P( factor of 72 and even) please answer in decimal

This statement is false.

Every real matrix has real eigenvalues or comes in pairs of complex conjugate eigenvalues.

In general, the eigenvalues of a 2x2 matrix A can be found by solving the characteristic equation det(A - lambda*I) = 0, where I is the 2x2 identity matrix and lambda is the eigenvalue. The characteristic equation is a quadratic equation in lambda, and its solutions are the eigenvalues of A.

For a real 2x2 matrix, the coefficients of the characteristic equation are real, and so the solutions to the characteristic equation are either real or complex conjugate pairs. This follows from the fact that complex roots of real polynomials always come in complex conjugate pairs.

Now, suppose that a real 2x2 matrix A has eigenvalues i and 2i. Since these eigenvalues are not real, they must be complex conjugates of each other. That is, 2i is also an eigenvalue of A, and the other eigenvalue must be the complex conjugate of i, which is -i.

However, the characteristic equation of a 2x2 matrix with eigenvalues i and 2i is given by:

det(A - lambda*I) = (a - lambda)(d - lambda) - bc = (a - lambda)(d - lambda) - b*c

where A = [[a,b],[c,d]], and lambda = i or 2i.

If we substitute lambda = i into the above equation, we get:

(a - i)(d - i) - b*c = 0

Expanding this equation, we get:

ad - ai - di + i^2 - bc = 0

Simplifying, we get:

(ad - bc) - i(a + d) = 0

Since the matrix A has real entries, the imaginary part of this equation must be zero, which implies that a + d = 0. But this contradicts the assumption that A has eigenvalues i and 2i, since the sum of the eigenvalues of A is equal to the trace of A, which is a + d. Therefore, there cannot exist a real 2x2 matrix with eigenvalues i and 2i.

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The average number of grams of fat per ounce of meat or fish consumed by a person over a 5 day period, using the weighted mean, is 2.72 g/oz.

To find the weighted mean of the average number of grams of fat per ounce of meat or fish consumed by a person over a 5 day period, we need to multiply each fat (g/oz) value by its corresponding weight and then add them all up.

Here's how to do it:

- Fried shrimp: 3 oz x 3.33 g/oz = 9.99 g
- Veal cutlet: 3.33 oz x 3 g/oz = 9.99 g
- Roast beef: 2 oz x 2.5 g/oz = 5 g
- Fried chicken: 2.5 oz x 4.4 g/oz = 11 g
- Canned tuna: 4 oz x 1.75 g/oz = 7 g

Next, add up all the total grams of fat consumed:

9.99 + 9.99 + 5 + 11 + 7 = 42.98 g

Finally, divide the total grams of fat by the total weight of meat and fish consumed:

(3 + 3.33 + 2 + 2.5 + 4) = 15.83 oz

42.98 g ÷ 15.83 oz = 2.72 g/oz

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Answer: 18

Step-by-step explanation: you can multiply the possibilities with each other:

2 numbers are less than 3, 3 numbers are even, and 3 are odd

2 x 3 x 3 = 18, meaning there are 18 different ways to accomplish the rolls

The basketball league has 576 games in total.

A mathematical procedure called addition combines two or more numbers to determine their sum or total.

It is indicated by the plus sign ("+"). The effect of adding numbers is unaffected by the sequence in which the numbers are added.

The sum is the name given to the outcome of an addition operation.

In the east conference, each team plays against the other seven teams from the same conference four times.

Since there are eight teams in the east conference, the total number of games within the east conference can be calculated as follows:

Total games in the east conference = 8 teams × 7 opponents × 4 games per opponent

Similarly, in the west conference, each team plays against the other seven teams from the same conference four times. So the total number of games within the west conference can be calculated as:

Total games in the west conference = 8 teams × 7 opponents × 4 games per opponent

Now, each team from the east conference plays against all eight teams from the west conference twice. Thus, the total number of games between the conferences is:

Games between the conferences = 8 teams × 8 opponents × 2 games per opponent

To find the total number of games, we sum up the games within each conference and the games between the conferences:

Total games = Total games in the east conference + Total games in the west conference + Games between the conferences

Plugging in the values:

Total games = (8 × 7 × 4) + (8 × 7 × 4) + (8 × 8 × 2)

= 224 + 224 + 128

= 576

Therefore, there are a total of 576 games in the basketball league.

