Help I don’t know this you dont have to explain:]

The number of different groups of subjects possible when randomly selecting from a group of subjects can be determined using the concept of combinations. The total number of possible groups can be calculated by finding the number of combinations of subjects that can be formed from the given group.

When selecting subjects from a group, the order of selection doesn't matter. We can use the combination formula to calculate the number of different groups. If there are 'n' subjects in the group and we want to select 'r' subjects, the number of different groups can be calculated as C(n, r), which is given by

n! / (r! * (n - r)!).

For example, if there are 10 subjects in the group and we want to select 3 subjects, the number of different groups of subjects possible would be

C(10, 3) = 10! / (3! * (10 - 3)!),

which simplifies to 10! / (3! * 7!).

Evaluating this expression will give you the total number of different groups of subjects that can be formed through random selection.

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Answer: c) 1 1/4

Step-by-step explanation: you do 1/4 times 5 .You make 5 a fraction which is 5/1.So now you do 1/4 times 5/1 which is 5/4.And you change it to a mixed number which is 1 1/4.

Answer:.

Step-by-step explanation:

Answer:

the answer is

Step-by-step explanation:

8.0208333333

To indicate a strong correlation between variables, the correlation coefficient will be close to its extreme values of -1 or 1, suggesting a high degree of linear relationship between the variables.

The correlation coefficient, also known as Pearson's correlation coefficient, ranges from -1 to 1.The correlation coefficient quantifies both the magnitude and direction of the linear association between two variables, providing a measure of the strength of their relationship.

When the correlation coefficient is close to -1, it suggests a strong inverse relationship between the variables, indicating that as one variable increases, the other variable tends to decrease consistently.

On the other hand, a correlation coefficient close to 1 indicates a strong positive correlation, where an increase in one variable is associated with an increase in the other variable.

A correlation coefficient near 0, or close to 0.5, does not indicate a strong correlation between the variables. A value close to 0 suggests a weak or no linear relationship between the variables, while a value close to 0.5 indicates a moderate correlation.

Therefore, to indicate a strong correlation between variables, we look for correlation coefficients that are near -1 or 1, indicating a strong negative or positive linear relationship, respectively.

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Answer:

The sum of any integer and its opposite is equal to zero. adding two negative integers always yields a negative sum. To find the sum of a positive and a negative integer, take the absolute value of each integer and then subtract these values.

Answer:

Step-by-step explanation:

The sum will have the same sign as the integer with the greater magnitude.

Example:  the sum of -11 and 5 is -6, where the 6 takes the sign of -11 (which has a greater magnitude than does 5).

Complete question :

From Monday through Friday, James works in the library on 2 days and in the cafeteria on another day. On Saturday and Sunday, James washes cars 50% of the days. How many days does James work in a week? What percent of the days from Monday through Friday does he work?

Answer:

4 days ;

60%

Step-by-step explanation:

From Monday to Friday

Number of days in library = 2

Number of days in cafeteria = 1

50% of days in weekends

Number of weekend days = 2

50% of 2 = 0.5 * 2 = 1 day

Total. Number of days worked = (2 +1 + 1) = 4 days

What percent of the days from Monday through Friday does he work?

Number of days workwd from. Monday to Friday = 3

Total number of days from Monday to Friday = 5

Percentage of days worked from Monday to Friday

= number of days worked / total number of days

= 3 /5 * 100%

= 0.6 * 100%

= 60%

Answer:

300°

Step-by-step explanation:

That'd be 5pi/3 radians.  Note that 3pi/3 radians corresponds to 180° and that 6pi/3 corresponds to 360°.  Thus 5pi/3 is in Quadrant IV, meaning that the angle in degrees is between 270° and 360°.  

The "radians to degrees" conversion factor is (180°/pi), and so

5pi/3 radians works out to:

5pi/3 · 180°       5· 180°

----------------- = ---------- = 300°

          3 pi             3

Answer:

Similar!

Step-by-step explanation:

Hope this helps!

Answer:

Add

Step-by-step explanation:

When MULTIPLY exponents with same base, you Keep the base and + ADD the exponents.

[tex] \purple{ \tt{ \huge{ \: ✨Answer ✨ \: }}}[/tex]

[tex] \: \: \: \: \: \: \: \: \: \: \: \: \red{ \boxed{ \boxed{ \huge{ \tt{{ \: add \: }}}}}}[/tex]

Answer:

2 ≤ x ≤ 8

Step-by-step explanation:

0 ≤ 3x - 6 ≤ 18

0 ≤ 3x - 6

6 ≤ 3x

2 ≤ x

3x - 6 ≤ 18

3x ≤ 18 + 6

3x ≤ 24

x ≤ 8

Combined:

2 ≤ x ≤ 8

Answer:

2 ≤ x≤ 8

2nd option:)

Step-by-step explanation:

The type of transformation in this problem is given as follows:

Vertical translation.

