Which of the following equations is also true?

Answer:

The answer to your question is 61.8 in²

Step-by-step explanation:

Process

1.- Calculate the height of the triangle

    3² = h² + 1.5²

    h² = 3² - 1.5²

    h² = 9 - 2.25

    h² = 6.75

    h = 2.6 in

2.- Calculate the area of the triangles

     Area = [tex]\frac{base x height}{2}[/tex]

     Area = [tex]\frac{3 x 2.6}{2}[/tex]

     Area = 3.9 in²

    There are 2 triangles = 2(3.9) = 7.8 in²

3.- Calculate the area of the rectangles

     Area = base x height

     Area = 6 x 3 = 18 in²

     There are 3 rectangles = 3(18) = 54 in²

4.- Total area

     At = 7.8 + 54

         = 61.8 in²

Answer:

(104 +16√13) ft

Step-by-step explanation:

To assess if there's a linear relationship between the Gesell score and the age at first word, one would create a scatterplot with the Gesell score as (y) and age as (x). The presence of a linear trend could be indicated by a clustering of points near a line, while the strength of the relationship would be further analyzed using the least-squares regression line and correlation coefficient.

To determine whether there is a linear relationship between the Gesell adaptive score test (Gesell score) and the age at which children speak their first word, you would start by plotting a scatterplot with the Gesell score as the response variable (y) and the age at first word as the explanatory variable (x). In the scatterplot, each point represents one child's data with their corresponding age at first word on the x-axis and Gesell score on the y-axis.

After plotting the data, you would examine the scatterplot to see if the points suggest a linear trend. If the points cluster around a line that slopes upwards or downwards, this could indicate a positive or negative linear relationship respectively. Conversely, if the points are widely scattered without any discernible pattern, it might suggest that there is no significant relationship between the variables.

If there seems to be a potential linear relationship, you might proceed to calculate the least-squares regression line to find the best-fitting line through the data and the correlation coefficient to measure the strength and direction of the relationship between the variables. Significant correlation coefficients (typically those near -1 or 1) would support the presence of a linear relationship, while coefficients near zero would suggest little to no linear relationship.

Final answer:

To analyze the relationship between the Gesell adaptive score test and the age of first word spoken by children, one would plot a scatterplot with Gesell score on the y-axis and age at first word on the x-axis. This visualization aids in identifying any potential linear or non-linear patterns.

Explanation:

To investigate if there is a relationship between the Gesell adaptive score test and the age at which a child speaks their first word, we would create a scatterplot with the age at first word (in months) as the x-axis (explanatory variable) and the Gesell score as the y-axis (response variable). By analyzing the pattern of the dots on the scatterplot, we could determine if there is any apparent linear relationship or if the data suggests a more complex relationship, such as an inverted U-shaped relationship.

It's important to note that an absence of linear correlation from a statistical test doesn't necessarily deny the existence of any relationship between two measures; the relationship could be non-linear or might change over time. An example of this would be a change in problem-solving strategies in children causing an inverted U-shaped relationship in cognitive ability over different stages. Therefore, a scatterplot is a crucial tool for visually identifying patterns and potential relationships in data.

Answer:

2.94 rads

Step-by-step explanation:

The number of radians that Jose sweeps out equals to the ratio of the chord length, which is the distance he travels in km, to the radius of the circular track, which is 0.9 km

= 2.65 / 0.9 = 2.94 rads

So Jose sweeps an angle of 2.94 radians

Final answer:

Using the formula for arc length in a circle, Jose sweeps out approximately 2.944 radians when he travels 2.65 km along a circular ski trail with a radius of 0.9 km.

Explanation:

The student is asking about how to convert a distance traveled along a circular path into radian measure. Specifically, Jose has skied 2.65 kilometers around a circular ski trail with a radius of 0.9 kilometers. To calculate the number of radians swept out by Jose, we can use the formula s = rθ, where s is the arc length (distance traveled), r is the radius, and θ is the angle in radians.