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The concept stoichiometry is used here to determine the moles of oxygen produced according to the given chemical reaction. It refers to the quantitative study of reactants and products involved in a reaction.

Stoichiometry is an important concept in chemistry which helps us to use the balanced chemical equation to find out the amounts of reactants and products. Here we make use of molar ratios from the balanced equation.

The balanced equation is:

4Al  +  3O₂     →     2Al₂O₃

So, 1.35 mol O₂ × 4 mol Al / 3 mol O₂

=5.4 /3

=  1.8 mol Al

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The volume of pyramid with given dimensions of base, length and height is 102.4 cubic units.

The volume of pyramid is calculated using the formula -

Volume = Length × width × height/3

Keep the values in formula to find the value of volume of pyramid.

Volume = 4 × 6.4 × 12/3

Performing multiplication on Right Hand Side of the equation to find the value of volume of pyramid

Volume = 307.2/3

Now divide the values on RHS to get the volume of pyramid

Volume = 102.4

Hence, the volume of the pyramid is 102.4 cubic units.

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The equation that represents the different amounts of chicken X and steak y that Mr Day can buy with the amount available would be = 3x + 5y = $75

The total amount of money that Mr Day has to spend = $75

The amount of money that chicken (X) is sold = $3

The amount of money that steak(y) is sold = $5

Therefore,the expression that should show the total amount of chicken and steak that Mr Day can get would be = 3x + 5y = $75

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The equation that represents the different amounts of chicken, x, and steak, y, Mr. Day can buy is:

3x + 5y = 75

Where x is the amount of chicken in pounds, and y is the amount of steak in pounds.

To rewrite the equation in slope-intercept form, we need to solve for y:

3x + 5y = 75

5y = -3x + 75

y = (-3/5)x + 15

So the slope of the line is -3/5 and the y-intercept is 15. This means that for every 5 pounds of steak Mr. Day buys, he can buy 3 pounds less of chicken and still stay within his budget. The y-intercept tells us that if Mr. Day doesn't buy any steak, he can buy 15 pounds of chicken.

To determine whether the mean annual tea consumption in the United States has decreased, we need to conduct a hypothesis test. We can use either a z-test or a t-test, depending on whether the population standard deviation is known or unknown, respectively. In this case, we are given that the population standard deviation is 2.2 gallons, so we can use a z-test.

Our null hypothesis is that the mean annual tea consumption in the United States is equal to 8.9 gallons. Our alternative hypothesis is that the mean annual tea consumption is less than 8.9 gallons. We want to test if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

Using the sample data provided, we calculate a test statistic of -2.45. This is calculated by taking the difference between the sample mean and the hypothesized population mean, divided by the standard error of the mean (which is the population standard deviation divided by the square root of the sample size).

Next, we use a standard normal distribution table to find the p-value associated with our test statistic. The p-value turns out to be 0.0071.

Finally, we compare the p-value to our chosen significance level (usually 0.05 or 0.01) to determine whether we should reject the null hypothesis. In this case, the p-value is less than 0.05, so we reject the null hypothesis and conclude that there is enough evidence to show that the mean annual tea consumption in the United States has decreased.

In conclusion, based on the given sample data, we can conduct a z-test to determine whether the mean annual tea consumption in the United States has decreased. We reject the null hypothesis and conclude that there is enough evidence to support the alternative hypothesis that the mean annual tea consumption has indeed decreased.

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Answer:

  [tex]\displaystyle \left[\begin{array}{ccc}\vphantom{\int\limits_q^B}\dfrac{1}{6}&-\dfrac{1}{6}&\dfrac{1}{3}\\\vphantom{\int\limits_q^B}-\dfrac{11}{6}&\dfrac{3}{2}&-\dfrac{8}{3}\\\vphantom{\int\limits_q^B}5&-3&5\end{array}\right][/tex]

Step-by-step explanation:

You want the inverse of the given coefficient matrix using the Gauss-Jordan elimination method.

The method starts by forming an augmented matrix with the given matrix on the left, and the identity matrix on the right. Row operations are then performed that cause the matrix on the left to become the identity matrix. The matrix on the right is then the inverse of the original coefficient matrix.

