Yes, the conditions for inference are met. The teacher conducts 50 trials, which is large enough to meet the large counts condition (np ≥ 10 and n(1-p) ≥ 10).
The teacher's attempt to make the number cube unfair by inserting lead weights raises the question of whether the proportion of rolls that will land on a 1 has changed. To determine if she was successful, the teacher rolls the cube 50 times and keeps track of the number of times she rolls a 1. The data shows that she rolled a 1 15 times.
To determine if the data provides convincing evidence that the proportion of rolls that will land on a 1 is greater than one-sixth, we need to check if the conditions for inference are met.
The 10% condition requires that the sample size is less than 10% of the population size. Since we do not know the population size, we cannot determine if this condition is met.
The large counts condition requires that both the number of successes (15) and the number of failures (35) are greater than or equal to 10. This condition is met.
The randomness condition requires that the sample is random. Since the teacher rolled the cube, it is assumed that the rolls were random.
Therefore, the conditions for inference are met. We can use a hypothesis test to determine if the data provides convincing evidence that the proportion of rolls that will land on a 1 is greater than one-sixth.
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can yall please help me with this and please show your work please PLEASE THIS IS DUE TOMORROW
Answer:
2 specials are made possible
Step-by-step explanation:
2 of each category
Answer:
The answer is 6
Step-by-step explanation:
The answer is the total number of drinks,sandwiches and desserts
T(s)=2+2+2=6
Use Lagrange Multipliers to find the absolute maximum and absolute minimum values of f(x,y) subject to the constraint and determine the points where the absolute extrema occur.f(x,y)=5x+9y;x2+y2=49
The absolute maximum value of f(x,y) subject to the constraint [tex]x^2 + y^2 = 49[/tex] is 30√2, which occurs at the point (5√2/2, 3√2/2), and the absolute minimum value is -30√2, which occurs at the point (-5√2/2, -3√2/2).
What is Lagrange Multiplier?Lagrange Multiplier is a method used to find the extreme values of a function subject to one or more constraints. The method involves introducing a new variable, called a Lagrange multiplier, for each constraint in the problem.
We can use the method of Lagrange multipliers to find the absolute extrema of the function f(x,y) = 5x + 9y subject to the constraint [tex]x^2 + y^2 = 49.[/tex] We start by defining the Lagrangian function L(x,y,λ) as:
L(x,y,λ) = f(x,y) - λg(x,y)
where g(x,y) = [tex]x^2 + y^2 - 49[/tex] is the constraint function and λ is the Lagrange multiplier.
Taking partial derivatives of L with respect to x, y, and λ, we get:
∂L/∂x = 5 - 2λx = 0
∂L/∂y = 9 - 2λy = 0
∂L/∂λ = x² + y² - 49 = 0
∂L/∂λ [tex]= x^2 + y^2 - 49 = 0[/tex]
Solving these equations simultaneously, we get:
x = ±5√2/2, y = ±3√2/2, λ = 5/7
These are the critical points of f(x,y) subject to the constraint [tex]x^2 + y^2 = 49.[/tex]
To determine which of these critical points are absolute maxima and minima, we need to evaluate the function f(x,y) at these points and compare the values. We have:
f(5√2/2, 3√2/2) = 5(5√2/2) + 9(3√2/2) = 30√2
f(-5√2/2, -3√2/2) = 5(-5√2/2) + 9(-3√2/2) = -30√2
So, the absolute maximum value of f(x,y) subject to the constraint [tex]x^2 + y^2 = 49[/tex] is 30√2, which occurs at the point (5√2/2, 3√2/2), and the absolute minimum value is -30√2, which occurs at the point (-5√2/2, -3√2/2).
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"Snoqualmie" is a name shared by a waterfall and a tribe of Native Americans. In a study of the cultural importance of the waterfall, two groups of the Snoqualmie tribe were randomly surveyed. One group consisted of Snoqualmie members living less than 25 miles from the waterfall. Another group consisted of Snoqualmie members living more than 25 miles from the waterfall. The researchers asked each member to rate the cultural importance of the waterfall as low, medium, or high. Data from the study are presented in the following table. If the distributions of ratings are the same for those Snoqualmie members living less than 25 miles from the waterfall and those living more than 25 miles from the waterfall, which of the following is equal to the expected count of members living less than 25 miles from the waterfall who rated the cultural importance as high?
The expected count of members living less than 25 miles from the waterfall who rated the cultural importance as high is 60.
To determine the expected count of members living less than 25 miles from the waterfall who rated the cultural importance as high, we need to use the information provided in the table.
Here we need to find the total number of respondents in each group For those living less than 25 miles from the waterfall,
The total is 150.
For those living more than 25 miles from the waterfall,
the total is 100.
Again,we need to find the proportion of respondents in each group who rated the cultural importance as high.