To find the angle θ, we rearrange the formula to θ = s / r. Using the given values, we get θ = 2.65 km / 0.9 km, which simplifies to approximately 2.944 radians.

So, Jose sweeps out roughly 2.944 radians on his ski trip along the circular trail.

Answer:

a. of the central limit theorem.

Step-by-step explanation:

The Central Limit Theorem estabilishes that, for a random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], a large sample size, larger than 30, can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]\frac{\sigma}{\sqrt{n}}[/tex]

In this problem, the sample size is 100, so it is sufficiently large to use the Central Limit Theorem. The mean of the sample in 69 and the standard deviation of the sample is 0.58.

So the correct answer is:

a. of the central limit theorem.

Answer: g(-x) = 2x^2 +5x + 5

Step-by-step explanation:

This Is a topic under functions in mathematics, it deals with substituting "x" for the new entity that has been placed in the new function you're asked to look for.

Hence, to solve this, we simply substitute "-x" for "x" wherever "x" appears in the equation.

By doing so, we go straight to the given function.

2x^2 -5x + 5

And we go ahead with our substitution which goes thus:

2(-x^2) -5(-x) + 5

On simplifying, we then finally have have:

2x^2 +5x + 5.

This means:

g(-x) = 2x^2 +5x + 5

Answer:

Max 0.09B+0.2s  

 B>=0.6 Bond fund minimum

 0.06B+0.2S>=0.085 Minimum Return

 B+S=1 All funds invested

 B,S>=0

Step-by-step explanation:

(a) In linear programming,  the  mathematical model and the linear objective function set of linear constraints the variables are not negative.

B=% funds invested in the bond fund

S=% of funds invested in the stock fund

Max 0.09B+0.2s  

 B>=0.6 Bond fund minimum

 0.06B+0.2S>=0.085 Minimum Return

 B+S=1 All funds invested

 B,S>=0

Solve the above by using the graphical solution procedure?

steps to solve the graphs

1)draw the graphs, making sure the constraints are consider

2)consider all the constraints

3)draw the objective function line to the decision variables to

4) place the parallel lines of objective function towards larger objective function

5)  consider as optimal function  the feasible solution on the objective function line with the largest value ia

Answer:

1) x-12

Step-by-step explanation:

(4x-6)-(3x+6)

4x-6-3x-6

x-12

Answer:

Step-by-step explanation:

The collection of current NHL players cant be defined as some may be on injury so therefore the current NHL players is ill-defined thereby they are described as Not well defined set

Final answer:

A collection of current NHL players is a well-defined set. We can precisely determine what is and is not a member of this set.

Explanation:

A collection of current NHL players is a well-defined set. A set is a collection of distinct objects, and in this case, the objects are the current NHL players. It is possible to precisely determine what is and is not a member of this set. We can provide a clear and concise definition of the set by listing the names of all current NHL players.

For example:

Auston Matthews

Connor McDavid

Alex Ovechkin

Sidney Crosby

Nathan MacKinnon

These are just a few examples, but by listing all the current NHL players, we can determine exactly what is included in the set and what is not.

Data is collected from a sample, which is a smaller subset of the population.

The correct answer is A. sample. When data is collected, it is usually infeasible or impractical to collect information from an entire population. Therefore, a sample, which is a smaller subset of the population, is selected for data collection and analysis. For example, if we want to know the average height of students in a school, it would be time-consuming and costly to measure the height of every student. Instead, we can select a sample of students from different grades or classrooms to represent the entire population and collect data from them.

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Answer:

c = 10.000cm

Step-by-step explanation:

If the 2 boards made the right angle, c would be the hypotenuse with 2 sides of 6 cm and 8 cm. We can then use Pythagorean formula to solve for c

[tex]c^2 = 6^2 + 8^2 = 36 + 64 = 100[/tex]

[tex]c = \sqrt{100} = 10.000 cm[/tex]

Answer:

The answer is a) less than 5% of the time

Step-by-step explanation:

By definition, the level of significance is the probability that one rejects a null hypothesis of a test when it is true. Denoted as alpha α, it shows how convincing the sample data is and concludes if it is statistically significant.

