The radius of a sphere is 12cm. What is the approximate change in surface area if the radius increases by 0. 01 cm?

Answer:

x is 226.154…

Step-by-step explanation:

1) x-14=y+196

x=y+196+14

x=y+210

2)x=14y

3) 14y=y+210 collect like terms together

14y-y=210

13y=210 divide both sides by 13

y=16.154

4)x=y+210 meaning:

x=16.154+210

=226.154

The three-dimensional shape would make by the two-dimensional figure is cone option (c) cone is correct.

It is defined as a three-dimensional shape in which the base is a circular shape and the diameter of the circle decreases as we move from the circular base to the vertex.

\(\rm V=\pi r^2\dfrac{h}{3}\)

The question is incomplete.

The complete question is in the picture, please refer to the attached picture.

It is given that:

A two-dimensional figure is shown in the picture.

As we know, in the cone the base is a circular shape.

The side view of the cone is a triangle with half curve circular base.

Thus, the three-dimensional shape would make by the two-dimensional figure is cone option (c) cone is correct.

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Answer:

180 degrees

Step-by-step explanation:

Answer:

Two angles are called supplementary when their measures add up to 180 degrees.

Step-by-step explanation:

Hence, any two angles can be supplementary, if their sum equal to 180°.

...

Difference between Complementary and Supplementary Angles.

Complementary Angles: Supplementary Angles:

Sum of two angles is 90° Sum of two angles is 180°

Answer:

Three cookies / or C

Step-by-step explanation:

You have 16 dollars, so you can nly buy one sandwich which leaves 7 dollars.

7/2 is three.5

your answer is 3

Answer:

3 cookies

Step-by-step explanation:

this is the ansa 4 Kailey wants to buy a sandwich and as many oatmeal cookies as she can with $16. A sandwich costs $9 and an oatmeal cookie costs $2. The inequality 2+ +9< 16 represents Kailey's situation, where x is the number of oatmeal cookies. Which is the greatest number of oatmeal cookies Kailey can buy

The accuracy and reliability of measurements can be enhanced, leading to a reduction in uncertainties.

To improve both statistical uncertainty and the "iv" value, which represents the largest propagated uncertainty, there are some simple yet effective measures that can be taken:

Increase Sample Size: For statistical uncertainty, collecting a larger sample size can help reduce random errors and improve the precision of measurements. By increasing the number of data points, the statistical uncertainty, represented by quantities such as standard deviation or standard error, can be reduced. This allows for more reliable and accurate statistical analysis.

Refine Measurement Techniques: Evaluating and refining the measurement techniques can contribute to reducing both statistical and propagated uncertainties. Ensuring proper calibration and using more precise instruments can enhance measurement accuracy and minimize systematic errors. This step involves reviewing measurement procedures, identifying potential sources of error, and implementing improvements to minimize uncertainty.

Implement Quality Control Procedures: Introducing robust quality control procedures can help identify and address measurement uncertainties. Regularly monitoring and verifying the measurement process, including equipment calibration, can ensure consistency and accuracy. Implementing quality control measures provides confidence in the reliability and accuracy of the measurements, thus reducing uncertainties.

Repeat Measurements: Taking multiple measurements and calculating the average can help mitigate the effects of random errors and reduce statistical uncertainty. Repeating measurements under similar conditions and averaging the results can provide a more accurate representation of the true value and reduce the impact of individual measurement errors.

Analyze and Optimize Experimental Design: Analyzing the experimental design and optimizing it can contribute to reducing uncertainties. By carefully planning the experiment, considering factors such as controls, replication, and randomization, potential sources of uncertainty can be minimized. Optimizing the experimental design ensures that the measurements are conducted in the most efficient and accurate manner.

By implementing these steps, it is possible to improve both statistical uncertainty and the largest propagated uncertainty (iv value). These measures focus on refining measurement techniques, increasing sample size, implementing quality control, repeating measurements, and optimizing experimental design. By doing so, the overall accuracy and reliability of measurements can be enhanced, leading to a reduction in uncertainties.

