I have no idea what this is​

The price of the headphones after the discount is $14.95

Madison bought headphones for $65

She used a coupon code of 30% discount

= (1-30/100)× (1 +10/100)

= (1-0.3) × (1 + 0.1)

= 0.7 × 1.1

= 0.77 × 65

= 50.05

The price after discount is

= 65-50.05

= $14.95

Hence the price after discount is $14.95

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the mean  decrease and the standard deviation  increase.

To find the new mean and standard deviation, we need to remove the score of 30 from the original set of ten test scores and recalculate the mean and standard deviation based on the remaining nine scores.

First, we can find the sum of the original set of ten test scores:

10 * 50 = 500

Then, we can subtract the score of 30:

500 - 30 = 470

Now we can find the new mean based on the remaining nine scores:

470 / 9 = 52.22

Next, we can find the sum of the squared deviations from the new mean:

(52.22 - 50)^2 + ... + (52.22 - x)^2

where x is the score of the remaining student.

Since we don't know the value of x, we can't calculate the exact value of the standard deviation for the new set of nine scores. However, we do know that the standard deviation is a measure of how spread out the scores are, and removing a score that is below the mean (such as the score of 30) will tend to make the scores less spread out. Therefore, we can conclude that the standard deviation of the new set of nine scores would likely decrease.

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The mean fill in ounces should be set at 5.068 to ensure that 98% of 6 oz cups will not overflow.

To determine the value of mean that will ensure that 98% of 6 oz cups will not overflow, we can use the following steps:

Calculate the z-score corresponding to the desired percentile (98%):

Using a standard normal distribution table or calculator, we can find that the z-score corresponding to the 98th percentile is approximately 2.33.

Convert the given cup size (6 oz) to a z-score using the formula:

z = (x - μ) / σ

where x is the given cup size, μ is the mean fill in ounces, and σ is the standard deviation in ounces. Solving for μ, we get:

μ = x - z × σ

= 6 - 2.33 × 0.4

= 5.068

Therefore, the mean fill in ounces should be set at 5.068 to ensure that 98% of 6 oz cups will not overflow.

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As we have proved that the formula F = A(F/A, i, n) = A[((1+i)ⁿ - 1)/i] and it  shows that the future value of an annuity can be calculated by multiplying the annuity payment by the sum of a geometric series with a common ratio.

Let's assume that the annuity has n payments, and each payment is denoted by A. The future value of the first payment is A(1+i)ⁿ-1. This is because the first payment will earn interest for n periods, which is represented by (1+i)ⁿ, and then we subtract the original payment A because it was not earning interest. Similarly, the future value of the second payment will be A(1+i)ⁿ-2, and so on.

Therefore, the total future value of the annuity will be the sum of all the future values of each payment. We can express this as:

F = A(1+i)ⁿ-1 + A(1+i)ⁿ-2 + ... + A(1+i)⁰

Now, let's try to simplify this expression. We can factor out A to get:

F = A[(1+i)ⁿ-1 + (1+i)ⁿ-2 + ... + (1+i)⁰]

Notice that the expression inside the brackets is a geometric series with a common ratio of (1+i)⁻¹ and n terms. We can use the formula for the sum of a geometric series to simplify this expression further. This formula is:

S = a(1-rⁿ)/(1-r)

where a is the first term, r is the common ratio, and n is the number of terms.

In our case, the first term is (1+i)ⁿ-1, the common ratio is (1+i)⁻¹, and there are n terms. Therefore, we can substitute these values into the formula to get:

S = (1+i)ⁿ-1[(1+i)⁻ⁿ - 1]/[(1+i)⁻¹ - 1]

Simplifying this expression gives:

S = [(1+i)ⁿ - 1]/i

Now, substituting this value of S into the expression we obtained earlier for F, we get:

F = A[(1+i)ⁿ-1 + (1+i)ⁿ-2 + ... + (1+i)⁰]

= A[(1+i)ⁿ - 1]/i

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Complete Question:

Given (F/P, i, N) = P(1+i)ⁿ, and (P/F, i, N) = F/(1+i)ⁿ, prove the following formula

F =  A(F/A, i , n)  = A [((1+i)ⁿ - 1) / i]

Answer: There are 21,000,000 phone numbers that can be made under the given constraints.