The four translation rules are defined as follows:

For this problem, we have a translation of 2 units up, which is called a vertical translation.

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Answer:

Option C is correct.

(3x)² - 2²

Answer: It is D or C

Step-by-step

I think it is D or C because 9 times 2 is 18. 18 minus 4 is 14. And all the other equations equal something else. The first one is 3- 2times 2 and that is 4 so that would be one. The second is x minus 4 that would be 8. Then the next would be 9 minus 4. That is 5 so it is either C or D.

Given,Test at 20% significance level whether one of the drugs is more effective than the other.

We need to calculate the absolute value of the critical value of this test, the absolute value of the calculated test statistic, and the p-value of this test.Solution:(a) The absolute value of the critical value of this test isThe level of significance is 20%.The degree of freedom is (r-1)(c-1) = (3-1)(2-1) = 2

From the chi-square table, the critical value for a chi-square distribution with 2 degrees of freedom and a level of significance of 0.20 is 3.219(i.e. critical value = 3.219)The absolute value of the critical value of this test is 3.219.(b) The absolute value of the calculated test statistic

The table is shown below:  We have, Total = 69 Test at 20% significance level whether one of the drugs is more effective than the other.To check the difference in effectiveness, we will use a chi-square goodness of fit test.

The null hypothesis H0: The two drugs are equally effective.

The alternative hypothesis Ha: One of the drugs is more effective.

The expected values of the two drugs are:  Expected Value of Drug A = (32+18) / 3 = 16 Expected Value of Drug B = (32+1) / 3 = 11 The calculations are summarized in the table below:  The formula to calculate the chi-square value is, chi-square = ∑ (O - E)2 / E The calculated value of chi-square is 6.07.The absolute value of the calculated test statistic is 6.07.(c) The p-value of this test isThe degree of freedom is (r-1)(c-1) = (3-1)(2-1) = 2

The level of significance is 20%.From the chi-square table, the p-value for a chi-square distribution with 2 degrees of freedom and a chi-square value of 6.07 is 0.047 (i.e. p-value = 0.047)The p-value of this test is 0.047.

Answer:(a) The absolute value of the critical value of this test is 3.219.(b) The absolute value of the calculated test statistic is 6.07.(c) The p-value of this test is 0.047.

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We fail to reject the null hypothesis and conclude that there is not enough evidence to suggest that one drug is more effective than the other at the 20% level of significance.

Below is the table of values for the drugs drug A and drug B

Group A 7 10 13 15 8 7 6 10 9 11 10 11

Group B 6 12 9 11 8 8 9 12 10 14

(a) The absolute value of the critical value of this test

The absolute value of the critical value of this test is given by the formula:

[tex]`z = |zα/2|`[/tex]

where α = 0.2;

degree of freedom = `n1+n2-2` = `12+10-2` = `20`

From the standard normal table, the value of zα/2 at 0.2 is 1.645

Therefore, the absolute value of the critical value of this test is; `|1.645| = 1.645`.

(b) The absolute value of the calculated test statistic

The formula for the test statistic is given by;

[tex]`t=bar x1 -bar x2)/((s^2/n1)+(s^2/n2))`[/tex]

where `bar x1` and `bar x2` are the sample means of the two groups,

`s^2` is the pooled variance, n1 and n2 are the sample sizes of the two groups

and [tex]`s^2 = ((n1 -1) s1^2 + (n2 -1) s2^2 )/(n1 + n2 - 2)`[/tex]

Using the data given, we get:

Group A 7 10 13 15 8 7 6 10 9 11 10 11

`n1` = 12

[tex]`bar x1` = `(7+10+13+15+8+7+6+10+9+11+10+11)/12 = 9.583`[/tex]

and `s1` = 2.818

Group B 6 12 9 11 8 8 9 12 10 14

`n2` = 10

[tex]`bar x2` = `(6+12+9+11+8+8+9+12+10+14)/10 = 9.9`[/tex]

and `s2` = 2.205

[tex]`s^2` = `((12-1)2.818^2 + (10-1)2.205^2)/(12+10-2)` = `((11)(7.95)+(9)(4.86))/20` = `6.2155`[/tex]

Therefore, [tex]`t= |9.583 - 9.9| / (6.2155[(1/12)+(1/10)])`= 0.3164[/tex]

The absolute value of the calculated test statistic is 0.3164

(c) The p-value of this test is

The p-value of this test is 0.76 (rounded to two decimal places)

This indicates that the p-value is higher than the significance level, 0.2.