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The following is an assembly program that calculates the value of the given polynomial, assuming signed integers x and y are stored in register r2 and r3, respectively.

\(```.LIST.ALIGN 4    .GLOBAL _start_start:    PUSH {R4, R5, LR}\)
   \(MOV R4, R2        // R4 < - x    MOV R5, #2        // R5 < - 2\)\(MUL R4, R4, R4    // R4 < - x^2    MUL R4, R4, R5    // R4 < - 2x^2    MOV R5, #3        // R5 < - 3\)
  \(ADD R4, R4, R5, LSL #16    // R4 < - 2x^2 + 3x^2    MOV R5, #5        // R5 < - 5    MUL R5, R5, R2    // R5 < - 5x\)
  \(SUB R4, R4, R5, LSL #16    // R4 < - 2x^2 + 3x^2 - 5x    MOV R5, #11       // R5 < - 11    SUB R4, R4, R5, LSL #16    // R4 < - 2x^2 + 3x^2 - 5x - 11\)
  \(MOV R3, R4        // R3 < - y    POP {R4, R5, PC}.END```\)

Explanation: The polynomial is given as

\(`y = 2x^4 + 3x^2 - 5x - 11`.\)

To calculate this polynomial in assembly language, we need to perform the following steps:

Load the value of `x` into a register. We assume that `x` is stored in register `r2`.

Calculate \(`2x^2`\) and add \(`3x^2`\) to it. We first square the value of `x` by multiplying it with itself and then multiply it with `2`. We then add \(`3x^2`\) to this result. We store this result in register `r4`.

Calculate `5x` and subtract it from the result of step 2. We first multiply the value of `x` with `5` and then subtract it from the result of step 2. We store this result in register `r4`.

Subtract `11` from the result of step 3. We subtract `11` from the result of step 3 and store this result in register `r4`.

Load the value of `y` into a register. We assume that `y` is stored in register `r3`.

Return from the subroutine. We pop the registers from the stack and return from the subroutine.

The assembly program that is used to calculate the value of a given polynomial assuming signed integers x and y are stored in registers r2 and r3, respectively. The polynomial given is\(y = 2x4 + 3x² - 5x - 11\). In this assembly program, we load the value of x into a register, calculate 2x^2, add 3x^2 to it, subtract 5x from the result, and subtract 11 from the final result. The value of y is then stored in a register, and we return from the subroutine.

This assembly program is designed for 32-bit ARM architecture, and it can be run on any ARM processor. The program is written in ARM assembly language, which is a low-level programming language used to write programs that run on ARM processors. It is a complex language that requires a deep understanding of the processor architecture and instruction set.

In conclusion, the assembly program presented here can be used to calculate the value of a given polynomial using signed integers x and y stored in registers r2 and r3, respectively. This program can be adapted to calculate other polynomials or perform other arithmetic operations on ARM processors. It is a powerful tool for low-level programming and optimization, but it requires a significant amount of expertise to write and debug.

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Answer:

is it 4.2626

Step-by-step explanation:

let me know if it right

To determine if a function has a vertical asymptote, you need to consider its behavior as the input approaches certain values.

A vertical asymptote occurs when the function approaches positive or negative infinity as the input approaches a specific value. Here's how you can determine if a function has a vertical asymptote:

Check for restrictions in the domain: Look for values of the input variable where the function is undefined or has a division by zero. These can indicate potential vertical asymptotes.

Evaluate the limit as the input approaches the suspected values: Calculate the limit of the function as the input approaches the suspected values from both sides (approaching from the left and right). If the limit approaches positive or negative infinity, a vertical asymptote exists at that value.

For example, if a rational function has a denominator that becomes zero at a certain value, such as x = 2, evaluate the limits of the function as x approaches 2 from the left and right. If the limits are positive or negative infinity, then there is a vertical asymptote at x = 2.

