the length of a rectangular garden is 9 feet longer than its width. the garden's perimeter is 182 feet. find the length of the garden.

The person is walking away from the streetlight at a rate of 0.16 meters per second.

We can use similar triangles to solve this problem. Let the length of the person's shadow be x meters. Then, we have the following ratio:

(person's height)/(length of person's shadow) = (distance from person to streetlight)/(height of streetlight)

or

2/x = d/8

where d is the distance from the person to the streetlight.

Now, let's take the derivative of both sides with respect to time t:

(2/x^2) (dx/dt) = (1/8) (dd/dt)

where dx/dt is the rate at which the person's shadow is lengthening, and dd/dt is the rate at which the person is moving away from the streetlight.

Solving for dd/dt, we get:

dd/dt = (2/x^2) (dx/dt) (8)

Substituting x = 10 (since the person's shadow is lengthening at a rate of 1 meter per second, it will be 10 meters long after 10 seconds), and dx/dt = 1, we get:

dd/dt = (2/100) (1) (8) = 0.16 meters per second

Therefore, the person is walking away from the streetlight at a rate of 0.16 meters per second.

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(L3) A segment that extends from the vertex of a triangle to the opposite side and is perpendicular to the side is called the incenter of the triangle.

The incenter of a triangle is an important point that can be constructed by drawing a perpendicular line from each vertex of the triangle to the opposite side, and then finding the intersection of these lines. This intersection point is equidistant from the three sides of the triangle, and is therefore the center of the circle that can be inscribed within the triangle.

The incenter also has the property that it is the point of concurrency of the angle bisectors of the triangle, meaning that it is equidistant from the three angles of the triangle as well. The incenter is used in a variety of geometric constructions and proofs, including the construction of the inscribed circle and the solution of problems involving the ratio of the sides of a triangle.

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If the null hypothesis is rejected, then you can conclude that there is a difference in means based on the provided data. If not, then there isn't enough evidence to support a difference in means.

if there is a difference in means between the number of points held by a sample of NHL's highest scorers in the Eastern and Western conferences. To answer this, you'll need to follow these steps:

1. Identify the data: You need to have the points data for the NHL's highest scorers from both the Eastern and Western conferences.

2. Calculate the means: Calculate the average number of points for both conferences' samples.

3. Determine the variables: Since you've mentioned that the variables are normally distributed and have unequal variances, we can use the independent two-sample t-test with unequal variances (Welch's t-test) to determine if there is a significant difference in means.

4. Perform Welch's t-test: Using the means, variances, and sample sizes of both groups, calculate the t-value and degrees of freedom.

5. Compare the t-value to the critical t-value: Determine the critical t-value at a specified significance level (commonly α = 0.05) using the degrees of freedom. If the calculated t-value is greater than the critical t-value, you can reject the null hypothesis and conclude that there is a significant difference in means.

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The box plot of the data is illustrated below.

To construct a box plot, we first need to find the five-number summary of the data set, which includes the minimum value, the first quartile (Q1), the median, the third quartile (Q3), and the maximum value. The median is the middle value of the data set, while Q1 and Q3 represent the values that separate the lower 25% and upper 25% of the data, respectively.

Using the provided data sets for the ages of the best actors and best actresses, we can compute the five-number summary for each group.

For the actors, the minimum age is 29, the maximum age is 64, and the median age is 42. The first quartile (Q1) is 38, and the third quartile (Q3) is 50.

For the actresses, the minimum age is 21, the maximum age is 80, and the median age is 35. The first quartile (Q1) is 29, and the third quartile (Q3) is 39.

Using this information, we can construct a box plot for each group on the same scale to compare their ages. The box plot for the actors will have a box extending from Q1 to Q3, with a line inside representing the median age. Whiskers will extend from the box to the minimum and maximum ages, and any values beyond the whiskers will be considered outliers.

Similarly, the box plot for the actresses will have a box extending from Q1 to Q3, with a line inside representing the median age. Whiskers will extend from the box to the minimum and maximum ages, and any values beyond the whiskers will be considered outliers.

By comparing the two box plots, we can see that the range of ages for the actresses is wider than that for the actors, as indicated by the longer whiskers. The median age for the actresses is lower than that for the actors, while the interquartile range (IQR) is narrower for the actresses, indicating less variability in their ages.