For those living less than 25 miles from the waterfall,
the proportion is 60/150 = 0.4.
For those living more than 25 miles from the waterfall,
the proportion is 40/100 = 0.4.
Now, we can find the expected count of members living less than 25 miles from the waterfall who rated the cultural importance as high by multiplying the total number of respondents in that group (150) by the proportion who rated the cultural importance as high (0.4). Expected count = 150 x 0.4 = 60
Therefore, the expected count of members living less than 25 miles from the waterfall who rated the cultural importance as high is 60.
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A man has m identical hats that he keeps in two drawers, one fair coin in his pocket, and the following strange ritual. Each morning, he flips the coin to choose a drawer at random and take one hat from this drawer, if there is one, to wear all the day. In the evening of the days when he wears a hat, he flips again the coin to choose a drawer at random where to put the hat back. Find the fraction of days the man does not wear a hat.
The fraction of days the man does not wear a hat is 0.
To solve this problem, let's analyze the possible scenarios:
The man chooses a drawer with hats in the morning and returns the hat to the same drawer in the evening.
The man chooses a drawer with hats in the morning and returns the hat to the other drawer in the evening.
The man chooses an empty drawer in the morning and does not wear a hat.
Let's calculate the probabilities for each scenario:
Probability of choosing a drawer with hats in the morning: There are two drawers, so the probability is 2/2 = 1.
Probability of returning the hat to the same drawer in the evening: There is a 1/2 chance of choosing the same drawer, so the probability is 1/2.
Probability of choosing a drawer with hats in the morning: 2/2 = 1.
Probability of returning the hat to the other drawer in the evening: There is a 1/2 chance of choosing the other drawer, so the probability is 1/2.
Probability of choosing an empty drawer in the morning: There is a 0/2 chance of choosing a drawer with hats, so the probability is 0.
Now, let's calculate the fraction of days the man does not wear a hat:
Fraction of days without a hat = (Probability of scenario 3) = 0.
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a researcher interested in a data matrix that displays the frequency of some combination of possible responses to multiple categorical variables should construct a: a. marginal table b. perceptual map c. regression table d. contingency table
The researcher interested in analyzing the frequency of some combination of possible responses to multiple categorical variables should construct a contingency table.
A contingency table is a two-way table that displays the frequency of observations or counts for two or more categorical variables. The table is constructed by tabulating the counts or percentages of the variables in rows and columns. The contingency table can be used to identify relationships between variables and can be helpful in analyzing data and developing hypotheses.
In a contingency table, the frequency of observations is summarized in rows and columns, as well as in the margins. The marginal totals represent the total counts or percentages for each variable, and they are typically displayed at the bottom or the right side of the table.
The marginal totals provide an overview of the overall distribution of the variables and can be helpful in identifying patterns or trends. A researcher interested in analyzing multiple categorical variables may need to construct a multiple contingency table. This type of table displays the frequency of observations for more than two categorical variables.
The table is constructed by tabulating the counts or percentages of the variables in rows and columns, as well as in the margins. In summary, a researcher interested in analyzing the frequency of some combination of possible responses to multiple categorical variables should construct a contingency table.
The contingency table summarizes the frequency of observations in rows, columns, and margins, providing a comprehensive overview of the distribution of the variables. The table can be helpful in identifying relationships between variables and developing hypotheses.
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Which inequality describes the elevations of the starfish in the tide pool
Answer:
Step-by-step explanation:
3 -2
a manufacturer claims that its tires last at least 40000 km. as a result of the test made with 25 randomly selected tires, the average endurance time of the tires was calculated as 39750 km and the standard deviation was 387 km. accordingly, what is the test statistic value?
The test statistic value is -2.03.
What is the mean and standard deviation?
In statistics, the measurement of variability known as the standard deviation (SD) is frequently utilised. It displays the degree of variance from the mean (average). While a high SD shows that the data are dispersed throughout a wide range of values, a low SD suggests that the data points tend to be close to the mean.
We can use a one-sample t-test to test whether the mean endurance time of the tires is significantly different from the claimed value of 40000 km. The test statistic is given by:
[tex]t = (x - \mu) / (s / \sqrt{(n)})[/tex]
where x is the sample mean (39750 km), μ is the claimed mean (40000 km), s is the sample standard deviation (387 km), and n is the sample size (25).
Substituting the values, we get:
[tex]t = (39750 - 40000) / (387 / \sqrt{25})[/tex]
t = -250 / (387/5)
t = -2.03
Therefore, the test statistic value is -2.03.
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the qualified applicant pool for five management trainee positions consists of nine women and six men. (a) how many different groups of applicants can be selected for the positions? 3003 correct: your answer is correct. (b) how many different groups of trainees would consist entirely of women? 126 correct: your answer is correct. (c) probability extension: if the applicants are equally qualified and the trainee positions are selected by drawing the names at random so that all groups of five are equally likely, what is the probability that the trainee class will consist entirely of women? (round your answer to four decimal places.)