For example, the first row operation is to divide the first row by the upper left coefficient, 9. This makes the first row be [1, 1/3, 1/9, 1/9, 0, 0]. This row is then used to zero the remaining 1st-column terms, by subtracting the values obtained by multiplying this by the first-column coefficients.

In the first attachment, all rows are replaced at each step, so there is not a separate step for making the diagonal term be 1. The "ReplacePart" function is what does the replacement of the designated row.

The second attachment shows a calculator computes the same result.

__

Additional comment

The number of math operations is quite large, so they are not all shown here. Rather, we have shown the result at each step of creating one column of the desired identity matrix in the first three columns.

If the starting matrix is further augmented by the constants on the right side of the matrix equation, the end result will show the solution to the equation(s). The second attachment shows the solution is ...

  (a, b, c) = (-1/3, 2, 5)

For any vectors u and v in V₃ and any scalar k,

(ku) v. is equal to (ku) v.

Let's consider,

k = 2, u = 2i and v = 3i.

k(u v) = (ku) v.

L. H. S. = R. H. S.

k(u v) ........(1)

(ku) v.........(2)

Plugging the values of k, u and v in equation 1.

2 (2i × 3i) = 2×6i² = 12.

Now, plugging the values of k, u and v in equation 2.

(ku) v. = (2 × 2i) × 3i = 4i ×3i = 12i² = 12.

Therefore, for any vectors u and v in V₃ and any scalar k,

k(u v)= (ku) v.

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The representations given, as regards whether it shows a quadratic function, is B. No.

To find out if the representations shows a quadratic function, find the first differences between the given y values:

10 - 5 = 5

15 - 10 = 5

20 - 15 = 5

Then, calculate the second differences, using the first differences :

5 - 5 = 0

5 - 5 = 0

The second difference is not the same as the first difference and so this is not a quadratic function.

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The margin of error can be calculated as half the width of the confidence interval. So, the margin of error is:

(0.257 - 0.183) / 2 = 0.037

Therefore, the answer is (a) 0.037.
The margin of error for this confidence interval can be calculated by finding the difference between the upper bound and the lower bound, and then dividing by 2.

In this case, the confidence interval is (0.183, 0.257).

Margin of error = (0.257 - 0.183) / 2 = 0.074.

So the answer is b. 0.074.

The margin of error is a measure of the precision of an estimate or a statistic, often used in opinion polls and surveys. It represents the amount of sampling error that is expected in the results due to random variation.

In statistical terms, the margin of error is calculated as a range of values around the estimated statistic, within which the true population parameter is likely to fall with a certain degree of confidence. The most common way to express the margin of error is as a plus or minus value, typically represented as a percentage of the sample size.

For example, if a survey of 1,000 people finds that 60% of them support a particular political candidate, with a margin of error of +/- 3%, this means that there is a 95% chance that the true level of support in the population falls between 57% and 63%. In other words, if the survey were to be repeated many times, 95% of the resulting confidence intervals would contain the true level of support.

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The integral of the 1-form field x dy over the straight-line path from (2,0) to (0,3) is equal to 3.

To integrate the 1-form field x dy over the straight-line path from (2,0) to (0,3), we need to parametrize the path first.

Let's define a parameterization r(t) = (x(t), y(t)) for t in [0,1] that describes the straight-line path from (2,0) to (0,3).

The equation of the line passing through (2,0) and (0,3) can be written as y = -3/2 x + 3. So we can choose:

x(t) = 2 - 2t

y(t) = 3t

with t in [0,1].

Now we can calculate the differential form evaluated along the path:

x dy = x(t) dy/dt = (2-2t)(3) = 6 - 6t

Finally, we can integrate over the interval [0,1]:

∫(2,0) x dy = ∫(0,1) (6 - 6t) dt = [6t - 3t^2] from 0 to 1 = 3.

Therefore, the integral of the 1-form field x dy over the straight-line path from (2,0) to (0,3) is equal to 3.

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The mean number of children per household in the community is 2.4. The correct answer is a.

This is because Tabitha surveyed 20 families in the community, and the mean number of children per household for those families was 2.4. As long as the sample was random and representative of the community, the mean of the sample can be used as an estimate of the mean of the population. Option b is incorrect because sample size is not too small. Option c is not necessarily true since we do not know the sampling method used. Option d may or may not be true depending on the sampling method used.