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Complete Question:

Use the same scale to construct boxplots for the ages of the best actors and best actresses from the accompanying data sets.

Actors Age Data

43 39 44 45 38 50 29 33

50 37 42 50 42 38 53 44

50 42 36 33 64 42 31 38

Actresses Age Data

21 33 36 35 39 53 31 29

31 33 24 37 39 42 25 26

33 38 37 35 32 80 25 43

Sure, the term you are referring to is called a sequence diagram. It is a graphical representation of a use-case that shows the interactions among different classes or objects along a time axis. Sequence diagrams are useful for visualizing the flow of messages or events between different parts of a system, and can be used to identify potential issues or bottlenecks in the system's design.

They are commonly used in software development to help teams understand and communicate complex interactions between different components of a system.
 In this context, the Sequence Diagram captures the behavior and communication between classes or objects, allowing developers to visualize the flow of control and understand the system's functionality more effectively.

this is a dynamic model fo a use-case showing the interaction among classes along a time axis

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i) 46,860 ways  to choose president, vice president and treasurer

ii) 21,420 ways to choose president, vice president and treasurer if man must bechosen as a president

iii) 6,460 ways to choose president, vice president and treasurer if woman must bechosen as a president and vice president while man must be chosen as a treasurer.

i) To choose the president, vice president and treasurer from a total of 37 members, we can use the permutation formula,

P(37,3) = 37 × 36  ×35 = 46,860.

Therefore, there are 46,860 ways to choose the three positions.

ii) If a man must be chosen as president, we have 17 options to choose the president from the male members. The remaining two positions can be filled by any of the 36 members. so we have

P(36,2) = 36 × 35 = 1,260

ways to fill those positions. Therefore, there are

17 × 1,260 = 21,420

ways to choose the three positions with a man as president.

iii) If a woman must be chosen as the president and vice president, we have 20 options to choose the president from the female members and 19 options to choose the vice president from the remaining female members. For the position of treasurer, we have 17 options to choose from the male members. Therefore, we have

20 × 19 × 17 = 6,460

ways to choose the three positions with a woman as president and vice president and a man as treasurer.

In summary, there are 46,860 ways to choose the three positions without any restrictions, 21,420 ways to choose with a man as president and 6,460 ways to choose with a woman as president and vice president and a man as treasurer.

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To maximize the manufacturer's total weekly profit, we need to find the critical point of the profit function P(x,y).

(a) To find the critical point, we need to find where the partial derivatives of P(x,y) are equal to zero.

Taking the partial derivative of P(x,y) with respect to x and y, we get:
P_x = 560 + 20y - 40x
P_y = 20x - 12y

Setting P_x = 0 and P_y = 0, we get:
560 + 20y - 40x = 0  and  20x - 12y = 0
Solving these equations simultaneously, we get:
x = 7 and y = 35

(b) To classify the critical point, we need to use the D-Test. The D value is:
D = P_xx * P_yy - (P_xy)^2
Substituting the values of P_xx, P_yy, and P_xy, we get:
D = -9600
Since D is negative, we have a saddle point at the critical point.

(c) To maximize the manufacturer's total weekly profit, we need to produce and sell 7,000 units of Product A and 35,000 units of Product B per week.

(d) Plugging x = 7 and y = 35 into the profit function P(x,y), we get:
P(7,35) = 560(7) + 20(7)(35) - 20(7)^2 - 6(35)^2
P(7,35) = $8,050
Therefore, the maximum weekly profit is $8,050.

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The integral on the right-hand side of the given equation depends only on the area of the region R enclosed by C and not on the shape of C or its location in the plane.

We can use Green's theorem to show that the line integral ∫C zd(x) - 2x(d(y)) + 3y(d(z)) depends only on the area of the region enclosed by C and not on the shape of C or its location in the plane.

Green's theorem states that for a simple closed curve C in the xy-plane that encloses a region R, and for a vector field F = P(x, y)i + Q(x, y)j, we have

∫C P(x, y)d(x) + Q(x, y)d(y) = ∫∫R (∂Q/∂x - ∂P/∂y)dA

where dA = dxdy is the area element in the xy-plane.

In our case, the curve C lies in the plane x + y + z = 1. We can rewrite this equation as z = 1 - x - y, so we have a parametric representation of C

r(t) = (x(t), y(t), z(t)) = (t, 1 - t - u, u)

where t and u are parameters that vary along C.

