we use a bode plot to show filter characteristics because the use of logarithms creates plots that can be approximated with straight lines. true false

To calculate the probability of obtaining 2 defective watches in a sample of 3 from a lot of 30 watches with a 20% defective rate, we can use the binomial probability formula.

The binomial probability formula is given by:

P(X = k) = (n C k) * p^k * (1 - p)^(n - k)

Where:

P(X = k) is the probability of getting exactly k successes (in this case, 2 defectives),

n is the sample size (3),

k is the number of successes (2),

p is the probability of success (defective rate, 20% or 0.2), and

(1 - p) is the probability of failure (1 - 0.2 = 0.8).

Using these values, we can calculate the probability as follows:

P(X = 2) = (3 C 2) * (0.2)^2 * (0.8)^(3 - 2)

(3 C 2) represents the number of ways to choose 2 out of 3 watches, which is calculated as 3! / (2! * (3 - 2)!), which simplifies to 3.

P(X = 2) = 3 * (0.2)^2 * (0.8)^(3 - 2)

      = 3 * 0.04 * 0.8

      = 0.096

Therefore, the probability that a sample of 3 watches from the lot of 30 watches contains 2 defectives is 0.096 or 9.6%.

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The probability that a company ships by truck or rail is 89%.

To calculate the probability that a company ships by truck or rail, we need to add the probability of shipping by truck to the probability of shipping by rail, and then subtract the probability of shipping by both truck and rail (to avoid double counting):

P(shipping by truck or rail) = P(shipping by truck) + P(shipping by rail) - P(shipping by both truck and rail)

We are given that:

P(shipping by truck) = 82%

P(shipping by rail) = 47%

P(shipping by both truck and rail) = 40%

Plugging in these values, we get:

P(shipping by truck or rail) = 82% + 47% - 40%

= 89%

Therefore, the probability that a company ships by truck or rail is 89%.

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A change in the unit of measurement of the dependent variable in a model does not lead to a change in the goodness-of-fit of the regression. The correct option is c.

The goodness-of-fit, often measured by the coefficient of determination (R-squared), reflects the proportion of variance in the dependent variable that can be explained by the independent variable(s) in the model. This measure is unitless and ranges from 0 to 1, where a value closer to 1 indicates a better fit.

When the unit of measurement of the dependent variable changes, the standard error of the regression (option a) may change since it represents the dispersion of the observed values around the predicted values in the model. Similarly, the sum of squared residuals of the regression (option b) might also change, as it is the sum of the squared differences between the observed and predicted values.

Additionally, the confidence intervals of the regression (option d) may be affected by a change in the unit of measurement, as these intervals provide a range within which the true population parameters are likely to fall, and the range is dependent on the scale of the dependent variable.

In summary, while a change in the unit of measurement of the dependent variable can affect the standard error, sum of squared residuals, and confidence intervals of the regression, it does not impact c. the goodness-of-fit of the regression model.

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When the spinner is spun, the P ( vowel, then P ) would be B. one-ninth.

To find the probability of P ( vowel, then P ), on the spinner given, the formula would be :

= P ( vowel ) x  P ( P )

We have six sides on the spinner which gives us:

P ( vowel ) = 4 / 6

P ( P ) = 1 / 6

The probability of P ( vowel, then P ) is :

= 4 / 6 x 1 / 6

= 4 / 36

= 1 / 9

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It is important to base design decisions on objective criteria such as user research, usability testing, and data analysis, rather than subjective biases or unfounded beliefs about numbers.

Not supported by any empirical evidence or logical reasoning. There is no inherent power or superiority associated with even numbers over odd numbers in the context of presenting or designing a product or message.

While some people may have personal preferences or cultural associations with even or odd numbers, the power or effectiveness of a product or message is determined by various factors such as its content, design, target audience, and context of use.

Therefore, it is important to base design decisions on objective criteria such as user research, usability testing, and data analysis, rather than subjective biases or unfounded beliefs about numbers.

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Complete the tree diagram as shown in the image attached.

The probability that he prepares chapattis and tea is 0.3

Since He prepares either samosas or chapattis for the food and the probability that he prepares samosas is 0.4.

Thus, the probability that he prepares chapattis is: 1 - 0.4 = 0.6

Also, He prepares either tea or coffee for the drink and He is equally likely to prepare tea or coffee. Thus,

The probability that the prepare tea  = 0.5

The probability that the prepare coffee = 0.5

Therefore, we can complete the tree diagram as shown in the image attached.