To determine the mean number of children per household in a community, Tabitha surveyed 20 families at a playground and found a mean of 2.4 children per household. The statement that must be true is:
c. The sampling method is flawed and may produce a biased estimate of the mean number of children per household in the community.
This is because Tabitha's sampling method only includes families at a playground, which likely leads to a higher mean number of children, as families with more children are more likely to be found at a playground.

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From the expansion of the algebraic expression (x + 9) (x + 4) (x + 10), the values of q, r, and t are:

The value of the expansion of the algebraic expression (x + 9) (x + 4) (x + 10) is given below as follows:

(x + 9) (x + 4) (x + 10) = (x + 9)(x + 4)(x + 10)

(x + 9) (x + 4) (x + 10) = (x + 9)(x² + 14x + 40)

(x + 9) (x + 4) (x + 10) = x(x² + 14x + 40) + 9(x² + 14x + 40)

(x + 9) (x + 4) (x + 10) = x³ + 14x² + 40x + 9x² + 126x + 360

(x + 9) (x + 4) (x + 10) = x³ + 23x² + 166x + 360

Comparing this expression with the given form x³ + qx² + rx + t, we can see that:

q = 23

r = 166

t = 360

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The minimum sample size required to estimate the population mean with 95% confidence and a margin of error of 1.5, given a population standard deviation of 10.75, is approximately 190 (option d).

To determine the minimum sample size (n) required to estimate a population mean with 95% confidence, given a desired margin of error (E) of 1.5 and a known population standard deviation (σ) of 10.75, we can use the formula:
n = (Z * σ / [tex]E)^2[/tex]
In this formula, Z is the Z-score associated with the desired confidence level. For a 95% confidence level, the Z-score is 1.96 (based on a standard normal distribution table).
Now, we can plug in the given values and solve for n:
n = [tex](1.96 * 10.75 / 1.5)^2[/tex]
n = [tex](20.67 / 1.5)^2[/tex]
n = [tex]13.78^2[/tex]
n ≈ 189.76
Since we require a whole number for the sample size, we round up to the nearest whole number, which is 190. This value is not among the given options, but it is closest to option d (n = 198). Therefore, the best answer among the given options is d (n = 198).

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50 degrees is the value of the variable x.

The sum of the interior angles of a pentagon is given by the formula (5-2) × 180 = 540 degrees.

One of the angles is known to be 90 degrees, while two of the angles are known to be 125 degrees. The remaining two angles should each be 2x degrees. The sum of the pentagon's inner angles may then be used to create the following equation:

90 + 125 + 125 + 2x + 2x = 540

Simplifying the left-hand side of the equation, we get:

340 + 4x = 540

Subtracting 340 from both sides, we get:

4x = 200

Dividing both sides by 4, we get:

x = 50

Therefore, the value of x is 50 degrees.

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The population mean time on death row is different than 15 years with p value is 0.0002.

To conduct the hypothesis test, we will use a one-sample t-test with the following null and alternative hypotheses:

Null hypothesis: The population mean time on death row is equal to 15 years. μ = 15

Alternative hypothesis: The population mean time on death row is different than 15 years. μ ≠ 15

We will use a significance level of 0.05.

First, we need to calculate the t-value:

t = (x - μ) / (s / √n)

t = (17.4 - 15) / (6.3 / √75)

t = 3.667

Next, we need to find the degrees of freedom:

df = n - 1

df = 74

Using an online calculator for the t-distribution, we find that the p-value for a two-tailed test with a t-value of 3.667 and 74 degrees of freedom is less than 0.00015 (or 0.0002 when rounded to 4 decimal places).

Since the p-value is less than the significance level of 0.05, we reject the null hypothesis and conclude that there is sufficient evidence to suggest that the population mean time on death row is different than 15 years.

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Power analysis is a valuable tool used before data collection to ensure that you have an adequate sample size to achieve the desired power, minimize Type II error rates, and maintain a Type I error significance level.

Yes, that is correct. Power analysis is a crucial step in the planning phase of a research study. It allows researchers to estimate the required sample size to achieve adequate statistical power for the study. Adequate statistical power ensures that the study is able to detect meaningful differences or effects between groups or variables. This is important because collecting too little data can lead to a low power, which increases the risk of false-negative results (type II error).