Now, we can calculate the curl of the vector field F = z(x)i - 2x(j) + 3y(k)

∂P/∂y - ∂Q/∂x = -2 - 0 - 1 = -3

Since the curl is a constant (-3) and does not depend on the variables x, y, or z, we can apply Green's theorem to find the line integral of F along C

∫C zd(x) - 2x(d(y)) + 3y(d(z)) = ∫∫R (-3)dA

Therefore, we have shown that the line integral ∫C zd(x) - 2x(d(y)) + 3y(d(z)) depends only on the area of the region enclosed by C and not on the shape of C or its location in the plane.

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The proportion of students who score between 400 and 600 on the SAT-M is approximately 68.27%.

To find the proportion of students who score between 400 and 600 on the SAT-M, we need to use the standard normal distribution.

First, we need to calculate the z-scores for the lower and upper limits of the range. To do this, we use the formula:

z = (x - μ) / σ

where x is the value we're interested in (400 or 600), μ is the mean score for the SAT-M (which we'll assume is 500), and σ is the standard deviation (which we'll assume is 100).

For 400:
z = (400 - 500) / 100
z = -1

For 600:
z = (600 - 500) / 100
z = 1

Next, we use a standard normal distribution table or calculator to find the area under the curve between these two z-scores.

Using a table or calculator, we find that the area to the left of z = -1 is 0.1587 and the area to the left of z = 1 is 0.8413. To find the area between these two z-scores, we subtract the smaller area from the larger area:

0.8413 - 0.1587 = 0.6826

So the proportion of students who score between 400 and 600 on the SAT-M is approximately 0.6826, or 68.26%.
Hi! To find the proportion of students who score between 400 and 600 on the SAT-M (math section), you need to look at the distribution of scores. The SAT-M scores typically follow a normal distribution with a mean (µ) of 500 and a standard deviation (σ) of 100.

To find the proportion of students who score between 400 and 600, we can use the Z-score formula to standardize the scores:

Z = (X - µ) / σ

For 400: Z1 = (400 - 500) / 100 = -1
For 600: Z2 = (600 - 500) / 100 = 1

Now, we need to find the probability between these two Z-scores, which can be represented as P(-1 < Z < 1). You can find this probability using a standard normal distribution table or a calculator with a normal distribution function. The result is approximately 0.6827, or 68.27%.

So, the proportion of students who score between 400 and 600 on the SAT-M is approximately 68.27%.

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The function that models the amount of money remaining in Carl's account is [tex]M(t) = -100t + 500[/tex]..

We will start by finding out how much money Carl will have left after each withdrawal:

After 30 days:

= $500 - $100

= $400

After 60 days:

= $400 - $100

= $300

After 90 days:

$300 - $100

= $200

After 120 days:

= $200 - $100

= $100

After 150 days:

= $100 - $100

= $0

We can see that Carl 5 withdrawals of $100 each over 150 days. So, we can use a linear function to model the amount of money remaining in Carl's account over time which is M(t) = -100t + 500.

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Anna believes there is insufficient evidence to conclude that the capacity of her car's gas tank is not 10 gallons when, in fact, it is not 10 gallons. This is an example of a Type II error.

A Type II error occurs when Anna believes there is insufficient evidence to conclude that the capacity of her car's gas tank is not 10 gallons when, in fact, it is not 10 gallons.

A Type II error occurs when the null hypothesis is not rejected even though it is false (i.e., the alternative hypothesis is true). In this case, the null hypothesis is that Anna believes the capacity of her car's gas tank is 10 gallons. Therefore, a Type II error would occur if Anna believes there is insufficient evidence to conclude that the capacity of her car's gas tank is not 10 gallons when, in fact, it is not 10 gallons. In other words, Anna fails to reject the null hypothesis (that the capacity is 10 gallons) when it is actually false (the capacity is not 10 gallons).

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The correct statement regarding whether the graph represents a function is given as follows:

C. Yes, because it passes the vertical line test.

A graph represents a function if it has no vertically aligned points, that is, each value of x is mapped to only one value of y. Vertically aligned points mean that a value of x is mapped to multiple values of y, that is, a single input is mapped to multiple outputs which disqualify the relation as a function.

For the graph in this problem, no matter which value of x we plot a vertical line, it would cross the graph of the function only once, hence it passes the vertical line test and it is a function.