A level of significaance of 5% or 0.05, shows that you need more convincing power before determining whether you willreject the null hypothesis

By way of rule,

-If p-value ≤ level of significance α , then reject the null hypothesis.

-If p-value ≥ level of significance α , then do not reject the null hypothesis.

This implies that a statistician with a 5% level of significance will reject the null hypothesis if her hypothesized value falls below this mark.

Answer:

c) 95% of the time or more.

Step-by-step explanation:

The level of significance in a statistical research is the probability of rejecting the null hypothesis when it is actually true. A possible example is a significance level of 0.02 which indicates a 2% risk of concluding that a difference exists when there is no actual difference. The lower the significance level, the stronger the evidence required before you can reject the null hypothesis. 5% significance level may imply 95% confidence interval which means the range of values that occurs 95% of the time.

Therefore, rejecting the null hypothesis at 5% significance level means that the alternative hypothesis differs from the null hypothesis by 95% or more of its possible values (95% of the time or more).

The question involves computing parameters of a joint probability density function given a specific function and ranges. The process involves setting up and evaluating appropriate double integrals over the given ranges.

The subject of this problem is related to joint probability density functions (pdfs) and probability theory which comes under mathematics, specifically statistics.

(a) To find the value of k that makes this a valid probability density function, we use the property that the integral of a pdf over its range should equal 1:

(b) To find P(Y1 ≤ 3/4, Y2 ≥ 1/2), you integrate the joint pdf over the given intervals:

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a) A student in your class has a cat, a dog and a Ferret is Ex(C(x)∧D(x)∧F(x)).

b) All students in your class have a cat, a dog or a Ferret is ∀x(C(x)∨D(x)∨F(x)).

c) Some student in you class has cat and Ferret but not a dog is Ex(C(x)∧D(x)∨¬F(x)).

The various types of logical connectives include conjunction (“and”), disjunction (“or”), negation (“not”), conditional (“if . . . then”), and biconditional (“if and only if”).

Let C(x) : x has a cat

D(x) : x has a dog

F(x) : x has a Ferret

a) A student in your class has a cat, a dog and a Ferret.

Ex(C(x)∧D(x)∧F(x))

b) All students in your class have a cat, a dog or a Ferret.

∀x(C(x)∨D(x)∨F(x))

c) Some student in you class has cat and Ferret but not a dog

Ex(C(x)∧D(x)∨¬F(x))

Therefore,

a) A student in your class has a cat, a dog and a Ferret is Ex(C(x)∧D(x)∧F(x)).

b) All students in your class have a cat, a dog or a Ferret is ∀x(C(x)∨D(x)∨F(x)).

c) Some student in you class has cat and Ferret but not a dog is Ex(C(x)∧D(x)∨¬F(x)).

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The statements can be expressed using logical connectives and quantifiers in mathematical logic. They express different combinations of students owning cats, dogs, and ferrets.

The statements can be expressed in terms of C(x), D(x), F(x), quantifiers, and logical connectives as follows:

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The combined transformation operation r1 r2 f1 r3 f2 f3 r3 is a reflection. This is because a sequence of transformations combining rotations and reflections, when arranged and executed in order, still produces a reflection.

The problem is understanding whether the composition of certain transformations, namely rotations (r1, r2, r3) and reflections (f1, f2, f3) from Dn, results in another rotation or a reflection. In mathematics, a composition of transformations means applying several transformations in sequence.

Here, think about the properties of rotations and reflections. A rotation followed by another rotation will result in a rotation. Similarly, a reflection followed by another reflection is equivalent to a rotation. However, a reflection followed by a rotation or vice versa results in a reflection.