Therefore, we fail to reject the null hypothesis and conclude that there is not enough evidence to suggest that one drug is more effective than the other at the 20% level of significance.

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The graph of f(x) = 1/2cos(x - π/2) -1 for -2π ≤ x ≤ 2π is a cosine curve with a maximum value of y = -1/2 and a minimum value of y = -3/2, shifted to the right by π/2 units and compressed vertically by a factor of 1/2.

To draw the graph of the function f(x) = 1/2cos(x - π/2) -1 for -2π ≤ x ≤ 2π, we first need to understand the transformations involved.

The function f(x) = cos(x) has a period of 2π and an amplitude of 1. The function f(x) = cos(x - π/2) is obtained by shifting the graph of f(x) = cos(x) to the right by π/2 units.

This means that the maximum value of f(x) = cos(x - π/2) occurs at x = 0, instead of x = π/2 as in f(x) = cos(x).

Multiplying the function by 1/2 compresses the graph vertically, which reduces the amplitude to 1/2. Finally, subtracting 1 from the function shifts the graph down by 1 unit.

Combining all these transformations, we can see that the graph of f(x) = 1/2cos(x - π/2) -1 for -2π ≤ x ≤ 2π is obtained by taking the graph of y = cos(x), shifting it to the right by π/2 units, compressing it vertically by a factor of 1/2, and then shifting it down by 1 unit.

To draw the graph, we can start with the graph of y = cos(x), which has a maximum value of 1 at x = 0 and a minimum value of -1 at x = π and x = -π. Shifting this graph to the right by π/2 units gives us a maximum value of 1 at x = π/2 and a minimum value of -1 at x = 3π/2 and x = -π/2.

     |

     |

 1.5 |                                      .

     |                              .

     |                    .

 1.0 |              .

     |       .

     | .

 0.5 x

     | .

     |       .

 0.0 |              .

     |                    .

     |                          .

-0.5 |                               .

     |                                      .

     |

-1.0 +-------------------------------------------------------

    -2π    -3π/2   -π    -π/2    0    π/2     π     3π/2     2π

Compressing this graph vertically by a factor of 1/2 reduces the maximum value to 1/2 and the minimum value to -1/2.  Finally, shifting the graph down by 1 unit moves the maximum value to y = -1/2 and the minimum value to y = -3/2.

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The expected frequency for the fourth category is 8.

To calculate the expected frequency for a particular category, we multiply the total number of observations by the expected proportion for that category. In this case, we have the observed frequencies for all four categories, but we need to determine the total number of observations.

To find the total number of observations, we sum up the observed frequencies for all categories:

Total number of observations = observed frequency of category 1 + observed frequency of category 2 + observed frequency of category 3 + observed frequency of category 4

In your case, the observed frequencies are as follows:

Observed frequency of category 1 = 23

Observed frequency of category 2 = 12

Observed frequency of category 3 = 34

Observed frequency of category 4 = 11

Substituting these values into the equation, we get:

Total number of observations = 23 + 12 + 34 + 11 = 80

Now that we know the total number of observations is 80, we can calculate the expected frequency for the fourth category using the null hypothesis proportions.

Expected frequency for category 4 = Total number of observations * Expected proportion for category 4

Expected proportion for category 4 = 10% = 0.10 (based on the null hypothesis)

Substituting the values into the equation, we have:

Expected frequency for category 4 = 80 * 0.10 = 8

Therefore, the expected frequency for the fourth category, according to the null hypothesis, is 8.

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Complete Question:

The table below lists the observed frequencies for all four categories for an experiment.

Category         Observed Frequency

1                                   23

2                                  12

3                                  34

4                                  11

The null hypothesis for the goodness-of-fit test is that 40% of all elements of the population belong to the first category, 30% belong to the second category, 20% belong to the third category, and 10% belong to the fourth category.

What is the expected frequency for the fourth category?

Answer:

I think the answer would be -2 . hope it helps u .

Among the given options, none of them correctly represent v₃ as a linearly independent vector.

To determine if a set of vectors is linearly independent,we can set up a linear combination equation where the   coefficients are variables.

In this case,we will introduce a   third vector v₃ = [a, b] and check if there exist non-zero values for a and b that satisfy the equation -

c₁ * v₁ + c₂ * v₂ + c₃ * v₃   = 0

where c₁, c₂, and c₃ are the coefficients  of the vectors.