Step-by-step explanation:

Given the first digit must be 1, the second digit must be a number in the range of 3-5, the third digit must be a number in the range of 6-9, and the last seven digits can be any single digit number 0-9.

There are 3 possible choices for the second digit (3, 4, or 5) and 4 possible choices for the third digit (6, 7, 8, or 9). For the last seven digits, there are 10 choices for each digit.

Therefore, the total number of phone numbers that can be made is:

1 x 3 x 4 x 10 x 10 x 10 x 10 x 10 x 10 x 10 = 21,000,000

Thus, there are 21,000,000 phone numbers that can be made under the given constraints.

Answer:

Step-by-step explanation:

Two numbers that each have exactly 16 factors two of which are 8 and 12 are 120 and 168

Factors of 120 = 1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 24, 30, 40, 60 and 120

Total number of factors of 120 = 16

Pairs of factors = (1, 120) (2,60) (3,40) (4,30) (5,24) (6,20) (8,15) (10,12)

Total Pairs of factor = 8

Hence the number of total factors = 16

Factors of 168 = 1, 2, 3, 4, 6, 7, 8, 12, 14, 21, 24, 28, 42, 56, 84 and 168

Total number of factors of 168 = 16

Pairs of factors = (1, 168) (2,84) (3,56) (4,42) (6,28) (7,24) (8,21) (12,14)

Total Pairs of factor = 8

Hence the number of total factors = 16

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The factors of the trinomial are

In the trinomial, 2x^2 + 22x + 60. We can look for  two values that:

The two numbers we use is 10 and 12 and hence we write as below

= 2x^2 + 22x + 60

= 2x^2 + 10x + 12x + 60

grouping them gives

= (2x^2 + 10x) + (12x + 60)

= 2x(x + 5) + 12(x + 5)

= (2x + 12)(x + 5)

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The number that should be subtracted from thrice 13 by 5 to obtain 5 by 13 is 130.

The number that should be subtracted from thrice 13 by 5 to obtain 5 by 13 is calculated as follows;

let the number = n

thrice 13 by 5 = 3 (13) x 5 = 195

5 by 13 = 5 x 13 = 65

The value of the unknown number, n,  is calculated as;

195 - n = 65

n = 195 - 65

n = 130

Thus, when we subtract 130 from thrice 13 by 5, we will obtain 5 by 13 as shown in the calculation above.

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Answer: 1/11

Step-by-step explanation: to get the answer you add all of the marbles and you get 12 you then see that to get the first marble the probability is 4/12 you then have to multiply the second probability which its 3/11 because it is without replacement. you multiply them together and get 1/11 which is your answer

Answer: 1/11

Step-by-step explanation:

P(E)= 4/12*3/11

      = 1/3*3/11

      = 1/11

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for a two-tailed test at a 12.66% significance level, the critical values of z are approximately +1.53 and -1.53. We are looking for the z-score that corresponds to the area to the right of it (for the positive critical value) equal to 0.0633.

For a two-tailed test at a 12.66% significance level, we need to find the critical value of z.

Step 1: Determine the significance level for each tail.
Since this is a two-tailed test, we need to divide the overall significance level (12.66%) by 2. This gives us 6.33% (or 0.0633 in decimal form) in each tail.

Step 2: Find the z-score associated with the given significance level.
To find the critical value of z, you can use a z-table or an online calculator. We are looking for the z-score that corresponds to the area to the right of it (for the positive critical value) equal to 0.0633.

Using a z-table or calculator, you'll find that the z-score is approximately ±1.53.

So, for a two-tailed test at a 12.66% significance level, the critical values of z are approximately +1.53 and -1.53.

