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Archy [21]
2 years ago
11

A large pot holds 12 gallons of soup Jared has 1-pint containers of chicken broth. Complete the table to help you find the numbe

r of 1-pint containers of chicken broth Jared will need to fill the pot.
Mathematics
2 answers:
lilavasa [31]2 years ago
8 0

Answer:

The number of 1-pint containers of chicken broth Jared will need to fill the pot is 96

Explanation;

The table is missing; however, I'll give the step by step explanation to get the measurements of a 1 pint container in terms of gallons.

Given

Size of pot = 12 gallons

To solve this problem, we need the relationship between pint and gallon measurements;

i.e what number of pint makes a gallon or vice versa.

The meaning of the question is that we want to get the equivalent of 12 gallon in pints

From US standard measurements;

1 liquid gallon = 8 liquid pints

If 1 liquid gallon = 8 liquid pints then

12 liquid gallon = 8 * 12 liquid pints

So,

12 liquid gallon = 96 liquid pints

The number of 1-pint containers of chicken broth Jared will need to fill the pot is 96

Rina8888 [55]2 years ago
7 0
There is 8 pints in 1 gallon so 12x8=96
96 pints to fill a 12 gallon pot
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sertanlavr [38]

Answer:

Y=x-1 is line n

y=-2x+4 is line m

y=4 is line t

x=-4 is line p

Step-by-step explanation:

5 0
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
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
2 years ago
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Arturiano [62]
D
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Stella [2.4K]

Answer:

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Step-by-step explanation:

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