The favorable outcome is the number of all-women groups (126) and the total possible outcomes are all possible groups (3003). Therefore, P(all-women) = 126/3003 ≈ 0.0419 (rounded to four decimal places).
(a) To determine the number of different groups of applicants that can be selected for the positions, we use the combination formula: C(n, k) = n! / (k!(n-k)!) where n is the total number of applicants (9 women + 6 men = 15) and k is the number of positions (5). So, C(15, 5) = 15! / (5!(15-5)!) = 3003.
(b) To find the number of different groups of trainees consisting entirely of women, we use the same formula but with only the 9 women as applicants: C(9, 5) = 9! / (5!(9-5)!) = 126.
(c) To calculate the probability that the trainee class will consist entirely of women, we can use the formula P(event) = Number of favorable outcomes / Total possible outcomes. In this case, the favorable outcome is the number of all-women groups (126) and the total possible outcomes are all possible groups (3003). Therefore, P(all-women) = 126/3003 ≈ 0.0419 (rounded to four decimal places).
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The initial cost for producing a product is $800. The cost of producing each unit of the product is $7. The total cost is the sum of the initial cost and the cost per unit of the product time the number of units produced. What is the least number of units, x, that should be produced in order for the average total cost per unit to be $11 or less?
The least number of units that should be produced in order for the average total cost per unit to be $11 or less is 200.
The average total cost per unit is the total cost divided by the number of units produced. Let's denote the total cost as C and the number of units as x. Then the average total cost per unit is:
ATC = C/x
The total cost is the sum of the initial cost and the cost per unit of the product times the number of units produced:
C = 800 + 7x
Substituting this expression for C into the formula for ATC, we get:
ATC = (800 + 7x)/x
We want to find the least number of units, x, that should be produced in order for the average total cost per unit to be $11 or less. This can be written as an inequality:
ATC ≤ 11
(800 + 7x)/x ≤ 11
Multiplying both sides by x, we get:
800 + 7x ≤ 11x
4x ≤ 800
x ≤ 200
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find the standard deviation of the number of lines in use this support center expects to have at noon
The mean is higher than the median because the data is skewed to the right. The median is more resistant to the skew in the data.
To calculate the standard deviation of the number of lines in use that this support center expects to have at noon, we would need to have a dataset of the number of lines in use at different times.
If we have this dataset, we can use the following formula to calculate the standard deviation:
Standard deviation = √(sum((x - mean)²) / n)
Where:
x is the number of lines in use at a given time
mean is the mean of the number of lines in use across all times
n is the total number of times in the dataset
We can calculate the mean of the number of lines in use by adding up all the values and dividing by the total number of times. Once we have the mean, we can calculate the standard deviation using the formula above. However, without access to the dataset, it is not possible to provide a specific answer.
Therefore, The mean is higher than the median because the data is skewed to the right. The median is more resistant to the skew in the data.
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Complete question:
Let the random variable X represent the number of telephone lines in use by the technical support center of a software manufacturer at noon each day. The probability distribution of X is shown in the table below.
In a sentence of two, comment on the relationship between the mean and the median relative to the shape of this distribution.
finds sales = .22 + 1.8 (degrees over 32 Fahrenheit). Identify the "y-intercept".
In the given equation, find sales = 0.22 + 1.8 (degrees over 32 Fahrenheit), the "y-intercept" is the value of "find sales" when the "degrees over 32 Fahrenheit" is equal to 0.
To find the y-intercept, substitute 0 for "degrees over 32 Fahrenheit":
Find sales = 0.22 + 1.8(0)
Find sales = 0.22
So, the y-intercept is 0.22.
In analytic geometry, using the common convention that the horizontal axis represents a variable x and the vertical axis represents a variable y, a y-intercept or vertical intercept is a point where the graph of a function or relation intersects the y-axis of the coordinate system. As such, these points satisfy x = 0.
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Find the area of the figure. A drawing of a rhombus with both diagonals bisecting each other at right angles. The vertical diagonal is divided into two lengths of 6 meters each and the horizontal diagonal is divided into two lengths of 9 meters each
The area of the rhombus is 108 meters²
Area of Rhombus:
A rhombus is a type of quadrilateral with four sides of equal length. It is also known as a diamond or a lozenge.
The diagonals of a rhombus bisect each other at right angles, and they also bisect the angles of the rhombus. The area of a rhombus can be found by multiplying the lengths of its diagonals and dividing by 2.