The probability that he prepares chapattis and tea is:

= P(chapattis) * P(tea)

= 0.6 * 0.5

= 0.3

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It will take Jill and John [tex]$\frac{6}{5}$[/tex] hours or 1 hour and 12 minutes.

To solve the problem, we can use the formula:

[tex]$ \frac{1}{x} = \frac{1}{a} + \frac{1}{b}$[/tex]

where x is the time it takes for Jill and John to complete the job working together, and a and b are the times it takes for Jill and John to complete the job individually, respectively.

Substituting the given values, we get:

[tex]$ \frac{1}{x} = \frac{1}{2} + \frac{1}{3}$[/tex]

Simplifying this expression, we get:

[tex]$ \frac{1}{x} = \frac{5}{6}$[/tex]

Multiplying both sides by 6x, we get:

[tex]$6 = 5x$[/tex]

Dividing both sides by 5, we get:

[tex]$x = \frac{6}{5} hours$[/tex]

Therefore, it will take Jill and John [tex]$\frac{6}{5}$[/tex] hours or 1 hour and 12 minutes (rounded to the nearest minute) working together to complete the job.

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The probability that John is late for work is 0.26, or 26%.

To calculate the probability that John is late for work, we need to use the law of total probability, which states that the probability of an event can be found by considering all possible ways that the event can occur.

Let B be the event that John takes the bus, and S be the event that he takes the subway. Let L be the event that he is late for work.

We know that P(B) = 0.3, P(S) = 0.7, P(L|B) = 0.4, and P(L|S) = 0.2. We want to find P(L), the probability that John is late for work.

Using the law of total probability, we have:

P(L) = P(L|B)P(B) + P(L|S)P(S)

= 0.4 x 0.3 + 0.2 x 0.7

= 0.12 + 0.14

= 0.26

Therefore, the probability that John is late for work is 0.26, or 26%.

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The percentage of utility companies that earned revenue less than 3939 million or more than 6161 million dollars is 0.1484 or 14.84% (rounded to the nearest hundredth).

We can use the standard normal distribution to find the percentage of utility companies that earned revenue less than 3939 million or more than 6161 million dollars.

First, we need to standardize the values using the formula:

z = (x - μ) / σ

where x is the revenue value, μ is the mean revenue, and σ is the standard deviation.

For x = 3939 million:

z = (3939 - 5050) / 77 = -1.45

For x = 6161 million:

z = (6161 - 5050) / 77 = 1.44

Using a standard normal distribution table or calculator, we can find the probability of a value being less than -1.45 or greater than 1.44.

P(z < -1.45) = 0.0735

P(z > 1.44) = 0.0749

The percentage of utility companies that earned revenue less than 3939 million or more than 6161 million dollars is:

0.0735 + 0.0749 = 0.1484 or 14.84% (rounded to the nearest hundredth).

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Instead of performing repeated two-mean comparisons, we conduct an analysis of variance to compare means because doing so reduces the likelihood of Type I errors. Here option A is the correct answer.

ANOVA is preferred over multiple two-mean comparisons because it reduces the Type I error probability and offers several benefits over conducting multiple t-tests.

When conducting multiple two-mean comparisons, the likelihood of making a Type I error (i.e., rejecting a true null hypothesis) increases with each comparison made. This is known as the multiple comparison problem, and it can lead to an inflated false positive rate.

In contrast, ANOVA uses a single test to compare multiple means simultaneously, reducing the likelihood of making a Type I error. ANOVA achieves this by partitioning the total variation in the data into different sources of variation and estimating the amount of variation due to random chance.

Additionally, ANOVA offers several benefits over multiple t-tests. One advantage is that it provides a statistical significance test for the overall difference among the groups, not just for pairwise comparisons. ANOVA also allows for the testing of interactions between factors that influence the mean differences. Furthermore, ANOVA calculations can be more efficient than conducting multiple t-tests, especially when the number of groups is large.

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Complete question:

The reason why we perform an analysis of variance for comparing means rather than conducting multiple two-mean comparisons is

A. Because multiple two-mean comparisons increase the Type I error probability.

B. Simply because ANOVA has simpler calculations but otherwise offers no benefit over the multiple t-tests.

C. Power is compromised when using t-tests.

We can add the two integrals to get the total distance traveled:

≈ 1,262.63 meters (rounded to two decimal.