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The correct option is (a) The correlation coefficient is approximately 0.57, with a positive direction; the slope is 0.41.

A scatter plot is a graph in which the values of two variables are plotted along two axes. The pattern of the resulting points reveals any correlation present between the two variables. If a line can be drawn through the points in such a way that most of the points fall close to the line, then there is a strong correlation between the two variables.

According to the given options, option (a) is the correct one as it states that the correlation between the age and bone density loss is positive and moderately strong, and the slope of the least squares regression line is 0.41. A positive correlation indicates that as the age of a person increases, the bone density loss also increases.

The slope of the least squares regression line measures the steepness of the line that best fits the data, and it tells us how much the bone density loss is expected to increase for a one-year increase in age.

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--The given question is incomplete, the complete question is given

" create a scatter plot of these two variables, with age as the x variable and bone density loss as the y variable. what is your conclusion about this linear relationship; and what is the slope of the least squares regression line?a.the correlation coefficient is approx 0.57, with a positive direction; the slope is 0.41.b.moderately strong negative linear relationship; slope of the least squares line is approx negative 0.41.c.a circular linear positive relationship; slope of the least squares line is approx positive 0.41d.none of the above "--

If a graph has vertices and edges such that there are no cycles (i.e. it is acyclic) and it is connected, then the graph must be a tree.

To prove this, we first note that a tree is defined as a connected, acyclic graph. Therefore, if we can show that a graph with the given properties is also connected and acyclic, then it must be a tree.

First, let us prove that the graph is connected. Since there are no cycles, every vertex must be part of a path. Moreover, since there are no isolated vertices, every vertex is connected to at least one other vertex. Therefore, if we start at any vertex and follow any path, we will eventually reach all other vertices. Hence, the graph is connected.

Next, let us prove that the graph is acyclic. Suppose, for the sake of contradiction, that the graph contains a cycle. Let v1, v2, ..., vn be the vertices of the cycle in order. Since the graph is connected, there must be a path from v1 to vn that does not include any of the vertices v2, ..., vn-1 (otherwise, we could "short-circuit" the cycle). But this path, together with the edges (v1, v2) and (vn-1, vn), forms a cycle, contradicting our assumption that the graph is acyclic. Therefore, the graph is acyclic.

Since the graph is both connected and acyclic, it must be a tree. Therefore, a graph with vertices and edges that satisfies the given conditions must be a tree.

No, the graph does not necessarily have to be a tree.

The formula v = e + 1 signifies that one more than the number of edges (e) is the number of vertices (v).

This formula is valid for all connected graphs with one extra edge, besides the necessary edges for forming a tree.

Thus, it is possible that the diagram remains with loops and more offshoots, classifying it as a graph that is connected, yet not a tree.

The equation v = e + 1 does not limit the graph's structure beyond its connectedness.

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Let X = number of red Skittles in a fun size Skittles package. Then X is a random variable, which means its value is uncertain and subject to chance.

The possible values of X range from 0 (if there are no red Skittles in the package) to the total number of Skittles in the package (if all Skittles are red). The probability distribution of X describes the likelihood of each possible value occurring.

The terms you've mentioned relate to the number of red Skittles in a fun size package. In this context, X represents a discrete random variable, as it can take on a finite number of distinct values (i.e., the possible counts of red Skittles in each package).

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The z-score for P(? ≤ z ≤ ?) = 0.60 is approximately 0.25.

The z-score for P(z ≥ ?) = 0.30 is approximately -0.52.

P(Z ≤ z) = 0.60

We can use a standard normal distribution table or a calculator to find that the z-score corresponding to a cumulative probability of 0.60 is approximately 0.25.

Therefore, the z-score for P(? ≤ z ≤ ?) = 0.60 is approximately 0.25.

For the second question:

We want to find the z-score such that the area under the standard normal distribution curve to the right of z is 0.30. In other words:

P(Z ≥ z) = 0.30

Using a standard normal distribution table or calculator, we can find that the z-score corresponding to a cumulative probability of 0.30 is approximately -0.52 (since we want the area to the right of z, we take the negative of the z-score).

