the parole board releases the prisoner based on the hypothesis that they have been rehabilitated but in reality, they have not been rehabilitated, it would result in a Type I error. The prisoner's release would lead to a tragic outcome, which could have been avoided if the null hypothesis had not been rejected.

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To determine how far a cheetah could run in 15 seconds at the given rate, we can use the formula:

distance = rate x time

Where the rate is the speed at which the cheetah is running and time is the duration of the run.

We are given that the cheetah runs 420 feet in 6 seconds. To find the rate at which the cheetah is running, we can divide the distance by the time:

Now we can use the rate and the given time of 15 seconds to find the distance the cheetah could run:

Therefore, the cheetah can run 1050 feet in 15 seconds.

the tradeoff between x and y is such that the weighted sum of their square roots is constant.

To derive the equation for the indifference curve, we need to find the combinations of x and y that yield the same level of utility U. Mathematically, we can express this as:

U(x, y) = constant

Substituting the given utility function, we get:

(.6x.5 + .4y.5)1/.5 = constant

Simplifying, we get:

(.36x + .16y) = constant^2

Dividing by the constant squared, we get:

(.36x + .16y)/constant^2 = 1

This is the equation for the indifference curve, which represents all the combinations of x and y that yield the same level of utility U. The constant represents the level of utility, and the equation shows that the tradeoff between x and y is such that the weighted sum of their square roots is constant.

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If events A and B are independent, then P(B|A) = P(B).

From the given information, we have:

P(B|A) = 0.32

P(A) = 0.54

Using the formula for conditional probability, we can write:

P(B|A) = P(A and B) / P(A)

Solving for P(A and B), we get:

P(A and B) = P(B|A) x P(A) = 0.32 x 0.54 = 0.1728

Now, to find P(B), we can use the formula:

P(B) = P(B and not A) + P(B and A)

Since A and B are independent, we have:

P(B and not A) = P(B) - P(A and B) = P(B) - 0.1728

Substituting the given values, we get:

P(B) - 0.1728 + 0.1728 = 0.33

P(B) = 0.33 + 0.1728 = 0.5028

Therefore, the probability of event B is 0.5028

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The correct expression for the estimated model is: In (odds ratio) = -7.8562 +0.0304 Income + 1.2804 Lawnsize. This model was created using binary logistic regression analysis to study the characteristics that differentiate home owners who have a lawn service (code 1) and those who do not (code 0).

The additional information available for these 30 home owners includes family income (Income, in thousands of dollars) and lawn size (Lawn Size, in thousands of square feet). The estimated model shows that for every one unit increase in income,

the odds of having a lawn service increase by 0.0304, and for every one unit increase in lawn size, the odds of having a lawn service increase by 1.2804. This information can be useful for the marketing manager to target potential customers based on their family income and lawn size.
The correct expression for the estimated model in this case is:

ln(odds ratio) = -7.8562 + 0.0304 Income + 1.2804 Lawn Size

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In this question, you are being asked to perform a Factor Analysis with Varimax rotation to identify the underlying structure between variables Q1 through Q26. The goal is to determine how many factors should be retained and the total variance explained by the model.

Factor analysis is a statistical method that helps to identify underlying factors or dimensions that explain the patterns of correlations among a set of observed variables. Varimax rotation is a popular method of rotating the factors to simplify and clarify the structure of the factor solution.

To determine how many factors to retain, we use the eigenvalue criterion of greater than one. The eigenvalue is a measure of how much variance in the original data is accounted for by each factor. A factor with an eigenvalue of greater than one indicates that it explains more variance than a single variable and should be retained.

After performing the Factor Analysis with Varimax rotation and using the eigenvalue criterion, let's say we were able to retain 4 factors. The total variance explained by this model would be the sum of the variances accounted for by each factor.

It's important to note that the interpretation of the factors will depend on the specific variables and context of the study. Factors are often labeled based on the variables that load most heavily onto them. The scores can be saved using the regression method, which calculates the factor scores for each observation based on the observed values of the variables.

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The value of the coordinates of the points R’, S’ and T’ are,

R' = (1, - 2)

S' = (- 8, 2)

T' = (- 4, 7)

We have to given that;

ΔRST is reflected across the line y=x to form ΔR'S'T'.

Here, All the coordinates are,

R = (- 2, 1)

S = (2, - 8)

T = (7, - 4)

Hence, After reflection across y = x, the coordinates of the points R’, S’ and T’ are,

R' = (1, - 2)

S' = (- 8, 2)

T' = (- 4, 7)

Thus, The value of the coordinates of the points R’, S’ and T’ are,

R' = (1, - 2)

S' = (- 8, 2)

T' = (- 4, 7)

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Answer:

WHere is the question

Step-by-step explanation:

An interesting relationship in the population may fail to achieve statistical significance if there are too few observations or sample size is too small.

Statistical significance is a measure of the probability that the observed relationship between variables in a sample could have occurred by chance alone. When a relationship is statistically significant, it means that the probability of observing the relationship by chance is very low, typically less than 5% (p < 0.05).

However, if the sample size is too small, there may not be enough data to detect a real relationship between variables, even if it exists in the population. In such cases, the observed relationship may not be statistically significant, even though it is important and meaningful.