In summary, to determine if a function has a vertical asymptote, check for restrictions in the domain and evaluate the limits as the input approaches suspected values. If the limits approach positive or negative infinity, there is a vertical asymptote at that value.

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Answer:

4567899999

Step-by-step explanation:

rfghnmbgfdsrthnb  cxdfghn cxdfghjnm cdfrtgyhjmn  

Answer:

idududydysehwghsysysgsbebeywyehe

Answer:

S₇ = 127

Step-by-step explanation:

the sum to n terms of a geometric series is

\(S_{n}\) = \(\frac{a_{1}(r^n-1) }{r-1}\)

where a₁ is the first term and r the common ratio

here a₁ = 1 and r = \(\frac{a_{2} }{a_{1} }\) = \(\frac{2}{1}\) = 2 , then

S₇ = \(\frac{1(2^7-1)}{2-1}\) = \(\frac{2^7-1}{1}\) = \(2^{7}\) - 1 = 128 - 1 = 127

The least squares method minimizes the sum of the squared differences between the observed data points and the predicted values.

The least squares method is a mathematical technique used to find the best fit line or curve for a set of data points. It is commonly used in regression analysis to determine the relationship between two variables.

The method works by minimizing the sum of the squared differences between the observed data points and the predicted values from the line or curve. This sum is known as the residual sum of squares (RSS) or the sum of squared residuals (SSR).

The least squares method aims to find the line or curve that minimizes this sum, meaning it minimizes the overall error between the observed data and the predicted values. By minimizing the sum of squared differences, the method finds the line or curve that best represents the data.

In other words, the least squares method seeks to find the line or curve that provides the best balance between fitting the data closely and avoiding extreme deviations from the data points.

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The least squares method for determining the best fit minimizes the sum of the squared differences between the observed data points and the corresponding values predicted by the mathematical model or regression line.

In other words, it aims to minimize the sum of the squared residuals, where the residual is the difference between the observed data point and the predicted value. By minimizing the sum of squared residuals, the least squares method finds the line or curve that best fits the data by minimizing the overall error between the predicted values and the actual data.

Mathematically, the least squares method minimizes the objective function:

E = Σ(yᵢ - ŷᵢ)²

where yᵢ is the observed value, ŷᵢ is the predicted value, and the summation Σ is taken over all data points. The goal is to find the values of the parameters in the mathematical model that minimize this objective function, usually by differentiating it with respect to the parameters and setting the derivatives equal to zero.

By minimizing the sum of squared differences, the least squares method provides a way to estimate the parameters of a mathematical model that best represents the relationship between the independent and dependent variables in a data set.

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The cumulative total in Vietnam j(n) is 23(n-1),cumulative takes into account all data up to the present. In month four, j(n) will be worth more than v(n).

The distinction between total and cumulative is that total refers to the entirety of something, whereas cumulative takes into account all data up to the present.

When a new number is added to a series of numbers, the running total is updated by adding the new number's value to the previous running total. Partially sum is another name for it. Running totals serve two distinct objectives.

We take it for granted that you mean that vehicles delivered to Vietnam should be modeled by v(n) = 5 + 11 and those sent to Japan by j(n) = 23(n-1).

In month four, j(n) will be worth more than v(n).      

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Answer: A random variable.

Step-by-step explanation:

a numerical description of the outcome of an experiment is called a random variable.

The number that could be expected to have cable television will be 138 people.

From the information, survey showed the 8 about of 20 home owners in a neighborhood had cable television

Therefore, when there were 320 homeowners in the neighborhood, the number that could be expected to have cable television will be:

= 8/20 ×320

= 8 × 16

= 128

The number of people will be 128.

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The corresponding polar coordinates are (10, 2π/3). Option B

From the information given, we have;

(-5, 5√3)

Given that the polar coordinates are represented as;

r = √(x² + y²)

θ = arctan(y / x)

We get;

\(r = \sqrt{(-5)^2} + (\sqrt{5} /3)^2\)

Find the value of square, we have;

r = \(\sqrt{25 + 75}\)

Add the values, we get;

r = \(\sqrt{100}\)

Find the square root

r = 10

To determine the value of theta, we get;

θ = arctan((-5√3) / -5)

Divide the values, we have;

θ = arctan(√3)

θ = 60 degrees

Then, we have; (10, 2π/3)

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When a predictive model is made overly complex to fit in the quirks of given sample data, it is called overfitting.

