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3 years ago

Solve eight and two fourths minus three and one third

Mathematics

1 answer:

soldier1979 [14.2K]3 years ago

3 0

Answer:

5 1/6

Step-by-step explanation:

Rewriting our equation with parts separated

=8+2/4−3−1/3

Solving the whole number parts

8−3=5

Solving the fraction parts

2/4−1/3=?

Find the LCD of 2/4 and 1/3 and rewrite to solve with the equivalent fractions.

LCD = 12

6/12−4/12=2/12

Reducing the fraction part, 2/12,

2/12=1/6

Combining the whole and fraction parts

5+1/6=5 1/6

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15 points! The data set shows the weights of pumpkins, in pounds, that are chosen for a photograph in a farming magazine. The re

vladimir2022 [97]

Answer:

Step-by-step explanation:

a). Mean of the given data set = $\frac{32+28+18+40+22}{5}$

= 28

b). x $x-\overline{x}$ $(x-\overline{x})^2$

32 32-28 = 4 16

28 28 - 28 = 0 0

18 18 - 28 = -10 100

40 40 - 28 = 12 144

22 22 - 28 = -6 36

$\sum({x-\overline x})^2$ = 296

c). Standard deviation = $\sqrt{\frac{\sum (x-\overline{x})^2}{n}}$

= $\sqrt{\frac{296}{5} }$

= 7.69

d). Mean of the new set of weights = $\frac{32+33+34+35+36}{5}$

= 34

Since mean of the data set is more close to each data, standard deviation will be less than the deviation given in part (c).

7 0

3 years 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 $\left( {a + b} \right)(a+b)$ .