Therefore, the z-score for P(z ≥ ?) = 0.30 is approximately -0.52.

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Yes, we can say that the shape of the sampling distribution for the sample mean will be approximately normal, even if the population distribution is not normal, due to the Central Limit Theorem (CLT).

According to the CLT, as the sample size increases, the sampling distribution of the mean tends to become normal regardless of the shape of the population distribution, as long as the sample size is sufficiently large (typically, n ≥ 30). In this case, the sample size is 14, which is less than 30, but the normality assumption can still be used as an approximation due to the fact that the population distribution is already normal.

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The "best-example" which shows the "safety-issue" related to large "manufacturing-business" is (b) broken machinery and flammable chemical containers are left sitting in "parking-lot".

The leaving of broken machinery and flammable chemical containers in the "parking-lot" poses a dangerous "safety-hazard" to both the employees and the surrounding community.

The broken machinery can cause accidents, while the flammable chemical containers can potentially leak or catch fire, leading to explosions and other dangerous situations. Therefore, it is crucial for the business to properly dispose of such hazardous waste and maintain a safe working environment.

Therefore, the correct option is (b).

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The given question is incomplete, the complete question is

Which example best shows a safety issue related to a large manufacturing business?

(a) The machines at the business are found to waste energy and cause excess costs.

(b) Broken machinery and flammable chemical containers are left sitting in the parking lot.

(c) Poverty has forced the neighborhood surrounding a business to become deserted.

(d) The flu season has caused a high rate of absent employees at the business.

There is not sufficient evidence to conclude that the average calorie content of chocolate chip cookies is greater than 110 calories at the 0.01 level of significance.

The null hypothesis is a type of hypothesis that explains the population parameter and is used to examine if the provided experimental data are reliable.

We will use a one-sample t-test to test the hypothesis.

Null hypothesis: The mean calorie content of chocolate chip cookies is 110 calories.

Alternative hypothesis: The mean calorie content of chocolate chip cookies is greater than 110 calories.

We will use a significance level of α = 0.01.

We first calculate the sample mean and sample standard deviation:

[tex]$\bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i = \frac{1780}{15} = 118.67$[/tex]

[tex]$s = \sqrt{\frac{1}{n-1} \sum_{i=1}^{n} (x_i - \bar{x})^2} = \sqrt{\frac{1}{14} \sum_{i=1}^{n} (x_i - 118.67)^2} = 26.899$[/tex]

The test statistic is calculated as:

[tex]$t = \frac{\bar{x} - \mu_0}{s/\sqrt{n}} = \frac{118.67 - 110}{26.899/\sqrt{15}} = 1.84$[/tex]

Using a t-distribution table with 14 degrees of freedom and a one-tailed test at the 0.01 level, the critical value is 2.977.

Since the test statistic (1.84) is less than the critical value (2.977), we fail to reject the null hypothesis. Therefore, there is not sufficient evidence to conclude that the average calorie content of chocolate chip cookies is greater than 110 calories at the 0.01 level of significance.

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Population growth rate is determined by the difference between the birth rate and the death rate, and the net migration rate.

This statement is incorrect. The rate of growth of a population cannot be calculated by simply measuring and subtracting a rate. Population growth rate is determined by the difference between the birth rate and the death rate, and the net migration rate.

To calculate the population growth rate, you need to use the following formula:

Population Growth Rate = (Birth Rate - Death Rate) + Net Migration Rate

The birth rate is the number of live births per 1000 people in a given population in a given year, and the death rate is the number of deaths per 1000 people in the same population and year. The net migration rate is the difference between the number of people immigrating to a country and the number of people emigrating from the same country.

By plugging in the values for the birth rate, death rate, and net migration rate, you can calculate the population growth rate.

A positive value indicates a population increase, while a negative value indicates a population decrease.