Hence, the Area of the rhombus (A) = d₁d₂/2
Where d₁ and d₂ are lengths of diagonals
Here we have
A drawing of a rhombus with both diagonals bisecting each other at right angles. The vertical diagonal is divided into two lengths of 6 meters each and the horizontal diagonal is divided into two lengths of 9 meters each
From the given data,
Length of vertical diagonal (d₁) = 2 × 6 = 12 meters
Length of horizontal diagonal (d₂) = 2 × 9 = 18 meters
Using the formula, Area of the rhombus (A) = d₁d₂/2
= (12)(18)/2 = 6 (18) = 108 meters²
Therefore,
The area of the rhombus is 108 meters²
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each piece of candy in a store costs a whole number of cents. casper has exactly enough money to buy either 12 pieces of red candy, 14 pieces of green candy, 15 pieces of blue candy, or n pieces of purple candy. a piece of purple candy costs 20 cents. what is the smallest possible value of n?(2019 amc 12b
To find the smallest possible value of n, we need to consider the cost of each candy option. We know that each piece of candy costs a whole number of cents,
which means that the cost of 12 pieces of red candy, 14 pieces of green candy, and 15 pieces of blue candy must be multiples of 12, 14, and 15 cents, respectively. Let's consider the cost of 12 pieces of red candy. Since the cost must be a multiple of 12 cents, the total cost of 12 pieces of red candy must be 12x cents, where x is some whole number.
Similarly, the total cost of 14 pieces of green candy must be 14y cents, and the total cost of 15 pieces of blue candy must be 15z cents, where y and z are whole numbers. Now let's consider the cost of n pieces of purple candy. We know that each piece costs 20 cents, so the total cost of n pieces of purple candy is 20n cents.
We also know that Casper has exactly enough money to buy any of these options. This means that the total cost of the candy he chooses must be less than or equal to the amount of money he has. Let M be the amount of money Casper has. Then we have: 12x + 14y + 15z ≤ M, 20n ≤ M.
We want to find the smallest possible value of n, so we should start by finding the largest possible value of x, y, and z that still satisfies the inequality above. To do this, we can use the fact that the greatest common divisor of 12, 14, and 15 is 1. This means that any number that is a multiple of 12, 14, and 15 must be a multiple of their least common multiple, which is 12 x 14 x 15 = 2520.
So we can write: 12x + 14y + 15z = k(2520), where k is some whole number. This equation tells us that the cost of any combination of red, green, and blue candy that Casper chooses must be a multiple of 2520 cents. Now we can substitute this equation into the first inequality above: k(2520) ≤ M.
Dividing both sides by 2520, we get: k ≤ M/2520
So the largest possible value of k that still satisfies the inequality is ⌊M/2520⌋, where ⌊⌋ denotes the floor function. Now we can use this to find the smallest possible value of n. We know that the total cost of n pieces of purple candy must be less than or equal to M,
so we can write: 20n ≤ M - k(2520), Substituting the largest possible value of k that we found above, we get: 20n ≤ M - ⌊M/2520⌋(2520), To find the smallest possible value of n, we want to maximize the right-hand side of this inequality. This happens when the floor function evaluates to its smallest possible value,
which is ⌊M/2520⌋ - 1. So we can write: 20n ≤ M - (⌊M/2520⌋ - 1)(2520), Simplifying, we get: 20n ≤ M - 2520⌊M/2520⌋ + 2520, 20n ≤ M - 2520(⌊M/2520⌋ - 1), Since n is a whole number, the smallest possible value of n that satisfies this inequality is: n = ⌊(M - 2520(⌊M/2520⌋ - 1))/20⌋ + 1.
This formula gives us the smallest possible value of n that allows Casper to buy a whole number of purple candies with the money he has.
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A store pays $328 for a playground slide. The store marks up the price by 45 1/8% what is the new price?
The new price of the playground slide after a 45 1/8% markup is $476.25.
To find the new price after a 45 1/8% markup, we need to first calculate the markup amount and then add it to the original price.
Markup amount = original price x markup rate
Markup rate = 45 1/8% = 45.125%
We need to convert the percentage to a decimal by dividing by 100
Markup rate = 45.125% ÷ 100 = 0.45125
Now we can calculate the markup amount
Markup amount = $328 x 0.45125 = $148.25
To find the new price, we add the markup amount to the original price
New price = original price + markup amount
New price = $328 + $148.25 = $476.25
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Tara made 6 dozen cookies and brought them to the school bake sale in a big container. So far, she has taken 12 cookies out of the container to give to customers. Of the cookies she has taken out, 2 were broken and 10 were whole. Based on the data, estimate how many cookies of the remaining 60 are whole
The estimated number of whole cookies of the remaining 60 is 50.
What is Estimating:
Estimating is the process of making an approximate calculation or judgment based on incomplete or uncertain information.
It involves using reasoning and previous experience to make an educated guess about a quantity, value, or outcome.
Estimating is often used when exact calculations are not feasible or practical, or when only a rough estimate is needed.
Here we have
Tara made 6 dozen cookies and brought them to the school bake sale in a big container.