To find the total distance traveled by the referee, we need to integrate the absolute value of the velocity function over the given time interval.

distance = ∫[tex][2,6] |v(t)| dt[/tex]

The absolute value of the velocity function is given by:

[tex]|v(t)| = |4t^6 cos(2t+5)|[/tex]

So, we have:

[tex]distance = ∫[2,6] |4t^6 cos(2t+5)| dt[/tex]

We can simplify this integral by using the fact that the absolute value of a product is the product of the absolute values:

[tex]distance = ∫[2,6] 4t^6 |cos(2t+5)| dt[/tex]

Next, we split the integral into two parts, depending on whether the argument of the cosine function is positive or negative:

distance = [tex]∫[2,6] 4t^6 cos(2t+5) dt + ∫[2,6] 4t^6 cos(-2t-5) dt[/tex]

Simplifying the second integral using the identity cos(-x) = cos(x), we get:

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Therefore, The smallest set of integers guaranteed to have a difference that is a multiple of 14 is 15. This is due to the Pigeonhole Principle and the 14 possible remainders when dividing integers by 14.

The smallest set of integers for which we are guaranteed there exist two whose difference is a multiple of 14 is 15. This can be explained by considering the possible remainders when dividing integers by 14. There are 14 possible remainders (0 to 13), but if we choose 15 integers, then by the Pigeonhole Principle, at least two of them must have the same remainder when divided by 14. The difference between these two integers will be a multiple of 14, as their remainders are the same. Therefore, the smallest set of integers required is 15.
Therefore, The smallest set of integers guaranteed to have a difference that is a multiple of 14 is 15. This is due to the Pigeonhole Principle and the 14 possible remainders when dividing integers by 14.

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Answer:

I'd say probably 6, as 3 + 3 = 6, and 3 is the middle number.

**EDIT**

It's a 7, because there are 6 ways of rolling this value, making it the most probable.

Any  solution to this equation is in the span of W. Conversely, any linear combination of v1 and v2 will satisfy this equation, so the solution set of this equation is exactly the span of W.

To find a set of homogeneous equations whose solution set is W, we need to find a set of equations that are satisfied by all linear combinations of the vectors in the span of W.

Let's call the vectors in the span of W, v1 and v2:

v1 = [\begin{array}{ccc}2\\1\\-3\end{array}\right]
v2 = [\begin{array}{ccc}1\\-1\\2\end{array}\right]

We want to find a set of homogeneous equations that are satisfied by all linear combinations of v1 and v2. We can do this by setting up an augmented matrix with v1 and v2 as its columns, and then row reducing the matrix to find a set of homogeneous equations.

[ v1 | v2 ] =
[\begin{array}{ccc|ccc}2 & 1 & 0 & 1 & 0 & 0\\1 & -1 & 0 & 0 & 1 & 0\\-3 & 2 & 0 & 0 & 0 & 1\end{array}\right]

Using elementary row operations, we can row reduce this matrix to reduced row echelon form:

[\begin{array}{ccc|ccc}1 & 0 & 0 & -5 & 3 & 0\\0 & 1 & 0 & -1 & 2 & 0\\0 & 0 & 0 & 0 & 0 & 1\end{array}\right]

The last row of the row reduced matrix corresponds to the equation 0x1 + 0x2 + 0x3 = 1, which is always false and has no solutions. Therefore, we can ignore this row and write down the remaining equations in terms of x1, x2, and x3:

x1 - 5x3 + 3x3 = 0
x2 - x3 + 2x3 = 0

Simplifying these equations, we get:

x1 - 2x2 + 4x3 = 0

So the set of homogeneous equations that correspond to W is:

x1 - 2x2 + 4x3 = 0

Therefore, any solution to this equation is in the span of W. Conversely, any linear combination of v1 and v2 will satisfy this equation, so the solution set of this equation is exactly the span of W.

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We can see that g(x) is less than or equal to h(x) for all x greater than or equal to 8, because sin^2(x) is always less than or equal to 1. Therefore, g(x) is also divergent, since h(x) is divergent.

To determine whether the integral g(x)=∫[infinity]8sin2(x) 4x−−√ 2dx diverges, we can compare it to the integral f(x)=∫ [infinity]83x−−√ 2dx. We know that f(x) is a divergent integral because the power of x in the denominator is greater than 1.

