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3 years ago

Find two numbers that round to 35.4 when rounded to the nearest tenth.Write the numbers in the box.

Mathematics

2 answers:

hodyreva [135]3 years ago

4 0

35.39, 35.38, 35.37, 35.36 are some numbers that round to 35.4 when rounded to the nearest tenth

Marrrta [24]3 years ago

4 0

Answer: The two numbers that round to 35.4 when rounded to the nearest tenth are 35.38 and 35.41.

Step-by-step explanation: We are given to find two numbers that round to 35.4 when rounded to the nearest tenth.

We know that

the numbers that will round to 35.4 will lie from 35.35 to 35.44.

That is, the numbers are 35.35, 35.36, 35.37, 35.38, 35.39, 35.41, 35.42, 35.43 and 35.44.

Therefore, two of the numbers are 35.38 and 35.41.

Thus, the two numbers that round to 35.4 when rounded to the nearest tenth are 35.38 and 35.41.

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Solve the math problem

luda_lava [24]

Answer: a) y=-29x-20 b) y=3/5x-18

Step-by-step explanation:

a) m = (-49-38)/(1 - (-2)) = -87/3 = -29

y=-29x+b --> Substitute (1,-49) as a point

(-49)=-29(1)+b

-49=-29+b

-b=-29+49

b=-20

Therefore, the equation of the line is y=-29x-20

b) m = (-18-(-9)/(0-15)) = -9/-15 = 3/5

The y-intercept is (0,-18) as it's where the line intersects the y-axis and x equals 0

Therefore, the equation of the line is y=3/5x-18

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2 years ago

Which is a rational number? A.9 B. 15 C. 28 D. 52

Law Incorporation [45]

A rational number is a number that can be expressed as a fraction, therefore all integers are rational numbers

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2 years ago

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

Firlakuza [10]

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, $0.9664 \times 100 = 96.64$

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sweet [91]

Answer:

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Step-by-step explanation:

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