Let's substitute the given   vectors into the equation  -

c₁ * [3, 2] + c₂ *   [-3, -2] + c₃ * [a, b]  = [0, 0]

[3c₁ -   3c₂ + ac₃, 2c1 - 2c₂ + bc₃] = [0, 0]

To determine if there exist   non-zero values for a and b that satisfy the equation,we can set up a system of equations -

3c₁ - 3c₂ +   ac₃ = 0

2c₁ - 2c₂ + bc₃ = 0

Solving   the system of equations, we find that for any non-zero values of a and b,the coefficients c₁, c₂, and c₃ must be zero to satisfy the equation.

This means that the   vectors v₁ = [3, 2] and v₂ = [-3, -2] are linearly independent.

Among the given options, none of them correctly represent v₃ as a linearly independent vector.

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Answer:

110 paper plates

Step-by-step explanation:

Given values:

1 Pack of paper cups (50) = 1.89

1 pack of paper plates (10) = 0.49

Anton has 15.00 to spend

If Anton were to buy 250 paper cups, that would cost him 5 packs since each pack is 50 cups. 5 packs would cost him 1.89 x 5 or 9.45. He now has 5.55 left to spend on paper plates.

Each pack of paper plates is 0.49. To find how many packs he can buy with the remaining amount of money, divide the money buy the cost.

5.55/0.49 ≈ 11.32

Since you can't buy .32 packs, Anton can buy 11 packs, or 110 paper plates.

Answer:

So the odds of flipping a coin 5 times and getting 5 heads are 1/2 ^5 (half to the power of 5). Which gives us 1/32 or just over a 3% chance.

Step-by-step explanation:

Answer:

11.375

Step-by-step explanation:

Answer:

4

Explanation:

The amount of calculation, if done by hand, will be quite effortful to input.

Answer:

angle w = 70 degrees

angle x = 60 degrees

angle y = 70 degrees

angle z = 60 degrees

Step-by-step explanation:

You need to understand the properties of supplementary angles and corresponding angles.

Answer:

I think that is right

Step-by-step explanation:

I hope that is useful for you :)

Answer:

1.0

Step-by-step explanation:

If you have any questions feel free to ask in the comments

Answer: 1.0

Step-by-step explanation:

Given:  "0.995 inches" ; round to the nearest TENTH of an inch.

(which means; round to the nearest "first decimal point" ; and to include the actual first decimal point (and that decimal point only, the "tenths place").

We are given the number:  "0.955" .  This is given to the nearest THOUSANDS.  

We examine the "tenths place":  "0.9" .  So we know our choices are:

"0.9" (round down to the nearest tenths place); or, "1.0" (round up to the nearest tenths place).  

           →  We examine the number: "0.955" ; and look to the next value, the "hundredths place".  

           → If that digit is 5 or greater (i.e. 5 to 9), we "round up" ; and the correct answer is:  "1.0" .  If that number is "4" or less (i.e. 0 to 4), we round down; and the correct answer is:  "0.9".

________________________________________

In the case of our give value:  "0.955" .  The digit to the right of "9" is "5" ; so, as previously mentioned, we "round up" ;  to:  "1.0".

Do not forget to include the units.

______________________________________________________

The answer is:  1.0 inches.

_________________________________________________

Answer:

I think option (d) is right answer

Answer:

c

Step-by-step explanation:

To determine the value of c such that the random variable cY follows a chi-squared (x²) distribution, we need to compare the moments of cY with the moments of a standard x² distribution.

The random variable Y is defined as Y = (X₁ + X₃ + X₅ + X₇)² + (X₂ + X₄ + X₆ + X₈)², where X₁, X₃, X₅, X₇, X₂, X₄, X₆, and X₈ are independent standard normal random variables.

The x² distribution is characterized by its degrees of freedom, which determine the shape and variability of the distribution. For a standard x² distribution, the degrees of freedom are equal to the number of squared independent standard normal variables that are summed together.

In this case, Y is a sum of squared independent standard normal variables. By expanding Y, we can see that it consists of two terms, each squared separately. Each term represents the sum of four independent standard normal variables. Therefore, each term follows a x² distribution with 4 degrees of freedom.

To make cY follow a x² distribution, we need to equate the degrees of freedom of cY with 4. By comparing the moments of cY with the moments of a standard x² distribution with 4 degrees of freedom, we can solve for the value of c. The moments of cY will depend on the value of c, and by setting them equal to the corresponding moments of a x² distribution, we can find the specific value of c.

In summary, to determine the value of c such that the random variable cY follows a x² distribution, we need to compare the moments of cY with the moments of a standard x² distribution. By equating the degrees of freedom and comparing the corresponding moments, we can find the value of c.

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