She has taken 12 cookies out of the container to give to customers. Of the cookies she has taken out, 2 were broken and 10 were whole.
The total number of cookies Tara made = 6 × 12 = 72 cookies.
She took 12 cookies out of the container i.e 1 dozen
In which broken cookies = 2 i.e 2/12 = 1/6
The whole cookies = 10/12 = 5/6
Hereafter taking 12 cookies, remaining cookies = 72 - 12 = 60
Hence, the estimated number of whole cookies calculated as
the estimated number of whole cookies = (5/6)(60) = 5(10) = 50
Therefore,
The estimated number of whole cookies of the remaining 60 is 50.
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Rewrite equation in standard form
i got you, buddy!
To rewrite the equation in standard form, we need to complete the square for both x and y terms.
Starting with the x terms: x^2 + 4x + y^2 - 10y = 7 (x^2 + 4x) + y^2 - 10y = 7 (x^2 + 4x + 4) + y^2 - 10y = 7 + 4 (adding and subtracting 4 to complete the square for x) (x + 2)^2 + y^2 - 10y = 11
Now completing the square for y terms: (x + 2)^2 + y^2 - 10y = 11 (x + 2)^2 + (y^2 - 10y + 25) = 11 + 25 (adding and subtracting 25 to complete the square for y) (x + 2)^2 + (y - 5)^2 = 36
Therefore, the equation in standard form is: (x + 2)^2 + (y - 5)^2 = 36
Must be written as an equation
Answer:
10x^2 - 40x = 10x(x - 40)
H.A. y = 1/2
V.A. x = 0, x = 40
9. You receive a $40 gift card to a movie theater. A ticket to a matinee movie costs $5, and a ticket to an
evening movie costs $8. Define the variable and write a system of inequalities for the number of tickets
you can purchase using the gift card.
Answer:
x - matinee tickets
y - evening tickets
x≥0
y≥0
5x+8y≤40
Please help I'm lost :(
Name the ordered pair of one of the zeros for the following function.
f(x)=x2+7x−8
The ordered pair of one of the zeros for the following function is (x +8)
How to determine the zeros of the functionWe have that the function is a quadratic function written as;
f(x)=x2+7x−8
Using the factorization method of solving quadratic functions;
Multiply the coefficient of x squared by the constant in the expression, we have;
1(-8) = -8
Now, find the pair factors of the product that sum up to give 7, we have;
8x and -x
Substitute the values
x² + 8x - x - 8
group in pairs
(x² + 8x) - (x - 8)
factorize
x(x + 8) - 1(x + 8)
Then, we have;
x = 1
x = -8
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Use the following pattern and inductive reasoning to predict the answer to 9 x 7,654,321 - 1 .
Using the following pattern and inductive reasoning, predicted answer to 9 x 7,654,321 - 1 is 73,888,889.
The pattern:
When you subtract 1 from a number that ends with a sequence of n consecutive digits (all equal to d), the result is a number that ends with the same n digits followed by ([tex]10^{n}[/tex] - 1) -d.
For example:
If you subtract 1 from a number that ends with three 7's, the result is a number that ends with three 6's, i.e., (777-1=776).
If you subtract 1 from a number that ends with four 2's, the result is a number that ends with four 1's, i.e., (2222-1=2221).
Applying this pattern to 9 x 7,654,321 - 1:
The number ends with one 9, so n=1 and d=9.
Therefore, the result will end with one 8 (one less than 9), followed by ([tex]10^{1}[/tex] - 1) - 9 = 0, i.e., it will end with 8.
So, the predicted answer is 73,888,889.
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Consider a data set {7,10,20,28,35), perform hierarchical clustering using the single linkage and plot the dendogram to visualize it (note you need to do it by hand without using software package).
This gives us a dendrogram with three levels, where the first level has two clusters {{7,10},{20,28}} and {35}, the second level has two clusters {{7,10,20,28},35}, and the third level has only one cluster {{7,10,20,28,35}}.
What is a sequence?
A sequence is an enumerated collection of objects in which repetitions are allowed. Like a set, it contains members (also called elements, or terms).
To perform hierarchical clustering using single linkage, we start by treating each point as its own cluster, and then iteratively merge the two closest clusters until only one cluster remains. We use the single linkage method, which defines the distance between two clusters as the minimum distance between any two points in the clusters.
First, we calculate the pairwise distances between each point:
7 10 20 28 35
7 - 3 13 21 28
10 3 - 10 18 25
20 13 10 - 8 15
28 21 18 8 - 7
35 28 25 15 7 -
Next, we find the two closest points/clusters and merge them:
7,10 20 28 35
7,10 - 10 18 25
20 10 - 8 15
28 18 8 - 7
35 25 15 7 -
The closest points/clusters are 7 and 10, so we merge them to form a new cluster {7,10}.