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Answer:

based on the statements, here are the ones I would select as true

Step-by-step explanation:

You cannot compare the value of rational and irrational numbers, The closer together the numbers being compared, the more decimal places you need to use

The value of x that makes the equation true is x = 16/9.

To solve for x in the equation 27=13(6x−9)+3x, we first simplify the right-hand side by distributing the 13:

27 = 78x - 117 + 3x

Then, we can combine like terms by adding 117 to both sides:

144 = 81x

Finally, we isolate x by dividing both sides by 81:

x = 144/81 = 16/9

This means that if we substitute x = 16/9 into the original equation, both sides will be equal.

In the context of the problem, this value of x represents the solution that satisfies the equation. It indicates that the given equation is consistent and has a unique solution for x.

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False.

When training a data model, the training dataset is generally larger than the testing dataset. This is because the model needs more data for learning and understanding patterns. A common ratio is to use 70-80% of the data for training and 20-30% for testing.

The training dataset helps the model learn and make predictions, while the testing dataset is used to evaluate the model's performance. Using a larger training dataset improves the model's accuracy and reduces overfitting, which occurs when the model is too specific to the training data.

In summary, it is false that the testing dataset is always larger than the training dataset when training a data model. It is crucial to have a larger training dataset to ensure the model learns effectively and performs well on unseen data.

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Using permutation, there are 8 ways to arrange the letters of "millimicron" in order.

What is permutation?

A permutation is a way of arranging objects in a specific order. In mathematics, a permutation of a set is a bijection from the set to itself, where a bijection is a function that maps each element of the set to a unique element of the same set and vice versa.

To find the number of distinguishable ways the letters of the word "millimicron" can be arranged in order, we can use the method of example 9.5.11.

First, we count the number of times each letter appears in the word:

There are 2 "m"s

There are 2 "i"s

There are 2 "l"s

There is 1 "c"

There is 1 "r"

There is 1 "o"

There is 1 "n"

Then, we compute the number of permutations for each group of letters with the same count. The number of permutations of n items, where there are k items of one type, l items of another type, and so on, is given by:

n! / (k! l! ...)

Using this formula, we get:

There are 2! = 2 permutations of the "m"s

There are 2! = 2 permutations of the "i"s

There are 2! = 2 permutations of the "l"s

There is 1! = 1 permutation of the "c"

There is 1! = 1 permutation of the "r"

There is 1! = 1 permutation of the "o"

There is 1! = 1 permutation of the "n"

Therefore, the total number of distinguishable ways the letters of the word "millimicron" can be arranged in order is:

(2!)(2!)(2!)(1!)(1!)(1!)(1!) = 8

So there are 8 ways to arrange the letters of "millimicron" in order.

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We can expect to observe 14 complete interference fringes within the central diffraction peak.

The act of bending light around corners such that it spreads out and illuminates regions where a shadow is anticipated is known as diffraction of light. In general, since both occur simultaneously, it is challenging to distinguish between diffraction and interference.

The number of complete interference fringes within the central diffraction peak depends on the specific experimental setup, such as the distance between the slits and the screen, the distance between the slits, and the wavelength of the light being used.

However, we can use the following equation to calculate the number of interference fringes within the central diffraction peak:

N = (2d/a)(L/λ)

where N is the number of interference fringes, d is the distance between the slits, a is the width of the slits, L is the distance between the slits and the screen, and λ is the wavelength of the light being used.

Assuming that we have a double-slit setup and the distance between the slits and the screen is much larger than the slit width, we can simplify the equation to:

N = 2d/a

Given that d/a = 7, we can substitute this value into the equation to get:

N = 2(7) = 14

Therefore, we can expect to observe 14 complete interference fringes within the central diffraction peak.

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Answer:

approximately 200.96 square inches.

Step-by-step explanation:

The area of a circle is given by the formula A = πr^2, where r is the radius of the circle. Since the diameter of the pizza is 16 inches, the radius is half of the diameter, or 8 inches. Thus, the area of the top of the pizza is:

A = π(8)^2

A = 64π

Therefore, the area of the top of the pizza is 64π square inches. If you want a numerical approximation, you can use 3.14 as an approximation for π and calculate:

A ≈ 64(3.14)

A ≈ 200.96

So the area of the top of the pizza is approximately 200.96 square inches.

