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ExtremeBDS [4]
3 years ago
14

NEED HELP ASAP GIVING BRAINLIST!!!!!!!!

Mathematics
2 answers:
zzz [600]3 years ago
5 0

Answer:

81

27

48

77

Step-by-step explanation:

please brainliest me

Marysya12 [62]3 years ago
3 0

Answer:

81, 27, 48, 77

Step-by-step explanation:

You might be interested in
WILL MARK BRAINLIEST The amount (A) that principal (P) will be worth after t years at interest rate (r) compounded annually is
alex41 [277]

Answer:

15 years

Step-by-step explanation:

Since we're looking for time, we can use the formula t =  A / Pr, which would be:
t = 6000/(7255 x 5.5%)
Converting the percentage into decimal form
t = 6000/(7255 x 0.055)
We can then solve to get:
t = 6000/399.025
t = 15.03665183...
Meaning it would take 15 years for an interest of 5.5% on $6,000 to reach $7,255. Not very worth $1,255 extra if you need that 6k for rent.

4 0
2 years ago
Jane will rent a car for the weekend. She can choose one of two plans. The first plan has an initial fee of $55 and costs an add
pishuonlain [190]
She will need to drive 110 miles to be the same
4 0
3 years ago
(100 points if answered) (attatched screenshot) Algebra II need question 1 and 2
Elanso [62]
Domain: (-♾,2)U(2,♾), {x | x ≠2}
Range: (-♾,3) U(3,♾), {y | y ≠ 3}

Sorry that’s all I can help with


3 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) .

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
Assume a researcher recruits 150 African American and Caucasian individuals taking warfarin to determine if there is a differenc
Anettt [7]

Answer:

Z=11.0

Step-by-step explanation:

Let Caucasian individuals be the population 1 and African American be the population 2. So, we are given that

n=150

n1=75

mean1=xbar1=6.1.

standard deviation1=S.D1=σ1=1.1.

Variance1=V(x1)=σ1²=1.1²=1.21.

n2=75.

mean2=xbar2=4.3.

standard deviation2=S.D2=σ2=0.9.

Variance2=V(x2)=σ2²=0.9²=0.81.

The z-statistic is

Z=\frac{xbar1-xbar2}{\sqrt{\frac{V(x1)}{n1}+ \frac{V(x2)}{n2} } }

Z=\frac{6.1-4.3}{\sqrt{\frac{1.21}{75}+ \frac{0.81}{75} } }

Z=\frac{1.8}{\sqrt{0.0161+ 0.0108 } }

Z=\frac{1.8}{\sqrt{0.0269 } }

Z=\frac{1.8}{0.164 } }

Z=10.97

Z=11.0

So, the z value obtained while calculating the test statistic is approximately 11.0.

4 0
3 years ago
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