To compare g(x) to f(x), we need to find a function h(x) that is greater than or equal to g(x) and less than or equal to f(x) for all x greater than or equal to 8. One function that satisfies this condition is h(x) = √x.

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Set theory is a branch of mathematics that deals with the study of sets, which are collections of objects. A subset is a set that contains only elements that belong to a larger set, called the universal set. In this context, let S, T, X, and Y be subsets of some universal set.

Assuming that S [T X [Y, we can conclude that if a belongs to T, then a belongs to S or a belongs to X or a belongs to Y. This is because T is a subset of S [T X [Y, and any element in T must belong to at least one of these sets.

Assuming that S \T D, we can conclude that if a belongs to T, then a does not belong to S. This is because S \T is the set of elements that belong to S but not to T, and D is the empty set, meaning that there are no elements in the set. Therefore, if a belongs to T, it cannot belong to S \T, and so it must not belong to S.

Assuming that X S, we can use all three assumptions to prove that T Y. Suppose that a belongs to T. Then, using assumption (i), we know that a belongs to S or a belongs to X or a belongs to Y. But since a cannot belong to S (using assumption (ii)), we must have either a belongs to X or a belongs to Y. But since X is a subset of S (using assumption (iii)), we know that if a belongs to X, then a belongs to S. Therefore, we must have a belongs to Y. This holds for any element a in T, so we can conclude that T Y.

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So the probability of rolling pairs of the same number is 6/36, which simplifies to 1/6 or approximately 0.1667.

When Lydia tosses two six-sided number cubes, the sample space consists of all possible outcomes for each roll. There are 6 sides on each cube, resulting in 6 x 6 = 36 possible outcomes.

For pairs of the same number, there are 6 possible outcomes: (1,1), (2,2), (3,3), (4,4), (5,5), and (6,6).

To find the probability of Lydia rolling pairs of the same number, divide the number of favorable outcomes (pairs of the same number) by the total number of outcomes in the sample space:

Probability = (number of favorable outcomes) / (total number of outcomes) = 6 / 36 = 1/6 or approximately 16.67% or 0.1667.

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The function f(x) is continuous for all x ≠ 0 but is not continuous at x = 0.

The function f: R → R is defined by f(x) = sin(1/x) if x ≠ 0 and f(x) = 0 if x = 0. To determine whether f is continuous, we need to examine its behavior at x = 0 and for all x ≠ 0.

For x ≠ 0, the function f(x) = sin(1/x) is a composition of two continuous functions, sin(u) and u = 1/x. Since the composition of continuous functions is continuous, f(x) is continuous for x ≠ 0.

At x = 0, we need to check the limit of f(x) as x approaches 0. Let's examine the limit from both the left and right sides:

lim(x→0-) sin(1/x) and lim(x→0+) sin(1/x)

As x approaches 0 from either side, 1/x becomes increasingly large in magnitude, causing the sine function to oscillate rapidly between -1 and 1. There is no unique value that the function approaches. Therefore, the limit does not exist:

lim(x→0) sin(1/x) does not exist.

Since the limit does not exist at x = 0, the function f(x) is not continuous at x = 0.

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To find out how high the truck can safely be to drive underneath the bridge underpass in the shape of an elliptical arch, we need to use the dimensions given. First, we need to determine the equation of the ellipse, which is:

(x^2/a^2) + (y^2/b^2) = 1

Where "a" is the width of the ellipse (in feet) and "b" is half the height of the ellipse (in feet). Therefore, we can substitute the given values to get:

(x^2/(feet)^2) + (y^2/(feet/2)^2) = 1

Simplifying this equation, we get:

(x^2/(feet)^2) + 4(y^2/(feet)^2) = 1

Now, we know that the truck is 8 feet wide, so we need to find the height "y" that allows the truck to safely drive underneath. To do this, we need to find the maximum value of "y" that satisfies the equation above and also allows for a clearance of at least 8 feet (the width of the truck). We can set up an inequality as follows:

4(y^2/(feet)^2) <= 1 - (8/(2*feet))^2

Simplifying this inequality, we get:

4(y^2/(feet)^2) <= 1 - 16/(feet)^2

y^2 <= (1/4)(feet)^2 - 4

y <= sqrt((1/4)(feet)^2 - 4)

Therefore, the maximum height "y" that allows the truck to safely drive underneath is:

y <= sqrt((1/4)(feet)^2 - 4)

Note that we cannot simplify this expression further without knowing the value of "feet". So, the final answer will depend on the specific dimensions of the bridge underpass.