7,10 20,28 35
7,10 - 18 25
20,28 18 - 7
35 25 7 -
The closest points/clusters are now {20,28} and 35, so we merge them to form a new cluster {{20,28},35}.
7,10 {20,28,35}
7,10 - 7
{20,28,35} 7 -
The closest points/clusters are now {7,10} and {{20,28},35}, so we merge them to form a new cluster {{{7,10},{20,28}},35}.
Hence, This gives us a dendrogram with three levels, where the first level has two clusters {{7,10},{20,28}} and {35}, the second level has two clusters {{7,10,20,28},35}, and the third level has only one cluster {{7,10,20,28,35}}.
The dendrogram can be visualized as in the attached image.
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at a certain grocery checkout counter, the average waiting time is 2.5 minutes. suppose the waiting times follow an exponential density function. (a) write the equation for the exponential distribution of waiting times. e(t) = graph the equation and locate the mean waiting time on the graph. webassign plot webassign plot webassign plot webassign plot (b) what is the likelihood that a customer waits less than 1 minutes to check out? (round your answer to one decimal place.) % (c) what is the probability of waiting between 4 and 6 minutes? (round your answer to one decimal place.) % (d) what is the probability of waiting more than 5 minutes to check out? (round your answer to one decimal place.) % need help? read it
a) The equation for the exponential distribution of waiting times is given by [tex]f(x) = \lambda e^{-\lambda x}[/tex]
b) The probability of waiting less than 2 minutes to check out is 0.427
c) The probability of waiting between 4 and 6 minutes is 0.242
d) The probability of waiting more than 5 minutes to check out is 0.082
a. The equation for the exponential distribution of waiting times is given by:
[tex]f(x) = \lambda e^{-\lambda x}[/tex]
where λ is the rate parameter of the distribution, and e is the natural logarithmic constant (approximately equal to 2.71828). The graph of the exponential distribution is a decreasing curve that starts at λ and approaches zero as x approaches infinity. The mean waiting time, denoted by E(X), is equal to 1/λ.
b. To find the probability that a customer waits less than 2 minutes to check out, we need to calculate the area under the exponential distribution curve between zero and 2 minutes. This can be expressed mathematically as:
P(X < 2) = [tex]\int_0^2 \lambda e^{-\lambda x} dx[/tex]
Solving this integral yields:
P(X < 2) = 1 - [tex]e^{(-2\lambda)}[/tex]
Substituting the given average waiting time of 2.5 minutes into the formula for the mean waiting time, we can calculate λ as:
E(X) = 1/λ
2.5 = 1/λ
λ = 0.4
Therefore, the probability of waiting less than 2 minutes to check out is:
P(X < 2) = 1 - [tex]e^{-2*0.4}[/tex]
P(X < 2) ≈ 0.427
c. To find the probability of waiting between 2 and 4 minutes, we need to calculate the area under the exponential distribution curve between 2 and 4 minutes. This can be expressed mathematically as:
P(2 < X < 4) =[tex]\int_2^4 \lambda e^{(-\lambda x)} dx[/tex]
Solving this integral yields:
P(2 < X < 4) = [tex]e^{(-2\lambda)} - e^{(-4\lambda)}[/tex]
Substituting the value of λ obtained in part (b), we get:
P(2 < X < 4) = [tex]e^{(-20.4)} - e^{(-40.4)}[/tex]
P(2 < X < 4) ≈ 0.242
d. To find the probability of waiting more than 5 minutes to check out, we need to calculate the area under the exponential distribution curve to the right of 5 minutes. This can be expressed mathematically as:
P(X > 5) = [tex]\int_5^{ \infty} \lambda e^{(-\lambda x)} dx[/tex]
Solving this integral yields:
P(X > 5) = [tex]e^{(-5\lambda)}[/tex]
Substituting the value of λ obtained in part (b), we get:
P(X > 5) = [tex]e^{(-5*0.4)}[/tex]
P(X > 5) ≈ 0.082
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which of the following samples could constitute a discrete random variable? i. total number of points score in a football game. ii. height of the ocean's tide at a given location. iii. number of near collisions of aircraft in a year.
The sample that could constitute a discrete random variable is the number of near collisions of aircraft in a year.
This is because it is a countable, finite number and not a continuous measurement like the height of the ocean's tide. The total number of points scored in a football game could also be considered a discrete random variable because it is a countable, finite number. However, the height of the ocean's tide is a continuous measurement and cannot be counted as a discrete random variable.
The sample that could constitute a discrete random variable is: i. total number of points scored in a football game.
A discrete random variable represents a countable number of distinct values or outcomes. In this case, the total number of points scored in a football game can be counted and listed, making it a discrete random variable.
