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3 years ago

Identify the reciprocal of each number. Type it in the blank, using a slash () to show a fraction,

Mathematics

1 answer:

amid [387]3 years ago

4 0

Answer: 6/5

Step-by-step explanation:

When finding the reciprocal, we essentially just "flip" or take the inverse of the number we are given. For example, the reciprocal of 1/2 would be 2/1 or 2. The given number multiplied by the reciprocal should always equal 1. If it does, then we know we are correct.

In this case, we are finding the reciprocal of 5/6, so we "flip" the numerator and the denominator to get 6/5.

To check our work, we need to multiply our original number and its reciprocal.

5/6 x 6/5 = 30/30 = 1

Now, we know we are correct, that the reciprocal of 5/6 is 6/5.

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Y varies directly with x,and y=7 when x=2. What is the value of y when x=5?

Travka [436]

Y=10 bc y-x=5 then 5+5= 10

3 0

3 years ago

Prove: If n is a positiveinteger and n2 is<br> divisible by 3, then n is divisible by3.

notka56 [123]

Answer:

If $n^2$ is divisible by 3, the n is also divisible by 3.

Step-by-step explanation:

We will prove this with the help of contrapositive that is we prove that if n is not divisible by 3, then, $n^2$ is not divisible by 3.

Let n not be divisible by 3. Then $\frac{n}{3}$ can be written in the form of fraction $\frac{x}{y}$ , where x and y are co-prime to each other or in other words the fraction is in lowest form.

Now, squaring

$\frac{n^2}{9} = \frac{x^2}{y^2}$

Thus,

$n^2 = \frac{9x^2}{y^2}$

$\frac{n^2}{3} = \frac{3x^2}{y^2}$

It can be clearly seen that the fraction $\frac{3x^2}{y^2}$ is in lowest form.

Hence, $n^2$ is not divisible by 3.

Thus, by contrapositivity if $n^2$ is divisible by 3, the n is also divisible by 3.

5 0

4 years ago

Billy ate 5/4 pizzas and John ate 5/3 pizzas. How much more pizza did John eat than Billy?

zaharov [31]

Answer:

Step-by-step explanation:

5/12

7 0

3 years ago

Read 2 more answers

A study was conducted to compare the proportion of drivers in Boston and New York who wore seat belts while driving. Data were c

In-s [12.5K]

(A.)test statistic for this test using Ha: p8くPN is Z = -2.164 (B.) p-value (3 decimal places) is 0.015. (C.) Based on this p-value, which of the following would you expect for a 95% is negative. (D.) the correct conclusion is The proportion of Boston drivers wearing seat was significantly lower than the proportion of new York drivers wearing seat belts.

A.)

According to given data find the test statistic for this test using Ha:

p1 = 0.581

p2 = 0.832

standard error = SE = 0.116

<h3>Test statistics :-</h3>

Z = $\frac{p1 - p2}{SE}$

Z = $\frac{0.581 - 0.832}{0.116}$ = -2.164

Z = -2.164

B.)

According to given Determine the p-value (3 decimal places).

<h3>p - value</h3>

it is left tailed test.

p - value for left tailed test is,

p - value = P(Z < test statistics )

p - value = P(Z < -2.164)

P - value = 0.015

C.)

Based on this p-value, which of the following would you expect for a 95%

confidence level = 95% = 0.95

significance level = α = 0.05

P -value is less than 0.05

so, reject null hypothesis at α = 0.05

That is $P_{B}$ < $P_{NY}$

so, based on P - value , All values in the 95% confidence interval

PB - PNY is negative.

D.)

According to given data What is the correct conclusion?

Reject null hypothesis at α = 0.05

so, $P_{B}$ < $P_{NY}$

Hence, The proportion of Boston drivers wearing seat was significantly lower than the proportion of new York drivers wearing seat belts.

know more about test statics visit this link

brainly.com/question/23332597

#SPJ4

3 0

1 year ago

A data mining routine has been applied to a transaction dataset and has classified 88 records as fraudulent (30 correctly so) an

chubhunter [2.5K]

Answer:

The classification matrix is attached below

Part a

The classification error rate for the records those are truly fraudulent is 65.91%.

Part b

The classification error rate for records that are truly non-fraudulent is 96.64%

Step-by-step explanation:

The classification matrix is obtained as shown below:

The transaction dataset has 30 fraudulent correctly classified records out of 88 records, that is, 30 records are correctly predicted given that an instance is negative.

Also, there would be 88 - 30 = 58 non-fraudulent incorrectly classified records, that is, 58 records are incorrectly predicted given that an instance is positive.

The transaction dataset has 920 non-fraudulent correctly classified records out of 952 records, that is, 920 records are correctly predicted given that an instance is positive.

Also, there would be 952 - 920 = 32 fraudulent incorrectly classified records, that is, 32 records incorrectly predicted given that an instance is negative.

That is,

Predicted value

Active value Fraudulent Non-fraudulent

Fraudlent 30 58

non-fraudulent 32 920

The classification matrix is obtained by using the information related to the transaction data, which is classified into fraudulent records and non-fraudulent records.

The error rate is obtained as shown below:

The error rate is obtained by taking the ratio of $\left( {b + c} \right)(b+c)$ and the total number of records.

The classification matrix is, shown above

The total number of records is, $30 + 58 + 32 + 920 = 1,040$

The error rate is,

$\begin{array}{c}\\{\rm{Error}}\,{\rm{rate}} = \frac{{b + c}}{{{\rm{Total}}}}\\\\ = \frac{{58 + 32}}{{1,040}}\\\\ = \frac{{90}}{{1,040}}\\\\ = 0.0865\\\end{array}$

The percentage is $0.0865 \times 100 = 8.65$

(a)

The classification error rate for the records those are truly fraudulent is obtained by taking the rate ratio of b and $\left( {a + b} \right)(a+b)$ .

The classification error rate for the records those are truly fraudulent is obtained as shown below:

The classification matrix is, shown above and in the attachment

The error rate for truly fraudulent is,

$\begin{array}{c}\\FP = \frac{b}{{a + b}}\\\\ = \frac{{58}}{{30 + 58}}\\\\ = \frac{{58}}{{88}}\\\\ = 0.6591\\\end{array}$

The percentage is, $0.6591 \times 100 = 65.91$

(b)

The classification error rate for records that are truly non-fraudulent is obtained by taking the ratio of d and $\left( {c + d} \right)(c+d)$ .

The classification error rate for records that are truly non-fraudulent is obtained as shown below:

The classification matrix is, shown in the attachment

The error rate for truly non-fraudulent is,

$\begin{array}{c}\\TP = \frac{d}{{c + d}}\\\\ = \frac{{920}}{{32 + 920}}\\\\ = \frac{{920}}{{952}}\\\\ = 0.9664\\\end{array}$

The percentage is, $0.9664 \times 100 = 96.64$

8 0

3 years ago