The sequence provided is r1 r2 f1 r3 f2 f3 r3. Assuming each transformation in the sequence is executed in order, we can see that it is divided into four segments: rr, rf, fr, and r. The first segment, rr, will result in a rotation. The second segment, rf, will result in a reflection. That reflection followed by the next r, resulting in another reflection. Finally, the last segment is just a rotation. Therefore, reflection followed by a rotation results in a reflection.

So, the combined transformation r1 r2 f1 r3 f2 f3 r3 is a reflection.

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Answer:

the probability is P=0.012 (1.2%)

Step-by-step explanation:

for the random variable X= weight of checked-in luggage, then if X is approximately normal . then the random variable X₂ = weight of N checked-in luggage = ∑ Xi  , distributes normally according to the central limit theorem.

Its expected value will be:

μ₂ = ∑ E(Xi) = N*E(Xi) = 121 seats * 68 lbs/seat = 8228 lbs

for N= 121 seats and E(Xi) = 68 lbs/person* 1 person/seat = 68 lbs/seat

the variance will be

σ₂² = ∑ σ² (Xi)= N*σ²(Xi) → σ₂ = σ *√N = 11 lbs/seat *√121 seats = 121 Lbs

then the standard random variable Z

Z= (X₂- μ₂)/σ₂ =

Zlimit= (8500 Lbs - 8228 lbs)/121 Lbs = 2.248

P(Z > 2.248) = 1- P(Z ≤ 2.248) = 1 - 0.988 = 0.012

P(Z > 2.248)= 0.012

then the probability that on a randomly selected full flight, the checked-in luggage capacity will be exceeded is P(Z > 2.248)= 0.012 (1.2%)

Answer:

Ö + θ ( (k/m) + (g/l)) = 0

Step-by-step explanation:

Use the FBD attached:

Apply Newtons 2 nd Law in tangential direction:

Sum ( Ft ) = m*a

Sum of all tangential forces is:

m*g*sin(θ) + k*l*sin(θ)*cos(θ) = - m*l*Ö

Using small angle approximations:

sin (θ) = θ

cos (θ) = 1

Ö = angular acceleration.

m*g*θ + k*l*θ = -m*l*Ö

Ö + θ ( (k/m) + (g/l)) = 0

Answer:

See explanation below.

Explanation:

If the statement is a tautology is true for all the possible combinations and we can check this with the table of truth for the statements

Part a

[tex] (p \land q) \Rightarrow (p \lor r)[/tex] lets call this condition (1)

[tex] (p \land q)[/tex] condition (2) and [tex](p \lor r)[/tex]  condition (3)

We can create a table like this one:

p       q     r      (2)       (3)     (1)  

T       T     T      T        T       T

T       T     F      T        T       T        

T       F     T      F        T       T

T       F     F      F        T       T

F       T     T      F        T       T

F       T     F      F        F       T

F       F     T      F        T       T

F       F     F      F        F       T

So as we can see we have a tautology since for all the possibilites we got true the final result.

Part b

[tex] p \Rightarrow (r \Rightarrow p)[/tex] let's call this condition (1)

And let [tex] (r \Rightarrow p)[/tex] condition (2)

We can create the following table:

p     r       (2)     (1)

T     T       T       T

T     F       T       T

F     T       F       T

F     F       T       T

So is also a tautology since the statement is true for all the possibilities or combinations.

Answer:

Step-by-step explanation:

Hello!

To see if there is an association between the variables:

"Level of satisfaction of an individual"

"Risk of diabetes of an individual"

The researchers studied 1621 people over 5 years.

Observations recorded:

Interviews of daily lives and hassles and hypothetical situations that were studied to assess their emotions.

Determination, if in the course of these 5 years the individuals experienced any type of diabetes.

Conclusion "Satisfied individuals are less likely to have diabetes"

a) This is a prospective cohort study.