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From the calculation, the population density of the place is 800 people/[tex]mile^2[/tex].

We know that we can be able to obtain the population density by the use of the formula;

Population density = Population of people/ Area of the place;

The area is obtained from;

Length = 30 miles

Width = 2/3 * 30 = 20 miles

Area of the city = 30 miles * 20 miles = 600 [tex]miles^2[/tex]

Final population after the evacuation = 555,000 - 75,000

= 480,000

Then the population density = 480,000/600 [tex]miles^2[/tex]

= 800 people/[tex]miles^2[/tex]

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Since the obtained Chi square (23.45) is greater than the critical Chi square (9.488), we can conclude that there is a significant relationship between marital status and church attendance.

A Chi square test is used to determine whether there is a significant association between two categorical variables. In this case, the variables are marital status and church attendance.

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The probability of making a Type I error is equal to 1 - 0.99 = 0.01, which is the same as the significance level.

To answer this question, we need to understand the concepts of statistical power and Type I error. Statistical power refers to the probability of correctly rejecting the null hypothesis when it is false. In other words, it is the ability of a statistical test to detect a true effect.

On the other hand, a Type I error occurs when we reject the null hypothesis even though it is true. This is also known as a false positive.

The investigator has indicated that the power of his test is 0.87 at a significance level of 1%. This means that if there is a true effect in the population, the test has an 87% chance of correctly detecting it. However, we are interested in the probability of making a Type I error, which is the probability of rejecting the null hypothesis when it is true.

To calculate the probability of making a Type I error, we need to use the complement of the significance level, which is 1 - 0.01 = 0.99. This represents the probability of not rejecting the null hypothesis when it is true. Therefore, the probability of making a Type I error is equal to 1 - 0.99 = 0.01, which is the same as the significance level.

In this case, the investigator has used a significance level of 1%, which means that there is a 1% chance of making a Type I error. This is a relatively low probability, which indicates that the investigator is being cautious in rejecting the null hypothesis.

However, it is important to note that the probability of making a Type I error depends on the significance level, the sample size, and the effect size. Therefore, it is important to consider these factors when interpreting the results of a statistical test.

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95% confidence that the true mean of the violent crime rate in Seattle's census tracts lies between 20.43 and 33.69 per 1,000.

In the given study, Rountree and Warner (1999) observed a mean official violent crime rate of 27.06 per 1,000 in 100 census tracts over a two-year period in Seattle, WA. The standard deviation was 33.81. To construct a 95% confidence interval around this mean, we can use the formula:

[tex]CI = mean ± (Z-score * (standard deviation / √sample size))[/tex]

For a 95% confidence interval, the Z-score is 1.96. The sample size is 100 census tracts. So, we can calculate the interval as follows:

CI = 27.06 ± (1.96 * (33.81 / √100))

CI = 27.06 ± (1.96 * (33.81 / 10))

CI = 27.06 ± (1.96 * 3.381)

CI = 27.06 ± 6.63

The lower bound for this confidence interval is 27.06 - 6.63, which is approximately 20.43. Therefore, we can say with 95% confidence that the true mean of the violent crime rate in Seattle's census tracts lies between 20.43 and 33.69 per 1,000.

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A company produces two types of smartphones: Model X and Model Y. The profit from each Model X sold is $150, while the profit from each Model Y sold is $200. Last month,the company sold a total of 500 smartphones and made a total profit of $85,000.

Here's a word problem that uses a system of linear equations:

Sarah and John went to a coffee shop and ordered lattes and scones. Sarah ordered 2 lattes and 3 scones and paid $10. John ordered 3 lattes and 2 scones and paid $11.50. What is the cost of one latte and one scone?

Let x be the cost of one latte and y be the cost of one scone. We can set up a system of linear equations to solve for x and y:

2x + 3y = 10

3x + 2y = 11.5

Now, your partner can solve this system of equations using any method they prefer, such as substitution or elimination.

To solve the system of equations:

2x + 3y = 10

3x + 2y = 11.5

We can use elimination method, by multiplying the first equation by 2 and the second equation by -3 to eliminate y:

4x + 6y = 20

-9x - 6y = -34.5

Adding the two equations, we get:

-5x = -14.5

Dividing both sides by -5, we get:

x = 2.9

Substituting x = 2.9 into the first equation, we can solve for y:

2(2.9) + 3y = 10

5.8 + 3y = 10

3y = 4.2

y = 1.4

Therefore, one latte costs $2.9 and one scone costs $1.4.