Overfitting occurs when a model is too complex and tries to fit the data too closely. This can result in a model that performs well on the training data but performs poorly on new, unseen data.

Overfitting can be prevented by simplifying the model or using a larger training dataset. It is important to strike a balance between complexity and simplicity in order to create a model that generalizes well to new data.

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Chenelle can travel at most 239.31 miles without exceeding her spending limit of $300.

To figure out the maximum number of miles Chenelle can travel without exceeding her spending limit, we need to use algebra. Let's start by setting up the equation:

19.95 + 1.17x ≤ 300

In this equation, x represents the number of miles Chenelle can travel. We want to solve for x, so we need to isolate it on one side of the inequality. First, we'll subtract 19.95 from both sides:

1.17x ≤ 280.05

Next, we'll divide both sides by 1.17:

x ≤ 239.31

So Chenelle can travel at most 239.31 miles without exceeding her spending limit of $300.

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While both star clusters and galaxies are assumed to have formed simultaneously from a collapsing ball of gas as linear equation .

A linear equation is one that satisfies the algebraic formula y=mx+b. m is the y-intercept, while B is the slope. The foregoing sentence is sometimes referred to as a "linear equation with two variables" because y and x are variables. Bivariate linear equations are two-variable linear equations. Examples of linear equations include 2x - 3 = 0, 2y = 8, m + 1 = 0, x/2 = 3, x + y = 2, and 3x - y + z = 3.

Here,

Given:

Both contain stars that are held together by gravity. Part of the Local Group of galaxies is the Milky Way galaxy, which houses our solar system.

The Milky Way, the Magellanic Clouds (big and small), and the Andromeda Galaxy are the largest. The average galaxy is also bigger than a star cluster. The cities where star clusters reside are like galaxies, according to Geller.

"Galaxies can contain thousands or more star clusters, numerous molecular clouds, dark matter, and other things," the author writes.

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The complete question is "Establish a linear equation between the following Solar system, Star cluster, Galaxy and Galaxy cluster. "

The derivative of the function is

A) dw/dz=z^5 (7z-18)

B) dh/dx=192x^11+36x^5-6x^2+19

C) dy/dx=24x^2-38x-15

In mathematics, the derivative of a function measures the sensitivity to change of the function value with respect to a change in its argument. It is the rate of change of a function with respect to a variable.

A) w = z^7 – 3z^6 + 14

dw/dz=7z^6-18z^5

dw/dz=z^5 (7z-18)

B) h(x) = 16x^12 + 6x^6 – 2x^3 + 19x – 7

dh/dx=192x^11+36x^5-6x^2+19

C) y = (8x + 5)(x^2 – 3x)

dy/dx=  d/dx  [8x+5]*〖(x〗^2-3x)+(8x+5)*d/dx[x^2-3x]

dy/dx=(8*  d/dx  [x}+d/dx[5])*〖(x〗^2-3x)+(8x+5)*(d/dx[x^2]-3 d/dx[x])

dy/dx=(8*1+0)(x^2-3x)+(8x+5)(2x-3*1)

dy/dx=8(x^2-3x)+(2x-3)(8x+5)

dy/dx=24x^2-38x-15

Note: The question is incomplete. The complete question probably is: Find the derivative of the following function: A) w = z^7 – 3z^6 + 14 B) h(x) = 16x^12 + 6x^6 – 2x^3 + 19x – 7 C) y = (8x + 5)(x^2 – 3x).

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a) The probability that a battery lasts more than four hours can be calculated by converting four hours (240 minutes) into a standard score and finding the area under the normal distribution curve to the right of that score.

