A 5 lb. bag of organic all-purpose flour costs $6.76. What is the unit price? _ per pound

Mathematics

2 answers:

Sonbull [250]3 years ago

7 0

for 5 lb. bag = $6.76

so, for 1 lb. bag = 6.76/5 = $1.35

So, unit prize is $1.35 per pound.

GrogVix [38]3 years ago

7 0

1.35 per pound to get that u need yo divide 6.76 by 5 you will then 1.352 multiply 1.35 with 5 and you will get 6.75 bug you are just one cent off.

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The ratio of the number of cars to the number of motorcycles in a parking lot is 10:3. The ratio of the number of motorcycles t

serg [7]

I'll do this step-by-step. Note, ratios are basically division signs.

The ratio of cars to motorcycles in a parking lot is 10:3, which means there are 10 cars for every 3 motorcycles.

The ratio of motorcycles to vans is 2:3, there are 2 motorcycles for every 3 vans.

There are 30 vans, so there are 20 motorcycles, since 20 motorcycles:30 vans is equal to 2 motorcycles: 3 vans in fractions. (20 / 30 = 2 / 3)

So, the ratio of cars represented by x to the number of motorcycles, 20 can be written as x / 20 = 10 / 3. 10 / 3 is 3.333, meaning 3.333 cars per 1 motorcycle, so multiply 20 by 3.333 to get x, which is 66. There are 66 cars and 30 vans, so there are 33 more cars than vans in the parking lot. 66/20 = 10/3, both have a ratio of 3.33 when divided.

If you don't want the explanation, there are 33 more cars than vans in the parking lot.

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

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