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The probability that her trip will take longer than 80 minutes  is 0.20.

The probability that the trip takes longer than 80 minutes is equal to the area of the uniform distribution to the right of 80. Since the distribution is uniform, the area to the right of 80 is equal to the proportion of the total area that is to the right of 80.

The total area under the uniform distribution is equal to the length of the interval, which is (90 - 40) = 50.

The area to the right of 80 is equal to (90 - 80) = 10.

So the probability that the trip takes longer than 80 minutes is:

10 / 50 = 0.2

Therefore, the answer is 0.20.

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When a line segment is drawn from a point on a circle's circumference, through the center to another point on the circle's circumference, the distance of the line segment is equal to the diameter of the circle.

The diameter is defined as any straight line passing through the center of a circle, connecting two points on the circumference. It is the longest chord of the circle and is also the length of a circle's widest point.

The diameter is an important parameter of a circle, as it determines the circle's size and area. It is related to the circle's radius, which is half the length of the diameter. The diameter is also used in various formulas for calculating the circumference, area, and other properties of circles.

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The probability of getting heads up is 5/6, or approximately 0.833.

There are three coins in the box, so the probability of picking any one of them at random is 1/3.

Let's consider the probability of getting heads up for each of the three coins:

For the two regular coins, the probability of getting heads up is 1/2, since they are fair coins.

For the fake two-headed coin, the probability of getting heads up is 1, since both sides are heads.

So, the overall probability of getting heads up depends on which coin is selected.

If you select one of the regular coins, the probability of getting heads up is 1/2.

If you select the fake two-headed coin, the probability of getting heads up is 1.

The probability of selecting one of the regular coins is 2/3, since there are two regular coins out of the total of three coins.

Therefore, the overall probability of getting heads up when you pick a coin at random and toss it is:

(2/3) x (1/2) + (1/3) x (1) = 2/6 + 3/6 = 5/6.  

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The hole size required for the meter to be bolted to the switchboard is 1.7565 inches in diameter, expressed in decimal form.

To find the hole size for the meter to be bolted to the switchboard, you need to add the given diameter difference of 1.32 inches to the diameter of the meter studs. The meter studs are 0.4365 inches in diameter. So, the calculation is as follows:

Hole size = Meter stud diameter + Diameter difference
Hole size = 0.4365 inches + 1.32 inches
Hole size = 1.7565 inches

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The decision regarding the hypothesis that the training was effective in improving customer relationships would depend on the results of the hypothesis test and the associated p-value.

To determine the decision regarding the hypothesis that the training was effective in improving customer relationships, a hypothesis test would need to be conducted. The null hypothesis, denoted as H0, would be that the training had no effect on improving customer relationships. The alternative hypothesis, denoted as Ha, would be that the training was effective in improving customer relationships.

Assuming a 0.05 significance level, if the p-value associated with the hypothesis test is less than or equal to 0.05, then the null hypothesis can be rejected in favor of the alternative hypothesis. This would indicate that there is evidence to suggest that the training was effective in improving customer relationships.

On the other hand, if the p-value is greater than 0.05, then the null hypothesis cannot be rejected. This would indicate that there is insufficient evidence to suggest that the training was effective in improving customer relationships.

Therefore, the decision regarding the hypothesis that the training was effective in improving customer relationships would depend on the results of the hypothesis test and the associated p-value.

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If 1(1/4) pound of turkey is sold for $9, then the unit-price of the turkey is $7.20 per pound.

The "Unit-Price" is defined as the price of a single unit or item of a product, typically expressed in terms of a standard unit of measurement, such as price per pound, price per liter, or price per piece.

To find the unit price of turkey in the package, we need to divide the total cost of the package by the weight of the turkey in the package.

First, we need to convert 1(1/4) pounds to a decimal, which is 1.25 pounds.

Then, we can find the unit price by dividing the total-cost of $9 by the weight of the turkey in the package:

⇒ Unit price = (Total cost)/(Weight of turkey in package),

⇒ Unit price = $9/1.25 pounds,

⇒ Unit price = $7.20 per pound,

Therefore, the unit price of turkey in the package is $7.20 per pound.

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The given question is incomplete, the complete question is

A store sells a 1(1/4) pound package of turkey for $9. ​What is the unit price of the turkey in the package?