Power analysis can also help evaluate the effect size of a study when there are restrictions on the data collection, such as limited time or resources and its significance level. Overall, power analysis is a valuable tool for ensuring that a study collects enough data to draw meaningful conclusions. By considering the practical situation and appropriate effect size, power analysis helps you make informed decisions about your study design and evaluate the study's effect size when faced with restricted resources. This way, you can maximize the reliability and validity of your study's findings.

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Answer: 5,000,000

Step-by-step explanation:

Simply add them together, and then put the zeros in with it.

Answer:

Step-by-step explanation:

5,000,000

Based on the given data, we can calculate the sample proportions of adults using social media for each country as follows:

United Kingdom: p1 = 480/(480+320) = 0.6

China: p2 = 215/(215+285) = 0.43

Russia: p3 = 343/(343+357) = 0.49

United States: p4 = 640/(640+360) = 0.64

To test whether the proportion of adults using social media is equal for all four countries, we can use a chi-squared test of independence with the null hypothesis:

H0: P1 = P2 = P3 = P4

And the alternative hypothesis:

Ha: Not all population proportions are equal

We can calculate the expected frequencies for each cell assuming the null hypothesis is true, and use these to calculate the chi-squared test statistic:

Expected frequency for Yes-UK = (480+320) * (480+215+343+640) / (480+215+343+640+320+285+357+360) = 400

Expected frequency for No-UK = (320+480) * (480+215+343+640) / (480+215+343+640+320+285+357+360) = 400

Expected frequency for Yes-China = (215+285) * (480+215+343+640) / (480+215+343+640+320+285+357+360) = 320

Expected frequency for No-China = (285+215) * (480+215+343+640) / (480+215+343+640+320+285+357+360) = 320

Expected frequency for Yes-Russia = (343+357) * (480+215+343+640) / (480+215+343+640+320+285+357+360) = 350

Expected frequency for No-Russia = (357+343) * (480+215+343+640) / (480+215+343+640+320+285+357+360) = 350

Expected frequency for Yes-US = (640+360) * (480+215+343+640) / (480+215+343+640+320+285+357+360) = 500

Expected frequency for No-US = (360+640) * (480+215+343+640) / (480+215+343+640+320+285+357+360) = 500

Using these expected frequencies and the observed frequencies, we can calculate the chi-squared test statistic:

chi2 = sum((observed - expected)^2 / expected) = (480-400)^2/400 + (320-400)^2/400 + (215-320)^2/320 + (285-320)^2/320 + (343-350)^2/350 + (357-350)^2/350 + (640-500)^2/500 + (360-500)^2/500 = 33.49

The degrees of freedom for this test are df = (r-1)(c-1) = (2-1)(4-1) = 3, where r is the number of rows (2 for Yes/No) and c is the number of columns (4 for the countries).

We can then use a chi-squared distribution with 3 degrees of freedom to calculate the p-value for the test:

p-value = P(Chi2 > 33.49) = 0.00000031 (using a chi-squared calculator)

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The function d(x) that represents the value of Dyanas truck X years after it's purchase is 15000(0.9)^x

Assuming that the depreciation rate is constant over the years, the function d(x) can be expressed as:

d(x) = 15000(1-0.1)^x

Where x is the number of years since the purchase of the truck.

This can be we can simplified further as: d(x) = 15000(0.9)^x

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The total land area of Edgardo's property is 36,400 square meters.

The area is a measure of the amount of two-dimensional space that a flat surface or shape occupies. It is a fundamental concept in geometry and is expressed in square units, such as square meters (m²), square feet (ft²), or square centimeters (cm²).

Edgardo's original rectangular lot has an area of:

140 m x 120 m = 16,800 m²

The square lot he purchased has an area of:

140 m x 140 m = 19,600 m²

To find the total land area of Edgardo's property, we add the areas of the two lots:

16,800 m² + 19,600 m² = 36,400 m²

Therefore, the property owned by Edgardo has a total land size of 36,400 square meters.

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Therefore, we do not have sufficient evidence to support the executive's claim that the true proportion of readers who own the particular make of car is different from the reported proportion of 43% at the 0.10 level of significance.

To determine whether there is sufficient evidence to support the executive's claim that the percentage of readers who own a particular make of car is different from the reported percentage of 43%, we need to conduct a hypothesis test.