First, we calculate the z-score using the formula:

z = (x - μ) / σ

where x is the value (240 minutes), μ is the mean (265 minutes), and σ is the standard deviation (50 minutes).

z = (240 - 265) / 50

z = -0.5

Next, we look up the corresponding area under the normal distribution curve for a z-score of -0.5. This can be found using a standard normal distribution table or a calculator. The area to the right of -0.5 is equal to the area to the left of 0.5, which is approximately 0.3085.

Therefore, the probability that a battery lasts more than four hours is 1 - 0.3085 = 0.6915, which rounds to 0.692.

The probability that a battery in a laptop computer lasts more than four hours is approximately 0.692.

b) To find the quartiles of battery life, we need to calculate the values corresponding to the 25th and 75th percentiles of the normal distribution.

The 25th percentile corresponds to a z-score of -0.674. We can use the formula mentioned earlier to calculate the value:

x = μ + (z * σ)

x = 265 + (-0.674 * 50)

x = 231.3

Therefore, the 25th percentile value (Q1) is approximately 231 minutes.

The 75th percentile corresponds to a z-score of 0.674. Again, using the formula:

x = μ + (z * σ)

x = 265 + (0.674 * 50)

x = 298.7

Therefore, the 75th percentile value (Q3) is approximately 299 minutes.

The quartiles of battery life in a laptop computer are 231 minutes (Q1) and 299 minutes (Q3), rounded to the nearest integer.

c) To find the value of battery life in minutes that is exceeded with 95% probability, we need to find the z-score that corresponds to a cumulative probability of 0.95.

Using a standard normal distribution table or a calculator, we find that the z-score corresponding to a cumulative probability of 0.95 is approximately 1.645.

Using the formula mentioned earlier, we can calculate the value:

x = μ + (z * σ)

x = 265 + (1.645 * 50)

x = 344.25

Therefore, the value of battery life in minutes that is exceeded with 95% probability is approximately 344 minutes.

With 95% probability, the battery life in a laptop computer exceeds approximately 344 minutes.

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To calculate the probability that none of the tables have two of the same version of the quiz, we can use the permutation formula: 4*3*2=24 ways to distribute the quizzes to the students in the class randomly. We can start by calculating the number of ways to distribute the quizzes so that each table has different quizzes.

To do that, we'll use the following formula for permutations:

6! (4!2!2!)^6. For each table, there are 4! ways to distribute the quizzes among the two students and 2! ways to arrange the quizzes for each student.

There are six tables, so multiply this by (4!2!2!)^6. The denominator is the total number of possible permutations, which is 3^12. Therefore, the probability is:

6!(4!2!2!)^6/3^12

=0.01736

(b) Let's define the set of tables T = {T₁, T2, T3, T4, T5, T6} and the sample space S = {all ways to distribute two versions of the quiz to each table T, € T}. Then, we can define a Bernoulli random variable for each s € S as follows: X(s) = 0, if no tables in s have two of the same version X(s), if at least one table in s has two of the same version find the probability mass function (pmf) for X, we can count the number of ways to distribute the quizzes for each value of X(s, and divide by the total number of possible outcomes.

P(X=0) is the probability that none of the tables have two of the same version of the quiz, which we calculated in part (a) as 0.01736.

P(X=1) is the complement of P(X=0), which is

1 - P(X=0)

= 0.98264.

(c)To sketch a graph of the cumulative distribution function (cdf) for X, we need to calculate the cumulative probabilities for each value of X. The cdf for X is defined as:

F(x) = P(X ≤ x)

For X=0, the cumulative probability is simply

P(X=0) = 0.01736.

For X=1, the cumulative probability is

F(1) = P(X ≤ 1)

= P(X=0) + P(X=1)

= 0.01736 + 0.98264

= 1.0

Therefore, the graph of the cdf for X is shown below. The probability that none of the tables have two of the same version of the quiz is 0.01736. To find the probability mass function (pmf) for the Bernoulli random variable X, we counted the number of ways to distribute the quizzes for each value of X(s). We divided by the total number of possible outcomes.