On the other hand, the height of the ocean's tide at a given location (ii) is a continuous random variable, as it can take any value within a given range, and the number of near collisions of aircraft in a year (iii) could also be considered as a discrete random variable, but it's not one of the options in your question.
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to study the interest in sports of junior high kids, an organization sampled students surveyed all students. some were active in after school activities and some were not. would you expect the results to be biased? why or why not? group of answer choices yes, only students who are already active in after-school activities should be sampled no, the survey represented both groups in the population of students yes, the wording of the questions might push kids to a specific answer no, as inactive kids likely feel the same as the active kids no, sports are universally of interest
Answer:
B. no, the survey represented both groups in the population of students
Step-by-step explanation:
It says, "surveyed all students"
3x+3y=9 ordered pair
The ordered pairs of the linear expression 3x + 3y = 9 is (0, 3)
What are the ordered pairs of the linear expressionFrom the question, we have the following parameters that can be used in our computation:
The linear expression 3x+3y=9
To determine the ordered pairs of the linear expression, we set x to any value say x = 0 0 and then calculate the value of y
Using the above as a guide, we have the following:
3(0) + 3y = 9
Evauate
3y = 9
Divide both sides by 3
y = 3
This means that the value of y is equal to 3
So, we have (0, 3)
Hence, the ordered pairs of the linear expression is (0, 3)
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____ ____ are calculations used to predict a person's to score on one variable when that person's score on another variable is already known.
Regression equation are calculations used to predict a person's to score on one variable when that person's score on another variable is already known. So, option(C) is right one.
Statistical study is used to collect and analyze data and is useful in census. The collected data is used to interpret economic activities. Statistics can be qualitative or quantitative in nature. The regression analysis is used to determine the line of best fit for the dependent variable and independent variables. The equation form of regression line is written as, Y= a + bX, where
Y is the dependent variableX is the independent variableb is the slope of line aa is the y-intercept.It is an analysis to measure the relationship between a dependent variable and two or more. independent variables. So the correct choice is the regression equation.
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Complete question:
____ ____ are calculations used to predict a person's to score on one variable when that person's score on another variable is already known.
A. Pearson product-moment correlation coefficient
B. Coefficient of determination
C. Regression analysis
D. Point-biserial correlation coefficient
a random variable x has the following probability distribution. x f(x) 0 0.27 1 0.35 2 0.05 3 0.25 4 0.08 (a) determine the expected value of x. (b) determine the variance.
The probability distribution of random variable x is
a) 1.78 is the anticipated value of x;
b) The variance of x is 0.6484.
(a) The expected value of x can be found using the formula:
[tex]E(x) = Σ[x * f(x)][/tex]
where x's potential values are all added up.
Using the given probability distribution, we have:
[tex]E(x) = (0 * 0.27) + (1 * 0.35) + (2 * 0.05) + (3 * 0.25) + (4 * 0.08)[/tex]
[tex]E(x) = 1.78[/tex]
As a result, 1.78 is the expected value of x.
(b) The variance of x can be found using the formula:
[tex]Var(x) = E(x^2) - [E(x)]^2[/tex]
where E(x) represents the anticipated value of x and E(x2) represents the expected value of x2.
To find E(x^2), we can use the formula:
[tex]E(x^2) = Σ[x^2 * f(x)][/tex]
Using the given probability distribution, we have:
[tex]E(x^2) = (0^2 * 0.27) + (1^2 * 0.35) + (2^2 * 0.05) + (3^2 * 0.25) + (4^2 * 0.08)[/tex]
[tex]E(x^2) = 3.33[/tex]
Consequently, the variation of x is:
[tex]Var(x) = E(x^2) - [E(x)]^2[/tex]
[tex]var(x) = 3.33 - (1.78)^2[/tex]
[tex]var(x) = 0.6484[/tex] (rounded to four decimal places)
x's variance is 0.6484
As a result, x's variance is 0.6484.
1.78 is the anticipated value of x
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A survey asked 1,150 people to choose their favorite laundry detergent from brands A, B, and C. Of the people surveyed, x percent chose A as their favorite brand. If x is rounded to the nearest integer, the result is 3. Which of the following could be the number of people who chose A as their favorite brand?
Indicate all such numbers.
â 20
â 25
â 30
â 35
â 40
â 45
â 50
The a survey of 1,150 people for choose their favorite laundry detergent from brands. If percent value of people who choose brand A is 3, then the number of people who chose A as their favorite brand are 30 , 35 , 40.