In this type of study, a group of individuals that share the same characteristics is observed over some time, recording the events of interest.

b) Considering that the experiment concluded that "satisfaction" reduces the "risk of diabetes", we can determine that the response variable is "Risk of diabetes of an individual" and the explanatory variable is "Level of satisfaction of an individual".

Remember, the explanatory variable is the one considered to have a direct effect over the response variable.

c) "the research team also hasn't ruled out that a common factor like genetics could be causing both the emotions and the lung cancer."

There is a new variable that may affect the experiment. Be "genetic factor" the new variable and it affects directly "emotions and lung cancer", we can say that if some of those individuals are genetically predisposed to have lung cancer, this affects their emotions (satisfaction) and therefore, modifying their risk of having diabetes.

If this is so, then the genetic factor could be a lurking variable affecting directly the result of the observational experiment. Then the correct answer is:

A. The researchers may be concerned with confounding that occurs when the effects of two or more explanatory variables are not separated or when some explanatory variables were not considered in a study, but that affects the value of the response variable.

I hope it helps!

Final answer:

This response explains the type of observational study conducted, identifies the response and explanatory variables, and clarifies the researchers' concern about confounding due to genetics.

Explanation:

(a) Type of observational study: This study is an observational study because the researchers are observing and analyzing individuals to determine a relationship between satisfaction levels and the risk of diabetes without intervening or manipulating variables.

(b) Response and explanatory variables: The response variable in this study is the occurrence of diabetes, while the explanatory variable is the level of satisfaction of the individuals.

(c) Explanation of sentence: The sentence suggests that a common factor like genetics could be influencing both the emotions and the risk of diabetes, indicating that the researchers are considering the possibility of confounding where unaccounted variables may affect the relationship observed.The statement indicates concern about confounding variables, where genetics might influence both satisfaction and diabetes risk, suggesting they have not separated the effects of these intertwined factors fully. (Option A)

Answer:

Part A:  vertex at (-1,4)

Part B:  line of symmetry x =-1

Part C:  x-intercept x = -3 and x = 1

Part D:  y-intercept y = 3

Step-by-step explanation:

Given f(x) = - (x+1)² + 4

The given equation represents a parabola.

The general equation of the parabola with a vertex at (h,k)

f(x) =  (x-h)² + k

Part A: To find the vertex, compare the general equation with the given function.

So, the vertex of the function will be at (-1,4)

Part B: the line for the axis of symmetry of function will be at x =-1

Part C: To find x-intercept, put y = 0

So, - (x+1)² + 4 = 0

- (x+1)² = -4

(x+1)² = 4

x + 1 = ±√4 = ±2

x + 1 = 2  OR   x + 1 = -2

x = 1  OR x =-3

x-intercept at x = 1 and x = -3

Part D:To find y-intercept, put x = 0

So, y = - (0+1)² + 4 = -1 + 4 = 3

y-intercept at  y = 3

See the attached figure.

Answer:

Vertex at (-1,4)

Line of symmetry x =-1

x-intercept x = -3 and x = 1

y-intercept y = 3

Answer Choices:

Answer:

Observational

The study design of using cameras to monitor an intersection and count cars during green light intervals is considered an "C. Observational" study, as it involves systematic data collection without experimental manipulation.

The study design described, where cameras are set up to watch an intersection and determine how many cars are allowed through with each green light interval, would be considered an example of a "C. Observational" study design.

Observational studies involve the systematic collection and analysis of data without manipulating any variables. In this case, researchers are merely observing and recording the number of cars passing through the intersection when the traffic light is green. They are not actively intervening, controlling variables, or conducting experiments. Instead, they are passively gathering information from the real-world scenario without any interference.

This type of observational study can provide valuable insights into traffic patterns, efficiency, and safety at the intersection without introducing external biases that might occur in experimental designs. It allows researchers to collect data in a naturalistic setting, making it suitable for studying real-world phenomena where experimentation might be impractical or unethical, such as traffic flow analysis.

