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hammer [34]
3 years ago
11

HELP!!!! this is very confusing I understand but I can't find the mistake or the right quotient.

Mathematics
1 answer:
Scorpion4ik [409]3 years ago
3 0
Part A:
Aaron forgot to turn the second fraction <span>upside down.
</span>
Part B:
4: \frac{1}{4}=4* \frac{4}{1}= \frac{4*4}{1}=16
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Answer:

59/8 or 7 3/8
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13 liters is approximately
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It is approximately 3.5 gallons.
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A study of consumer smoking habits includes A people in the 18-22 age bracket (B of whom smoke), C people in the 23-30 age brack
madreJ [45]

The correct question is:

A study of consumer smoking habits includes 167 people in the 18-22 age bracket (59 of whom smoke), 148 people in the 23-30 age bracket (31 of whom smoke), and 85 people in the 31-40 age bracket (23 of whom smoke). If one person is randomly selected from this sample, find the probability of getting someone who is age 23-30 or smokes

Answer:

The probability of getting someone who is age 23-30 or smokes = 0.575

Step-by-step explanation:

We are given;

Number consumers of age 18 - 22 = 167

Number of consumers of ages 22 - 30 = 148

Number of consumers of ages 31 - 40 = 85

Thus,total number of consumers in the survey = 167 + 148 + 85 = 400

We are also given;

Number consumers of age 18 - 22 who smoke = 59

Number of consumers of ages 22 - 30 who smoke = 31

Number of consumers of ages 31 - 40 who smoke = 23

Total number of people who smoke = 59 + 31 + 23 = 113

Let event A = someone of age 23-30 and event B = someone who smokes. Thus;

P(A) = 148/400

P(B) = 113/400

P(A & B) = 31/400

Now, from addition rule in sets which is given by;

P(A or B) = P (A) + P (B) – P (A and B)

We can now solve the question.

Thus;

P(A or B) = (148/400) + (113/400) - (31/400)

P(A or B) = 230/400 = 0.575

4 0
3 years ago
Assumptions of a regression model can be evaluated by plotting and analyzing the ____________
Gala2k [10]

The assumptions of a regression model can be evaluated by plotting and analyzing the error terms.

Important assumptions in regression model analysis are

  1. There should be a linear and additive relationship between dependent (response) variable and independent (predictor) variable(s).
  2. There should be no correlation between the residual (error) terms. Absence of this phenomenon is known as auto correlation.
  3. The independent variables should not be correlated. Absence of this phenomenon is known as multi col-linearity.
  4. The error terms must have constant variance. This phenomenon is known as homoskedasticity. The presence of non-constant variance is referred to heteroskedasticity.
  5. The error terms must be normally distributed.

Hence we can conclude that the assumptions of a regression model can be evaluated by plotting and analyzing the error terms.

Learn more about regression model here

brainly.com/question/15408346

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4 0
2 years ago
If there are 10 billion transistors (each of a square shape) uniformly (without space) distributed on a silicon chip with the sq
Karo-lina-s [1.5K]

Answer:

the transistors have L=1 mm of linear size

Step-by-step explanation:

For the silicon chip the area is A=1 cm² and for the transistors the area is At=L² (L=linear size) . Then since N= 10 billion transistors of area At should fit in the area A

A=N*At

A=N*L²

solving for L

L= √(A/N)

Assuming that 1 billion=10⁹ (short scale version of billion), then

L= √(A/N) = √(1 cm²/10⁹) = 1 cm / 10³ = 1 mm

then the transistors have L=1 mm of linear size

4 0
3 years ago
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