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2 years ago

How to round 831,756 nears hundred thousand

Mathematics

1 answer:

Anettt [7]2 years ago

4 0

Answer:

800,000

Step-by-step explanation:

If the number was above 850,000 it would round up. If the number was 849,999 or below it would round down. In other words, you look at the number to the right of the hundred thousands place, or the number in the tens of thousands place, and if it is 5 or above you round up. 4 and below you round down.

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Can you help me????????

aev [14]

Check the picture below.

now, if AC is that much, recall, BD is the midsegment, thus AB = BC, so AB is just one of the equal halves of AC, so, AB is AC/2.

8 0

3 years ago

The description for a certain brand of house paint claims a coverage of 460 ft2/gal. (a) express this quantity in square meters

Angelina_Jolie [31]

Given 460 $ft^2/gal$

a) convert it into $m^2/l$

=> It means $ft^2 -> m^2$ and $gal -> l$

1 ft = 0.3048 m

$1 ft^2 = 0.092903 m^2$

1 gal = 3.78541 l

$460ft^2= [tex]460\frac{ft^2}{gal} = \frac{42.7354m^2}{3.78541l} = 11.2895m^2/l$ m^2[/tex].

b. $11.2895\frac{m^{2} }{l} *1000\frac{l}{m^3} =11289.5 m^{-1}$

c.Given= $460\frac{ft^2}{gal}$

To find inverse, divide it by 1.

Inverse- $\frac{1}{460} \frac{gal}{ft^2}$

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3 years ago

Find the equation of the line

kirill115 [55]

Answer:

Step-by-step explanation:

The line is passing through 2 points: (0,9 and (3,3)

slope=(9-3)/(0-3)=-2

equation is : y-9=-2(x-0) or y=-2x+9

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3 years ago

Solve this equation for x x-2y=8

nignag [31]

Im pretty sure the answer is x=-2

8 0

3 years ago

Read 2 more answers

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, $0.9664 \times 100 = 96.64$

6 0

3 years ago