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Que. Cameras are set up to watch an intersection and determine how many cars are let through with each green light interval. This study design would be considered:

A. Simulation

B. Survey

C. Observational

D. Experimental

There is only 1 two-headed coin in the collection of 65 coins.

The probability of selecting the two headed coin is 1/65.

The outcome achieved when any of the other coins is tossed a number of times is based purely on chance.

Although if the 2-headed coin is selected, the only possible outcome is having a head, but  It also possible to have 6 heads in 6 tosses with a coin that is not 2-headed.

What we're concerned with, is the probability that the  2-headed coin was selected from the lot of 65 coins, which is  1/65.

Answer:

There is only 1 two-headed coin in the collection of 65 coins.

The probability of selecting the two-headed coin is 1/65.

The outcome achieved when any of the other coins is tossed a number of times is based purely on chance.

Although if the 2-headed coin is selected, the only possible outcome is having a head, but  It is also possible to have 6 heads in 6 tosses with a coin that is not 2-headed.

What we're concerned with, is the probability that the  2-headed coin was selected from the lot of 65 coins, which is  1/65.

Step-by-step explanation:

Answer:

Stratified

Step-by-step explanation:

The stratified sampling is defined as the type of random sampling when the population is divided into non-overlapping groups known as strata and then sample is selected from each of the stratum.

The sampling technique used is stratified sampling because the population is divided into 3 strata math, music and history and then sample of 35,52 and 38 is selected from these 3 strata respectively.

Answer:

  a) y = -19/44t +62; y = 5/11t +29

  b) 2007

Step-by-step explanation:

(a) The two-point form of the equation of a line is useful when two data points are given.

  y = (y2 -y1)/(x2 -x1)(x -x1) +y1

Middle Income

The two given points are (0, 62), (44, 43), so the equation is ...

  y = (43 -62)/(44 -0)(t -0) +62

  y = -19/44t +62

__

Upper Income

The two given points are (0, 29), (44, 49), so the equation is ...

  y = (49 -29)/(44 -0)(t -0) +29

  y = 20/44t +29

  y = 5/11t +29

__

(b) The year in which the shares are equal is found by setting the y-values equal:

  -19/44t +62 = 5/11t +29

  33 = 39/44t

  t = (33)(44)/39 ≈ 37.2

The upper income share exceeded the middle income share in the year 1970 +37.2 = 2007.

The mathematical models for the middle-income and upper-income households are y1(t) = 62% - 0.38% * t and y2(t) = 29% + 0.40% * t, respectively. The aggregate income held by upper-income households first exceeded that held by middle-income households in the year 2014.

In order to find the mathematical model, we will first determine the slope for each data set. The slope is calculated as the change in y divided by the change in x. Here, 'y' represents the share of aggregate income and 'x' represents time.

For the middle-income households, the slope (m1) is: m1 = (43% - 62%)/(2014 - 1970) = -0.38% per year. So, the equation becomes: y1(t) = 62% - 0.38% * t.

For the upper-income households, the slope (m2) is: m2 = (49% - 29%)/(2014 - 1970) = 0.40% per year. So, the equation becomes: y2(t) = 29% + 0.40% * t.

To find out when the aggregate income held by the upper-income households first exceeded that held by middle-income households, we set the two equations equal to each other and solve for 't'. This gives us t = (62% - 29%)/(0.40% + 0.38%) = 43.7, or roughly 44 years after 1970 which is the year 2014.

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Answer:

Some other information should be given, I have added those necessary information, should in case you come across a similar question on line integral.

Step-by-step explanation:

To evaluate line integral, some other information has to be given, Assume (so when you see a similar question, you will know how to evaluate line integral) as part of the question X =t2, Y = 2t and Z = 3t and at interval 0≤t≤1

A step by step explanation and calculation has been attached.