Answer:

about 201 sq.inches

Step-by-step explanation:

have a good day :)

The type of measurement, the nature of the comparison, and the number of groups to be compared influence the statistical choice is true.

The choice of statistical test depends on several factors, including the type of measurement (nominal, ordinal, interval, or ratio), the nature of the comparison (independent or dependent), and the number of groups being compared.

For example, different statistical tests are used for comparing means between two groups, three or more groups, or paired observations within the same group. It is important to select the appropriate statistical test that matches the research question and the data being analyzed to ensure accurate and valid results.

Hence, The type of measurement, the nature of the comparison, and the number of groups to be compared influence the statistical choice is true.

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c. The statement "The constant variance condition can be somewhat relaxed when the sample sizes are relatively consistent across groups" is generally false in ANOVA.

In ANOVA, the constant variance assumption, also known as homogeneity of variance, states that the population variances of the dependent variable are equal across all levels of the independent variable. Violation of this assumption can lead to biased or inefficient estimates of the treatment effects. While there are some methods that can be used to address violations of this assumption, such as using robust standard errors or transforming the dependent variable, relaxing this assumption based on sample sizes is generally not recommended.

d. The statement "The independence assumption can be relaxed when the total sample size is large" is false in ANOVA.

The independence assumption in ANOVA states that the observations within each group or level of the independent variable are independent of each other. This assumption is important because violations of independence can lead to increased Type I error rates or inaccurate estimates of the treatment effects. The total sample size, while important for the power of the analysis, does not affect the independence assumption. Each observation should still be independent of all other observations within each group, regardless of the total sample size.

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The derivative of a function f(x) at a point x = a is defined as the limit of the difference quotient as h approaches zero:

f'(a) = lim (h → 0) [f(a + h) - f(a)] / h

What is derivative?

The derivative of a function in calculus measures the function's sensitivity to changes in its input variable. Specifically, at a particular point, it represents the function's rate of change with respect to its input variable at that moment.

The derivative of a function f(x) at a point x = a is defined as the limit of the difference quotient as h approaches zero:

f'(a) = lim (h → 0) [f(a + h) - f(a)] / h

This limit represents the instantaneous rate of change or slope of the function at the point x = a. The difference quotient is the change in the function value divided by the change in the input variable (or the distance between two points on the graph of the function).

The derivative is a fundamental concept in calculus, and it has many applications in various fields of science, engineering, and economics. It allows us to calculate important quantities such as velocity, acceleration, and marginal cost, and it is used to optimize functions and solve many real-world problems.

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The equations are solved to w = 0 and z = -2. Option B

From the information given, we have the simultaneous equations as

3w-4z=8

2w+3z=-6

Make w the subject from equation 1

w = 8 + 4z/3

Substitute the value into equation 2, we have that;

2(8 + 4z/3) + 3z = -6

expand the bracket, we get;

16 + 8z/3 + 3z = -6

find the LCM, we have;

16 + 8z + 9z/3 = -6

cross multiply

16 + 17z = - 18

add the values

19z = -34

Make 'z' the subject

z =-2

Substitute the value

3w - 4z = 8

3w = 8 - 8

w = 0

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Answer:

210 bicycles

Step-by-step explanation:

If Jared has assembled 25% of the bicycles, then the remaining percentage of bicycles that need to be assembled is:

100% - 25% = 75%

To find out how many bicycles still need to be assembled, we can multiply the total number of bicycles by the remaining percentage:

Number of bicycles still to be assembled = 75% of 280 bicycles

Number of bicycles still to be assembled = (75/100) x 280

Number of bicycles still to be assembled = 0.75 x 280

Number of bicycles still to be assembled = 210

Therefore, Jared still needs to assemble 210 bicycles.