Increasing the sample size can help to increase the power of the analysis, making it more likely to detect a true relationship between variables. Thus, having a sufficiently large sample size is important for achieving statistical significance and for making reliable conclusions about the relationship between variables.

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The probability of selecting a user who chooses cinnamon and is under 13 years old is: 0.18 or 18% (rounded to two decimal places).

In the problem, we are given the number of users who choose bacon and are under 13 years old, which is 8. We are also given the number of users who choose plain and are under 13 years old, which is 5. Therefore, the total number of users under 13 years old is 8 + 5 = 13.

Next, we are asked to find the probability of selecting a user who chooses cinnamon and is under 13 years old. We know that the number of users who choose cinnamon and are under 13 years old is 3. Therefore, out of the total 13 users under 13 years old, the probability of selecting a user who chooses cinnamon and is under 13 years old is:

The total number of users under 13 years old is 8 (choose bacon) + 5 (choose plain) = 13.

The number of users who choose cinnamon and are under 13 years old is 3.

Therefore, the probability of selecting a user who chooses cinnamon and is under 13 years old is:

3 / 13 ≈ 0.23 or 23% (rounded to two decimal places)

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The expression  3 / 4 a + 8 - 1 / 4 a - 2  is equivalent to  1 / 2(a + 12)..

'

Two expressions are said to be equivalent if they have the same value irrespective of the value of the variable(s) in them.

The equivalent expression can be found by simplifying the expression as follows:

Therefore,

3 / 4 a + 8 - 1 / 4 a - 2

collect like terms

3 / 4 a  - 1 / 4 a + 8 - 2

3a - 1a / 4 + 6

2a/ 4 + 6

1 / 2 a + 6

Therefore, the second expression is 1 / 2(a + 12).

Let's open the brackets

1 / 2(a + 12) = 1 / 2a + 6

Therefore, the expression are equivalent.

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We may build up a proportion and solve for the scale model radius of Neptune using the ratio between the radii of the two planets and the known scale model radius of the Earth. The scale model of Neptune that is produced has a radius of around 9.7 cm.

We may take advantage of the fact that the ratio between the two planets' radii and the ratio between their respective scale model radii is the same. Let's name the Neptune scale model radius "r" Then, we may set up the ratio shown below:

Neptune's radius is equal to the product of Earth's radius and its scale model.

With the provided values, we may simplify and obtain:

24620 km / 6370 km equals 2.5 cm / r

We obtain the following when solving for "r":

r = (24620 km * 2.5 cm) / (6370 km)

r ≈ 9.7 cm

Therefore, a scale model of Neptune would have to be drawn with a radius of approximately 9.7 cm.

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The equation of the graph is y = -| x | + 1

Given data ,

The graph of y = -|x| + 1 is a V-shaped graph with the vertex at the origin (0, 1), and it opens downwards along the y-axis. The negative sign in front of the absolute value function reflects the graph of y = |x| across the x-axis, flipping it upside down.

When x is greater than or equal to 0, the expression |x| becomes x, and the graph of y = -|x| + 1 will be y = -x + 1 for x ≥ 0.

When x is less than 0, the expression |x| becomes -x, and the graph of y = -|x| + 1 will be y = x + 1 for x < 0.

Thus, the graph of y = -|x| + 1 consists of two linear segments with slopes of -1, intersecting at the point (0, 1), and extends indefinitely in both directions along the x-axis.

Hence , the equation of graph is y = -| x | + 1

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y=-|x|+1

Step-by-step explanation:

I checked RSM, its correct

Answer:

16.642

Step-by-step explanation:

a. The scatter diagram for these data with the speed of execution as the independent variable would plot each brokerage firm's speed of execution score on the x-axis and their overall satisfaction rating score on the y-axis.

b. The scatter diagram developed in part (a) shows a positive correlation between the speed of execution and overall satisfaction rating. As the speed of execution score increases, the overall satisfaction rating score also tends to increase.

c. The least squares estimated regression equation is:

y = 2.108 + 0.473x

where y represents the overall satisfaction rating score and x represents the speed of execution score.

d. The slope of the estimated regression equation (0.473) represents the change in the overall satisfaction rating score for a one-unit increase in the speed of execution score. In other words, on average, for every increase of 1 in the speed of execution score, the overall satisfaction rating score is predicted to increase by 0.473.

e. If Zecco.com's speed of execution rating increased from 2.7 to the average speed of execution rating (3.3), we can use the estimated regression equation to predict their new overall satisfaction rating score:

y = 2.108 + 0.473(3.3) = 3.616

Therefore, we would predict a new overall satisfaction rating score of approximately 3.616 for Zecco.com if they increased their speed of execution rating to the average of the other 10 brokerage firms surveyed.

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An expression is equivalent to m + m + m + m which is a sum of two terms that can be written as 2(m) + 2(m).

To write an expression equivalent to m + m + m + m that is a sum of two terms, we can use the distributive property of multiplication over addition.

Combine like terms on the left-hand side to get 4m.

Factor out 4 from 4m to get 4(m).

Since we want to write this expression as a sum of two terms, we can split the 4 into 2 + 2.

Substitute the 2 + 2 for 4 in our factored expression from step 2 to get:

4(m) = 2(m) + 2(m)

Thus, an expression equivalent to m + m + m + m that is a sum of two terms is 2(m) + 2(m).

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