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inna [77]
3 years ago
7

On a coordinate plane, a line goes through (0, negative 3) and (3, negative 2). A point is at (negative 4, 2).

Mathematics
1 answer:
bulgar [2K]3 years ago
5 0

Answer:

Correct option: first one -> y = (1/3)x + (10/3)

Step-by-step explanation:

The linear function that represents a line is:

y = ax + b

Where a is the slope and b is the y-intercept.

First we need to find the slope of the line that goes through (0, -3) and (3, -2).

Using both points, we can find the equation of the line:

x = 0 -> y = -3

-3 = a*0 + b

b = -3

x = 3 -> y = -2

-2 = 3a - 3

3a = 1

a = 1/3

The parallel line needs to have the same slope as the line, so we can model the parallel line with the following equation:

y = (1/3)x + b

The parallel line goes through the point (-4, 2), so we have:

x = -4 -> y = 2

2 = (1/3)*(-4) + b

b = 2 + (4/3)

b = 10/3

So the equation of the parallel line is:

y = (1/3)x + (10/3)

Correct option: first one

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Answer:

3.

Step-by-step explanation:

Let's break it down.

Sparkles used 12 balloons total to make 4 balloon animals. Divide 12 by 4, and you will get 3.

What I did for part 2 of the equation, is I multiplied 4 and 5 by 3 to ensure the variable matched my calculations. 4x3 is 12, and 5x3 is 15. add 12 and 15, and you will get 27 total balloons.

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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

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Answer:

It is either C or D.

Step-by-step explanation:

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Dora goes to a store and buys a phone originally priced at $45. The store is running a discount of 18%. What is the price of the
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Answer: 8.1 $

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2 years ago
The per capita growth rate of many species varies temporally for a variety of reasons, including seasonality and habitat destruc
Veronika [31]

Answer:

n(t)=n_0e^{(1-e^{-t }-t)}

Step-by-step explanation:

If n(t) represents the population size at time t, where n is measured in individuals and t is measured in years.

\frac{dn}{dt}=n(e^{-t }-1), n(0)=n_o

\frac{dn}{n}=(e^{-t }-1)dt

Taking the integral of both sides

\int\frac{dn}{n}=\int(e^{-t }-1)dt\\\int\frac{dn}{n}= \int e^{-t }dt-\int1dt

ln |n| = -e^{-t }-t+C

Where C is integration constant

Taking the exponential of both sides

n=e^{(-e^{-t }-t+C)}

n=e^{(-e^{-t }-t)}e^C\\n=Ke^{(-e^{-t }-t)} whee the exponential of a constant is a constant K.

When t=0, n(0)=n_o

n_0=Ke^{-1

Therefore:

n=n_0e^{1}e^{(-e^{-t }-t)}

n(t)=n_0e^{(1-e^{-t }-t)}

7 0
3 years ago
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