The temperature at noon was -6°c.By midnight, the temperature dropped 9°c.which exprssion represent the temperature at midnight

if the doctor orders 1 dram of medication to be given and the vial states that there is 1 mg in 1 cc you must do what

adelina 88 [10]

First, you need to convert all units to calculate what dose the doctor ordered.

1 dram = 4 mL
So, it must be given 4 mL of medication

1 cc = 1 mL
So, if t<span>he vial states that there is 1 mg in 1 cc </span>, that means the concentration is 1 mg in 1 mL or, 1 mg/mL.

Now, we need to calculate what is the dose of medication in 4 mL:
1 mg is in 1 mL, how many mg is in 4 mL:
1 mg : 1 mL = x : 4 mL

Crossing the products:
x · 1 = 1 mg · 4
⇒ x = 4 mg

8 0

4 years ago

For each question below, use the function f (x) = x3

givi [52]

Answer:

A. (-∞ , 0)

B. x = 0 triple root

C. The function tends to infinity

Step-by-step explanation:

For the function f(x) = x^3 we have:

Negative intervals:

(-∞ , 0)

The roots of this function is:

x = 0

The final behavior of this function tends toward infinity

A graphic is attached below

5 0

3 years ago

Read 2 more answers

The velocity of a particle moving along the x-axis is v(t) = t2 + 2t + 1, with t measured in minutes and v(t) measured in feet p

kkurt [141]

V(t)=t^2+2t+1 integrating we get d(t)

d(t)=t^3/3+t^2+t or more neatly

d(t)=(t^3+3t^2+3t)/3 so

d(2)=(8+12+6)/3

d(2)=26/3

So the particle moves 9 feet (to the nearest foot) in the first two minutes.

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

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

3 years ago

PLEASE HELP I WILL MARK YOU BRAINLIEST. To feed his plant, Aaron creates a mixture that requires a 3/8 cup of plant food for eve

SpyIntel [72]

Answer: Aaron would need 3.9375 cups of food

Step-by-step explanation: SImply, just multiply 10.5 by .375. And you get 3.9375.

6 0

3 years ago