The null hypothesis H0 is that there is no difference between the true proportion of readers who own the particular make of car and the reported proportion of 43%:

H0: p = p0

The alternative hypothesis Ha is that the true proportion of readers who own the particular make of car is different from the reported proportion:

Ha: p ≠ p0

We can use a z-test for proportions to test this hypothesis, since the sample size is sufficiently large and the sampling distribution of the sample proportion can be approximated by a normal distribution.

test statistic is calculated as:

z = (x/n - p0) / √(p0*(1-p0)/n)

Substituting the values from the problem statement, we get:

z = (0.38 - 0.43) / √(0.43*(1-0.43)/200)

= -1.51

At the 0.10 level of significance, the critical values for a two-tailed test are ±1.64. Since our calculated z-value of -1.51 is not in the rejection region, we fail to reject the null hypothesis.

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According to the information, the constant of proportionality is 1/2, the equation that represents the relationship p = (1/2)t, and the points awarded for 18 minutes of play p = (1/2)(18) = 9.

To find the constant of proportionality, we can use the fact that the game awards one-half point for every 1 minute of play. We can set up a proportion:

where,

Simplifying the left side, we get:

Therefore, the constant of proportionality is 1/2.

To write an equation that represents the relationship, we can use the constant of proportionality we found in Part A. Let p be the number of points awarded and t be the amount of time played. Then the equation is:

For 5 minutes or more, the game awards two and one-half points for every 5 minutes of play. Thus, for t ≥ 5, we have:

Thus, the equation that represents the relationship is:

To graph the relationship, we can plot the points (t, p) for different values of t. Because the number of points awarded is proportional to the amount of time played, the points will lie on a straight line that passes through the origin (0, 0). We can plot at least two points to draw the line, such as (1, 1/2) and (5, 2.5).

To find the number of points awarded for 18 minutes of play, we can use the equation we found in Part B:

Substituting t = 18, we get:

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The range would increase by 1 when the age of 17 is added to the data

Given data ,

The range is the difference between the highest and lowest values in a data set

In the original data set provided, the highest value is 33 and the lowest value is 18, so the range is 33 - 18 = 15

Now , when the age 17 is added , the new lowest value of the data is 17

So , the new range is R = 33 - 17 = 16

Hence , the range of the data has increased by 1

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The value of the trigonometric ratio Sin 89 is 0.9998

From the question, we have the following parameters that can be used in our computation:

Sin 89

The trigonometric ratio can be evaluated using a calculator

Using a calculator, we have the following result

Sin 89 = 0.99984769515

Approximate

Sin 89 = 0.9998

Hence, the solution is 0.9998

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a) The probability that a point is inside the square is given as follows: 1/6.

b) The probability that a point is outside the triangle is given as follows: 43/48.

A probability is calculated as the division of the desired number of outcomes by the total number of outcomes in the context of a problem/experiment.

The total area of the figure is given as follows:

12 x 8 = 96 units². (area of rectangle is the multiplication of the side lengths).

The area of the square is given as follows:

4² = 16 units² (square of the side lengths).

Hence the probability of the square is given as follows:

p = 16/96

p = 1/6.

The area of the triangle is given as follows:

A = 0.5 x 4 x 5 = 10 units². (half the multiplication of the side lengths, as the triangle is a right triangle).

Hence the complement of the area of the triangle is of:

96 - 10 = 86 units².

And the probability of the complement is of:

86/96 = 43/48.

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Causation between two variables shows how the independent variable influences the dependent variable. The given statement is True.

Causation refers to the relationship between two variables where one variable, known as the independent variable, influences the other variable, known as the dependent variable. In other words, causation is the mechanism by which changes in one variable lead to changes in another variable. For example, if a researcher wanted to determine if studying more hours leads to better exam scores, studying more hours would be the independent variable and exam scores would be the dependent variable.

It is important to note that correlation does not necessarily imply causation. Correlation simply means that there is a relationship between two variables, but it does not indicate that one variable is causing the other to change. For example, there may be a strong positive correlation between ice cream sales and crime rates, but this does not mean that ice cream sales cause crime. Rather, both variables may be influenced by a third variable, such as temperature.

In order to establish causation between two variables, researchers often conduct experiments. In an experiment, the independent variable is manipulated in a controlled manner, while all other variables are held constant, to determine its effect on the dependent variable. If a cause-and-effect relationship is found, then it can be concluded that the independent variable influences the dependent variable.

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