Answer:

Step-by-step explanation:

The relationship between predictor and response variables in scatterplots can be analyzed in terms of direction (positive or negative), form (linear or non-linear), and strength. Each plot is described based on this analysis.

To determine the relationship between the predictor and response variables in each of the provided scatterplots, we need to analyze the form, direction, and strength of the scatterplots.

For plot (1), if the points are following a downward path but not a straight line, we would classify this as Negative, non-linear.

For plot (2), if the points are following an upward path but not a straight line, we would classify this as Positive, non-linear.

For plot (3), if the points are increasing in a straight line, we would classify this as Positive, linear.

For plot (4), if the points are decreasing in a straight line, we would classify this as Negative, linear. However, if the points seem to be randomly scattered with no discernible pattern, then we would classify this as No association.

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Answer:

The average height of poodles in your kennel is 25.19 inches.

Step-by-step explanation:

This is a weighed average problem.

To find the weighed average, we sum each value of the set multiplied by it's weight, and then we divide by the sum of the weights.

In this problem, we have that:

Average 20.5 inches has weight 28.

Average 32.1 inches has weight 19.

What is the average height of poodles in your kennel?

[tex]A = \frac{32.1*19 + 20.5*28}{19+28} = \frac{1183.9}{47} = 25.19[/tex]

The average height of poodles in your kennel is 25.19 inches.

To start with, in finding the height of the poodle, we consider both groups. We then add the multiplication of the number of dogs by the average, and finally, we divide them all by the number dogs. Mathematically, i am saying that;

Finally,

[tex]\frac{574+609.9}{47}[/tex] = 25.19

Therefore, the average height of the dogs in the kennel is 25.19 inches

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Answer:

Step-by-step explanation:

The Pythagorean theorem is expressed as

Hypotenuse² = opposite side² + adjacent side²

If the distances of the routes given are Pythagorean triples, then they obey the Pythagorean theorem hence, they would form a right angle triangle.

1) for the bus routes between stop A, B and C,

13² = 12² + 5²

169 = 144 + 25 = 169

A Pythagorean triple is formed hence, stop A, B and C form a right angle triangle.

2) for the bus routes between stop C, E and E,

22² = 14² + 18²

484 = 196 + 324 = 520

A Pythagorean triple is not formed hence, stop C, E and E do not form a right angle triangle.

3) 25² = 9² + HJ²

625 = 81 + HJ²

HJ² = 625 - 81 = 544

HJ = √544 = 23.32

4) EG² = 15² + 8²

EG² = 225 + 64

EG² = 289

EG = √289 = 17

Answer:

B. Sample error.

Step-by-step explanation:

Such type of error is called Sample error.

Sample error occurs when

Sample error is a statistical error when an expert fails to choose a sample which symbolizes the whole data population and the outcomes of the sample do not reflect the outcomes from the whole population.

If a population mean is 300 and the sample mean is 400, the difference of 100 is called sampling error. So, option b is correct.

Sampling error represents the difference between the population mean and the sample mean, which arises purely by chance because a sample rather than the entire population is observed.

The other options are incorrect in this context:

Answer: 9.9248

Step-by-step explanation:

We know that the critical t- value for confidence interval is a two-tailed value from the t-distribution table corresponding to degree of freedom (df = n-1 , where n is the sample size) and the significance level ([tex]\alpha/2[/tex]) .

The given confidence interval = 99%

⇒ Significance level = [tex]\alpha=100\%-99\%=1\%=0.01[/tex]

Sample size : n=3

DEgree of freedom : df = n-1 = 2

Then, the  critical t- value for 99% confidence interval will be;

[tex]t_{\alpha/2, df}=t_{0.01/2,\ 2}[/tex]

[tex]t_{0.005 , 2}=\pm9.9248[/tex]   [From t-distribution table]

Hence, the appropriate percentile from a​ t-distribution for constructing the 99​% confidence interval with n = 3 is 9.9248.

Answer:

A pitcher can hold only 2 cup in his hands