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The correct statement regarding whether the line is a good fit is given as follows:

A. No, the equation is not a good fit because the sum of the residuals is a large number.

For a data-set, the definition of a residual is that it is the difference of the actual output value by the predicted output value, that is:

Residual = Observed - Predicted.

A line is a good fit for a data-set when the sum of the residuals of the line of fit is close to zero.

The sum of the residuals for this problem is given as follows:

90 - 50 - 100 - 100 + 150 - 50 = -60.

-60 is a number that is far from zero, hence it is considered a large number, and the line is not a good fit.

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The value of q' as a decimal, rounded to the nearest hundredth, is 0.23.

To find the value of q', we need to first understand what it represents. q' is the complement of the proportion of executives who want to strengthen innovation, i.e., the proportion of executives who do not want to strengthen innovation.

Since the survey showed that 23% of executives want to strengthen innovation, we can calculate the proportion of executives who do not want to strengthen innovation as:

q = 1 - 0.23 = 0.77

Therefore, q' can be calculated as:

q' = 1 - q = 1 - 0.77 = 0.23

Rounding to the nearest hundredth, we get:

q' ≈ 0.23

Therefore, the value of q' as a decimal, rounded to the nearest hundredth, is 0.23.

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The length of BP¯ is 25 mm and the length of CP¯ is 31 mm.

The circumcenter is the point where the perpendicular bisectors of a triangle intersect, and it is equidistant from the three vertices of the triangle. The circumcenter can be used to construct the circumcircle, which is a circle passing through all three vertices of the triangle.

Since P is the circumcenter of Δ ABC, it lies on the perpendicular bisectors of all three sides of the triangle. Therefore, DP¯, EP¯, and FP¯ are all radii of the circumcircle, and they all have the same length, say r.

Since DP¯ is a perpendicular bisector of AB, we have AP=BP=r+25.

Similarly, FP¯ is a perpendicular bisector of AC, so we have AP=CP=r+31.

Solving for r in the first equation, we get r=AP-25=25-25=0.

Substituting this value of r into the second equation, we get CP=r+31=0+31=31.

Therefore, the length of BP¯ is 25 mm and the length of CP¯ is 31 mm.

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Answer:

1- red, green

2- blue, green

3- green, blue

Step-by-step explanation:

The underlined options should be your answers.

This question has been solved using R. The output is shown

A.

95% Confidence interval at (x = 68) = (75.28278, 85.11336)

95% Confidence interval at (x = 75) = (83.83844, 90.77724)

95% Confidence interval at (x = 82) = (89.10713, 99.72809)

B.

95% Confidence interval at (x = 68) = (75.28278, 85.11336)

95% Confidence interval at (x = 75) = (83.83844, 90.77724)

95% Confidence interval at (x = 82) = (89.10713, 99.72809)

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The true statements are:

b. The variability of the sampling distribution of the mean ([tex]\overline x[/tex]) is less than the variability of the individual observations (x)

a. A sufficiently large sample size for the central limit theorem is greater than 30.

The CLT states that if a sample is drawn randomly from any population, the distribution of sample means will approach a normal distribution, regardless of the shape of the population distribution, as the sample size increases.

This means that for large enough sample sizes, the mean and standard deviation of the sample mean can be estimated using a normal distribution.

Let's check each option as follows  

Statement c is false because a sample size of 30 or greater is often considered sufficiently large for the central limit theorem.

Statement d is false because the variability of the sampling distribution of the mean decreases as the sample size increases, which is why the central limit theorem is useful.

Therefore,

The true statements are:

b. The variability of the sampling distribution of the mean (X-bar) is less than the variability of the individual observations (X)

a. A sufficiently large sample size for the central limit theorem is greater than 30.

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stimulates controlled lowering of the foot during 1st ankle rocker bottom sole The controlled lowering of the foot during the first ankle rocker is facilitated by the activation of the tibialis anterior muscle.

This muscle is located in the anterior compartment of the leg and is responsible for dorsiflexion of the ankle joint, which means it lifts the foot upwards towards the shin. However, during the first ankle rocker, the tibialis anterior is activated eccentrically, which means it contracts while lengthening, to control the lowering of the foot towards the ground. This eccentric contraction of the tibialis anterior slows down the rate of plantarflexion and helps to maintain a smooth and controlled descent of the foot, allowing for a controlled heel strike and efficient transfer of weight to the forefoot.

