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dimaraw [331]
3 years ago
15

Now that we have our linear regression model, let’s try to make a prediction for the sales given a new set of advertising budget

s as follows: new.dat <- data.frame(TV=200, Radio=10, Newspaper=20) You are required to report the predicted sales as well as the lower and upper bound for the 95% prediction interval. What will you report?
Mathematics
1 answer:
ValentinkaMS [17]3 years ago
5 0

Answer:

The predicted sales for the new set of advertising budgets is 14.

Step-by-step explanation:

The linear regression model is:

\text{Sales}=2.9389+0.0458\cdot(\text{TV})+0.1885\cdot(\text{Radio})-0.0010\cdot(\text{Newspaper})

Compute the value of sales for:

TV = 200,

Radio = 10,

Newspaper = 20

\text{Sales}=2.9389+0.0458\cdot(\text{TV})+0.1885\cdot(\text{Radio})-0.0010\cdot(\text{Newspaper})

        =2.9389+0.0458\cdot(200)+0.1885\cdot(10)-0.0010\cdot(20)\\=2.9389+9.16+1.885-0.0002\\=13.9837\\\approx 14

Thus, the predicted sales for the new set of advertising budgets is 14.

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Find the value of x from the equation
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Assume that the weights of quarters are normally distributed with a mean of 5.67 g and a standard deviation of 0.07 g. If a vend
Black_prince [1.1K]

Answer:

0.98046

Step-by-step explanation:

Given:

  • μ = 5.67
  • σ = 0.07

Here we are required to find

P(5.48 <X<5.82) and between 5.48 and 5.82 we each z-score given by

<em>z = (x - μ) / σ</em>

So 5.48 we have z = -2.714  and 5.82 we have  z = 2.143

Therefore we have the area of interest on the normal distribution chart given by :

P( -2,714 < z < 2.143)

= 1 - P(Z<-2.714) + P(Z >  2.143)

= 1 - P(Z<-2.714) + (1 - P(Z <  2.143))

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6 0
4 years ago
Read 2 more answers
P(A)* P(B)≠ p(AnB)<br><br> Why does it contain a not equal to sign
RoseWind [281]

Answer:

P(A|D) and P(D|A) from the table above are not equal because P(A|D) =  and P(D|A) =

Step-by-step explanation:

Conditional probability is the probability of one event occurring  with some relationship to one or more other events

.

P(A|D) is called the "Conditional Probability" of A given D

P(D|A) is called the "Conditional Probability" of D given A

The formula for conditional probability of P(A|D) = P(D∩A)/P(D)

The formula for conditional probability of P(D|A) = P(A∩D)/P(A)

The table

              ↓         ↓       ↓

           :  C     :   D    : Total

→ A      :  6     :    2    :   8

→ B      :  1      :    8    :   9

→Total :  7     :    10  :  17

∵ P(A|D) = P(D∩A)/P(D)

∵ P(D∩A) = 2 ⇒ the common of D and A

- P(D) means total of column D

∵ P(D) = 10

∴ P(A|D) =

∵ P(D|A) = P(A∩D)/P(A)

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- P(A) means total of row A

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∴ P(D|A) =

∵ P(A|D) =

∵ P(D|A) =

∵   ≠  

∴ P(A|D) and P(D|A) from the table above are not equal

Step-by-step explanation:

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