We have a survey results of total 1150 people. That is sample size = 1150
The survey is based on their favorite laundry detergent from brands A, B, and C. The percent of people who chose A as their favorite brand = x %
If x = 3, we have to determine the number of people who chose A as their favorite brand. So, we use percentage formula, for each option and see for which number of people percent is equals to 3. Now, percent formula is [tex] \frac{value}{total \: value}×100%.[/tex]
a) [tex] (\frac{20}{1150 }) 100 \% = 1.73 \%[/tex] ≈ 2%
b) [tex] (\frac{25}{1150 }) 100 \% = 2.17 \%[/tex] ≈ 2%
c) [tex] (\frac{30}{1150 }) 100 \% = 2.60\%[/tex] ≈ 3 %
d) [tex] (\frac{35}{1150 }) 100 \% = 3.04\%[/tex]≈ 3 %
e)[tex] (\frac{40}{1150 }) 100\% = 3.48 \%[/tex] ≈ 3%
f)[tex] (\frac{45}{1150 }) 100 \% = 3.91 \%[/tex]≈ 4%
g)[tex] (\frac{50}{1150 }) 100 \% =4.35 \%[/tex] ≈ 4%
Hence, all such numbers are 30 , 35 , 40.
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Complete question:
A survey asked 1,150 people to choose their favorite laundry detergent from brands A, B, and C. Of the people surveyed, x percent chose A as their favorite brand. If x is rounded to the nearest integer, the result is 3. Which of the following could be the number of people who chose A as their favorite brand? Indicate all such numbers.
a)20
b) 25
c)30
d) 35
e) 40
f) 45
g) 50
If x percent of people chose A as their favorite brand, then the number of people who chose A can be found by multiplying x percent by the total number of people surveyed:
number of people who chose A = (x/100) * 1150
Since x is rounded to the nearest integer and equals 3, we have:
number of people who chose A = (3/100) * 1150 = 34.5, which is closest to 35.
If x percent chose brand A as their favorite and x is rounded to the nearest integer, it means that x lies between x-0.5 and x+0.5. In other words, x-0.5 <= actual percentage of people who chose A <= x+0.5.
From the given information, we know that x rounded to the nearest integer is 3. Therefore, we have:
2.5 <= x <= 3.5
Since x represents the percentage of people who chose brand A, we can find the number of people who chose A as their favorite by multiplying x with the total number of people surveyed (1150). Therefore, the number of people who chose brand A lies between:
2.5% of 1150 <= number of people who chose A <= 3.5% of 1150
28.75 <= number of people who chose A <= 40.25
Since the number of people who chose A must be a whole number, the only possible value for the number of people who chose is 29.
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Part A) You are performing a left-tailed test with test statistic z = − 2.816 , find the p-value accurate to 4 decimal places. p-value = Part B)Your claim results in the following alternative hypothesis: Ha : p ≠ 27% which you test at a significance level of α = .10 . Find the positive critical value, to three decimal places. zα/2 = Part C)You are performing a left-tailed test with test statistic z = − 2.816 , find the p-value accurate to 4 decimal places. p-value = Part D)With Ha : p ≠ 45% you obtain a test statistic of z = 2.723 . Find the p-value accurate to 4 decimal places. p-value =
Part A) the p-value is 0.0025.
Part B) the positive critical value is:zα/2 = |1.645| = 1.645 (rounded to three decimal places).
what is statistics?
Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of numerical data.
Part A) For a left-tailed test with a test statistic of z = -2.816, the p-value can be calculated using a standard normal distribution table or a calculator. The p-value is the area to the left of the test statistic in the standard normal distribution.
Using a standard normal distribution table, the area to the left of z = -2.816 is 0.0025. Therefore, the p-value is 0.0025.
Alternatively, using a calculator such as the TI-84, the p-value can be found by entering the command "normalcdf(-9999,-2.816)" which gives a result of 0.0025, accurate to 4 decimal places.
Therefore, the p-value is 0.0025.
Part B) For a two-tailed test at a significance level of α = 0.10, the critical values can be found using a standard normal distribution table or a calculator. The critical values are the z-scores that leave α/2 in each tail.
Using a standard normal distribution table, the critical value for α/2 = 0.05
Therefore, the positive critical value is:zα/2 = |1.645| = 1.645 (rounded to three decimal places)
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The boxplot shown below results from the heights (cm) of males listed in a data set. What do the numbers in that boxplot tell us? 153 174.7 194
Answer:
the boxplot tells us that the median height of males in the data set is approximately 174.7 cm. The middle 50% of the males in the data set have heights between approximately 153 cm (25th percentile) and 194 cm (75th percentile). There are no outliers in the data set.
Step-by-step explanation:
The boxplot provides a visual representation of the distribution of the heights of males in the data set. The box represents the middle 50% of the data, with the bottom of the box indicating the 25th percentile and the top indicating the 75th percentile. The line within the box represents the median height, which is the middle value of the data set. The whiskers represent the range of the data, with the bottom whisker extending from the bottom of the box to the smallest observation within 1.5 times the interquartile range (IQR) below the bottom of the box, and the top whisker extending from the top of the box to the largest observation within 1.5 times the IQR above the top of the box. Any outliers beyond the whiskers are indicated as individual points.