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Full Question

stimulates controlled lowering of the foot during 1st ankle rocker______

To determine for what values of x a series absolutely converges or conditionally converges, we need to analyze the given series. However, since you have not provided a specific series, I will explain the concepts in general terms.

Absolute convergence: A series is said to absolutely converge if the series formed by taking the absolute values of its terms converges. In other words, a series ∑a_n absolutely converges if ∑|a_n| converges. To find the values of x for which a series absolutely converges, we can use convergence tests, such as the Ratio Test, Root Test, or Comparison Test, on the absolute values of its terms.

Conditional convergence: A series is said to conditionally converge if it converges but does not absolutely converge. This means that the original series ∑a_n converges, but the series of its absolute values ∑|a_n| does not. The Alternating Series Test is often used to determine conditional convergence, as it is specifically designed for series with alternating signs.

In summary, to find the values of x for which a series absolutely converges or conditionally converges, you must first analyze the given series using appropriate convergence tests. Absolute convergence occurs when the series of absolute values converges, while conditional convergence occurs when the original series converges but the series of absolute values does not.

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PS is the perpendicular bisector of ST, and P is equidistant from X and Y.

To prove that PX=PZ=PY, we need to show that P is the circumcenter of triangle XYZ, where X, Y, and Z are the midpoints of sides RT, ST, and RS, respectively.

First, we will show that P is equidistant from X and Z. Since PX is perpendicular to RT and PZ is perpendicular to RS, we have to show that PR is the perpendicular bisector of RT and RS. From the given information, we know that PR is the angle bisector of angle TRS and PS is the angle bisector of angle RST. Therefore, angle TPS is congruent to angle SPR, and angle SPR is congruent to angle RPS. This means that triangles RPS and TPS are similar, and we can use this similarity to show that PR is the perpendicular bisector of RT and RS. Specifically, we have:

PR/RS = PS/TS (by the angle bisector theorem)

PR/TS = PS/RS (by the same reasoning)

PR/TS = PR/RS (since PS/TS = PR/RS)

TS = RS (by cross-multiplying)

Therefore, PR is the perpendicular bisector of RT and RS, and P is equidistant from X and Z.

Next, we will show that P is also equidistant from X and Y. Since PY is perpendicular to ST, we have to show that PS is the perpendicular bisector of ST. Again using the angle bisector theorem, we have:

PS/RS = PT/RT

PS/TS = PT/RT

TS = RS

Therefore, PS is the perpendicular bisector of ST, and P is equidistant from X and Y.

Since P is equidistant from X, Y, and Z, it must be the circumcenter of triangle XYZ, and therefore PX=PZ=PY.

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Step-by-step explanation:

They are equal .....

   a transveral across two parallel lines creates equal alternate interior angles

Answer:

89

Step-by-step explanation:

The solution to the equation (a - b) / (a + b) = 3/5 in terms of b is a = 4b.

A statement that affirms the equivalence of two expressions joined by the equals symbol "=" is known as an equation.

Your steps are correct, and here's the solution to the equation:

(a - b) / (a + b) = 3/5

To solve for a in terms of b, we cross multiply:

5(a - b) = 3(a + b)

Expanding the brackets, we get:

5a - 5b = 3a + 3b

Simplifying the equation by combining like terms, we get:

5a - 3a = 8b

2a = 8b

Dividing both sides of the equation by 2, we get:

a = 4b

Therefore, the solution to the equation (a - b) / (a + b) = 3/5 in terms of b is a = 4b.

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The complete question is:

The equation given is A = 4b. Please write an appropriate question related to this equation.

The phase described in the question is the Analyze phase of the Six Sigma DMAIC (Define, Measure, Analyze, Improve/Design, Control/Verify) methodology.

During this phase, teams analyze data collected during the Measure phase to develop hypotheses about the causal relationships between inputs and outputs. They narrow down the causation to the vital few factors that are most significant in affecting the output. Statistical analysis is used to validate the hypotheses and assumptions made during the Define and Measure phases.

The goal of the Analyze phase is to identify the root cause of the problem and establish a baseline performance for future comparison. Once the root cause is identified, the team can move on to the Improve/Design phase to develop and implement solutions to address the problem.