We found that P(X=0) = 0.01736 and P(X=1) = 0.98264. Finally, we sketched the graph of the cumulative distribution function (cdf) for X, which shows that the probability of having at least one table with two of the same version of the quiz increases as the number of tables increases.

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Answer:

Δy = 5

Step-by-step explanation:

Answer:

a

Step-by-step explanation:

hi lol

Answer:

The Indapendent virable is: t

Noah took 4 hours to walk 12 miles

(7 1/2, 2 1/2)

Step-by-step explanation:

I hope this is right!

The point that represents the time it took to walk 7 ½ miles is (2.5, 7.5).

Coordinate, is represented as the values on the x-axis and y-axis of the graph. while the coordinate x is called abscissa and the coordinate of the y is called ordinate.

Here,
The independent variable in this situation is time (t).

Point (12, 4) represents that Noah walked for 4 miles in 12 hours.

To find the point that represents the time it took to walk 7 ½ miles, we need to solve for t in the equation ⅓ d = t.

Substituting d = 7.5, we get:

⅓ (7.5) = t

Simplifying, we get:

t = 2.5

Therefore, the point that represents the time it took to walk 7 ½ miles is (2.5, 7.5).

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Answer:

too hard!! (jkk)

Step-by-step explanation:

188 albums.

Add all numbers together and you should get 188

The probability of a z-score being greater than 1.9 is approximately 0.0287. i.e option a) is correct

To calculate the probability that the sample proportion will be greater than 0.276, we can use the sampling distribution of the sample proportion.

In this case, the sample size is 100, and the population proportion is 0.2. The sample proportion follows an approximately normal distribution with a mean equal to the population proportion (0.2) and a standard deviation equal to the square root of (p * (1 - p) / n), where p is the population proportion and n is the sample size.

Let's calculate the standard deviation first:

Standard deviation (σ) = √(p * (1 - p) / n)

= √(0.2 * (1 - 0.2) / 100)

= √(0.16 / 100)

= √0.0016

= 0.04

Now, we can calculate the z-score corresponding to the sample proportion of 0.276:

z = (sample proportion - population proportion) / standard deviation

= (0.276 - 0.2) / 0.04

= 0.076 / 0.04

= 1.9

Using a standard normal distribution table or a calculator, we can find the probability associated with a z-score of 1.9. The probability of a z-score being greater than 1.9 is approximately 0.0287.

Therefore, the answer is (a) 0.0287.

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Since an outlier will have an effect when dealing with small sample sizes the true statement is option b. They have the biggest effect when dealing with small sample sizes.

An outlier

In Mathematics, an outlier is a value of a set of data that is quite different from the other values. In simple terms, outliers are values that are far from the middle.

Outliers have a significant impact on the mean, but not on the median or mode. Remember There is no such rule that determines the outliers of data, these are extreme values of data.

Example:

1) 15, 16, 20, 26, 30, 31, 35, 42, 45, 150, 200  

In the given data, 150 and 200 are different from other values

An outlier of given data is 150 and 200

These are the points that lie outside the entire distribution. These outliers are shown as dots.  

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Answer:

Function A  intercept: 4

Function B intercept: 3

Step-by-step explanation:

The angle of BDC is 60 degrees.

The angle sum property of a triangle states that the sum of a triangle's three internal angles is 180 degrees. A triangle is a closed figure made up of three line segments that have both internal and external angles. When the values of the other two angles are known, the measure of an unknown interior angle can be determined using the angle sum property.

The data listed above can be inserted into our diagram. ADB and BDC complement one another, therefore their combined degree measurement is 180. Thus, m∠ADB is equal to 180 – 60, or 120 degrees.

Given,

∠ADC = 59

∠BDC = ?

∠ADB = ∠BCD

∠BCD = 90

∠ADB = 90

∠ADC = 59

∠ADB + ∠BDC = 59

90 + ∠BDC = 59

∠BDC = -31

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