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Tanya [424]
3 years ago
8

Verify the identity. 4 csc 2x = 2 csc2x tan x

Mathematics
1 answer:
vlada-n [284]3 years ago
6 0

Step-by-step explanation:

4csc(2x) = 2csc^2(x) tan(x)

We start with Left hand side

We know that csc(x) = 1/ sin(x)

So csc(2x) is replaced by 1/sin(2x)

4 \frac{1}{sin(2x)}

Also we use identity

sin(2x) = 2 sin(x) cos(x)

4 \frac{1}{2sin(x)cos(x)}

4 divide by 2 is 2

Now we multiply top and bottom by sin(x) because we need tan(x) in our answer

2\frac{1*sin(x)}{sin(x)cos(x)*sin(x)}

2\frac{sin(x)}{sin^2(x)cos(x)}

2\frac{1}{sin^2(x)} \frac{sin(x)}{cos(x)}

We know that sinx/ cosx = tan(x)

Also  1/ sin(x)= csc(x)

so it becomes 2csc^2(x) tan(x) , Right hand side

Hence verified



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The average salary of all residents of a city is thought to be about $39,000. A research team surveys a random sample of 200 res
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Answer:

z=\frac{40000-39000}{\frac{12000}{\sqrt{200}}}=1.179    

p_v =2*P(z>1.179)=2*[1-P(Z    

Step-by-step explanation:

Data given and notation    

\bar X=4000 represent the average score for the sample    

s=12000 represent the sample standard deviation    

n=200 sample size    

\mu_o =39000 represent the value that we want to test    

\alpha represent the significance level for the hypothesis test.    

z would represent the statistic (variable of interest)    

p_v represent the p value for the test (variable of interest)    

State the null and alternative hypotheses.    

We need to apply a two tailed test.    

What are H0 and Ha for this study?    

Null hypothesis:  \mu = 39000    

Alternative hypothesis :\mu \neq 39000    

Compute the test statistic  

The sample size is large enough to assume the distribution for the statisitc normal. The statistic for this case is given by:    

z=\frac{\bar X-\mu_o}{\frac{s}{\sqrt{n}}} (1)    

z-test: "Is used to compare group means. Is one of the most common tests and is used to determine if the mean is (higher, less or not equal) to an specified value".    

We can replace in formula (1) the info given like this:    

z=\frac{40000-39000}{\frac{12000}{\sqrt{200}}}=1.179    

Give the appropriate conclusion for the test  

Since is a two tailed test the p value would be:    

p_v =2*P(z>1.179)=2*[1-P(Z    

Conclusion    

If we compare the p value and a significance level given for example \alpha=0.05 we see that p_v>\alpha so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, then the true population mean for the salary not differs significantly from the value of 39000.    

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The junior class of Eastlake High School has 146 students, and 67 of them are girls. How many boys are there in the class?
Katena32 [7]

Hello!

In order to find how many boys there are in the class simply subtract 67 from 146:

146-67= 79

There are 79 boys in the class

I hope it helps!

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3 years ago
The time taken to deliver a pizza has a uniform probability distribution from 20 minutes to 60 minutes. What is the probability
Whitepunk [10]

Answer:

(1) The probability that the time to deliver a pizza is at least 32 minutes is 0.70.

(2a) The percentage of results more than 45 is 79.67%.

(2b) The percentage of results less than 85 is 91.77%.

(2c) The percentage of results are between 75 and 90 is 15.58%.

(2d) The percentage of results outside the healthy range 20 to 100 is 2.64%.

Step-by-step explanation:

(1)

Let <em>Y</em> = the time taken to deliver a pizza.

The random variable <em>Y</em> follows a Uniform distribution, U (20, 60).

The probability distribution function of a Uniform distribution is:

f(x)=\left \{ {{\frac{1}{b-a};\ x\in [a, b] } \atop {0};\ otherwise} \right.

Compute the probability that the time to deliver a pizza is at least 32 minutes as follows:

P(Y\geq 32)=\int\limits^{60}_{32} {\frac{1}{b-a} } \, dx \\=\frac{1}{60-20} \int\limits^{60}_{32} {1 } \, dx\\=\frac{1}{40}\times[x]^{60}_{32}\\=\frac{1}{40}\times[60-32]\\=0.70

Thus, the probability that the time to deliver a pizza is at least 32 minutes is 0.70.

(2)

Let <em>X</em> = results of a certain blood test.

It is provided that the random variable <em>X</em> follows a Normal distribution with parameters \mu = 60 and s = 18.

The probabilities of a Normal distribution are computed by converting the raw scores to <em>z</em>-scores.

The <em>z</em>-scores follows a Standard normal distribution, N (0, 1).

(a)

Compute the probability that the results are more than 45 as follows:

P(X>45)=P(\frac{X-\mu}{\sigma}> \frac{45-60}{18})=P(Z>-0.833)=P(Z

The percentage of results more than 45 is: 0.7967\times100=79.67\%

Thus, the percentage of results more than 45 is 79.67%.

(b)

Compute the probability that the results are less than 85 as follows:

P(X

The percentage of results less than 85 is: 0.9177\times100=91.77\%

Thus, the percentage of results less than 85 is 91.77%.

(c)

Compute the probability that the results are between 75 and 90 as follows:

P(75

The percentage of results are between 75 and 90 is: 0.1558\times100=15.58\%

Thus, the percentage of results are between 75 and 90 is 15.58%.

(d)

Compute the probability that the results are between 20 and 100 as follows:

P(20

Then the probability that the results outside the range 20 to 100 is: 1-0.9736=0.0264.

The percentage of results outside the range 20 to 100 is: 0.0264\times100=2.64\%

Thus, the percentage of results outside the healthy range 20 to 100 is 2.64%.

4 0
3 years ago
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