Finally, the Control/Verify phase is used to ensure that the improvements made are sustained and that the problem does not recur.

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The regression equation for the given data set is y = -0.51x + 100.12.

To find the regression equation, we need to first calculate the slope and intercept of the regression line. Using the formula for slope (b) and intercept (a) of the regression line, we get:

b = r(Sy/Sx)

a = y-bar - b(x-bar)

where r is the correlation coefficient, Sy and Sx are the standard deviations of y and x respectively, and y-bar and x-bar are the means of y and x respectively.

Using the given data, we can calculate the means, standard deviations, and correlation coefficient. Then, we can substitute these values into the formulas to get the regression equation.

After calculating, we get the equation y = -0.51x + 100.12. This equation represents the line of best fit for the given data, which can be used to predict the value of y for any given value of x.

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The midpoint of the yearly number of unprovoked alligator attacks can be calculated by finding the class interval that contains the median value. In this histogram, the class intervals are not provided, so we cannot determine the exact midpoint.

However, we can estimate the midpoint by visually locating the point where the histogram is balanced or roughly in the middle. Based on the histogram, it appears that the midpoint is around 6-8 attacks per year.
The midpoint of the yearly number of unprovoked alligator attacks in Florida over 33 years can be found by first identifying the highest and lowest number of attacks in the histogram. Once you have those values, add them together and divide by 2 to find the midpoint.

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The quotient of 874 and 23 is 38.

To get the quotient of 874 and 23 using an area model, create a rectangle with an area of 874 and divide it into 23 equal parts.

Each part would depict the value of one of the 23 groups that 874 is divided into. Then count how many of these equal parts fit into the rectangle and this would give the answer to the division problem.

The rectangle can be divided into 23 equal parts horizontally, and then count how many of these parts fit into the rectangle vertically. Start with one part and see how many times we can fit it into the rectangle vertically before reaching a total of 874.

874 ÷ 23 = ( 1 x 23) + ( 7 x 23)

874 ÷ 23 = 23 + 161

874 ÷ 23 = 38

So the quotient of 874 and 23 is 38.

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For a logarithmic equation in Pareto's law for capitalist countries, log y = log b - k log x, the solution of this equation is equal to the y = b x⁻ᵏ or [tex]y = \frac{ b}{x^k}[/tex].

A logarithmic equation is one of equation form that involves the logarithm of an expression containing a variable. We have Pareto's law for capitalist countries defines as the relationship between annual income, x and the number y of individuals whose income exceeds x is written as log y = log b - k log x --(1) , where b and k are positive constants. We have to solve this equation. As we see it is an logarithm equation. Using logarithm properties we can solve it. The equation is log y = log b - k log x

Using substraction property of logarithm,

[tex]log\: m - log\: n = log( \frac{ m}{n})[/tex]

So, [tex]log y- log b= log(\frac{y}{b})[/tex]

=> [tex]log(\frac{ y}{b }) = -k \: log(x) [/tex]

Using the logarithm rule, log( x²) = 2log x

so, [tex]log( \frac{ y}{b }) = log(x^{-k}) [/tex]

Taking anti-logarithm both sides

=> [tex]\frac{y}{b} = x^{-k} [/tex]

=> y = b x⁻ᵏ

=>[tex]y = \frac{ b}{x^k}[/tex].

Hence, required solution is [tex]y = \frac{ b}{x^k}[/tex].

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The answer is true. The goal of a hypothesis test is indeed to demonstrate that the patterns observed in the sample data are not simply due to chance or sampling error, but rather represent real patterns in the population.

Hypothesis testing is a statistical tool used to determine whether a hypothesis about a population parameter is supported by sample data. The hypothesis being tested is called the null hypothesis, which assumes that there is no significant difference or relationship between variables in the population. The alternative hypothesis, on the other hand, suggests that there is a significant difference or relationship.

Through hypothesis testing, we can determine whether the observed differences or relationships in the sample are likely to occur by chance or are actually reflective of the true population. If the p-value (the probability of obtaining a result as extreme as the one observed, assuming the null hypothesis is true) is less than a predetermined level of significance, typically 0.05, we reject the null hypothesis and conclude that the alternative hypothesis is supported by the data.

In summary, the goal of a hypothesis test is to provide evidence that the observed patterns in the sample data are reflective of the true population and not just due to chance or sampling error.

To know more about sampling error visit:

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