What is the operation used for the following terms: times, product, twice, etc.

House price y is estimated as a function of the square footage of a house x and a dummy variable d that equals 1 if the house ha

tresset_1 [31]

Answer:

a-1. The predicted price of a house with ocean views and square footage of 2,000 is $411,500.00.

a-2. The predicted price of a house with ocean views and square footage of 3,000 is $531,500.00.

b-1. The predicted price (in $1,000s) of a house without ocean views and square footage of 2,000 is $358,900.

b-2. The predicted price of a house without ocean views and square footage of 3,000 is $478,900.00.

c. The correct option is An ocean view increases the value of a house by approximately $52,600.

Step-by-step explanation:

Given:

yˆ = 118.90 + 0.12x + 52.60d ………………. (1)

a-1. Compute the predicted price (in $1,000s) of a house with ocean views and square footage of 2,000. (Round intermediate calculations to at least 4 decimal places. Round your answer to 2 decimal places.)

This implies that we have:

x = 2,000

d = 1

Substituting the values into equation (1), we have:

yˆ = 118.90 + (0.12 * 2000) + (52.60 * 1) = 411.50

Since the predicted price is in $1,000s, we have:

yˆ = 411.50 * $1000

yˆ = $411,500.00

Therefore, the predicted price of a house with ocean views and square footage of 2,000 is $411,500.00.

a-2. Compute the predicted price (in $1,000s) of a house with ocean views and square footage of 3,000. (Round intermediate calculations to at least 4 decimal places. Round your answer to 2 decimal places.)

This implies that we have:

x = 3,000

d = 1

Substituting the values into equation (1), we have:

yˆ = 118.90 + (0.12 * 3,000) + (52.60 * 1) = 531.50

Since the predicted price is in $1,000s, we have:

yˆ = 531.50 * $1000

yˆ = $531,500.00

Therefore, the predicted price of a house with ocean views and square footage of 3,000 is $531,500.00.

b-1. Compute the predicted price (in $1,000s) of a house without ocean views and square footage of 2,000. (Round intermediate calculations to at least 4 decimal places. Round your answer to 2 decimal places.)

This implies that we have:

x = 2,000

d = 0

Substituting the values into equation (1), we have:

yˆ = 118.90 + (0.12 * 2000) + (52.60 * 0) = 358.90

Since the predicted price is in $1,000s, we have:

yˆ = 358.90 * $1000

yˆ = $358,900.00

Therefore, the predicted price of a house without ocean views and square footage of 2,000 is $358,900.00.

b-2. Compute the predicted price (in $1,000s) of a house without ocean views and square footage of 3,000. (Round intermediate calculations to at least 4 decimal places. Round your answer to 2 decimal places.)

This implies that we have:

x = 3,000

d = 0

Substituting the values into equation (1), we have:

yˆ = 118.90 + (0.12 * 3,000) + (52.60 * 0) = 478.90

Since the predicted price is in $1,000s, we have:

yˆ = 478.90 * $1000

yˆ = $478,900.00

Therefore, the predicted price of a house without ocean views and square footage of 3,000 is $478,900.00.

c. Discuss the impact of ocean views on the house price.

Since the coefficient of d in equation (1) is 52.60 and positive, and the predicted price is in $1,000s; the correct option is An ocean view increases the value of a house by approximately $52,600.

3 0

2 years ago

The area of Norway is 24% more than the area of Colorado so Norway's area is-------% of Colorado's area

zhenek [66]

Possibly 124%.........

8 0

2 years ago

Suppose you are an expert on the fashion industry and wish to gather information to compare the amount earned per month by model

Ann [662]

Answer:

(1) The degrees of freedom for unequal variance test is (14, 11).

(2) The decision rule for the 0.01 significance level is;

If the value of our test statistics is less than the critical values of F at 0.01 level of significance, then we have insufficient evidence to reject our null hypothesis.

If the value of our test statistics is more than the critical values of F at 0.01 level of significance, then we have sufficient evidence to reject our null hypothesis.

(3) The value of the test statistic is 0.3796.

Step-by-step explanation:

We are given that you are an expert on the fashion industry and wish to gather information to compare the amount earned per month by models featuring Liz Claiborne's attire with those of Calvin Klein.

The following is the amount ($000) earned per month by a sample of 15 Claiborne models;

$3.5, $5.1, $5.2, $3.6, $5.0, $3.4, $5.3, $6.5, $4.8, $6.3, $5.8, $4.5, $6.3, $4.9, $4.2 .

The following is the amount ($000) earned by a sample of 12 Klein models;

$4.1, $2.5, $1.2, $3.5, $5.1, $2.3, $6.1, $1.2, $1.5, $1.3, $1.8, $2.1.

(1) As we know that for the unequal variance test, we use F-test. The degrees of freedom for the F-test is given by;

$\text{F}_(_n__1-1, n_2-1_)$

Here, $n_1$ = sample of 15 Claiborne models

$n_2$ = sample of 12 Klein models

So, the degrees of freedom = ( $n_1-1, n_2-1$ ) = (15 - 1, 12 - 1) = (14, 11)

(2) The decision rule for 0.01 significance level is given by;

If the value of our test statistics is less than the critical values of F at 0.01 level of significance, then we have insufficient evidence to reject our null hypothesis.

If the value of our test statistics is more than the critical values of F at 0.01 level of significance, then we have sufficient evidence to reject our null hypothesis.

(3) The test statistics that will be used here is F-test which is given by;

T.S. = $\frac{s_1^{2} }{s_2^{2} } \times \frac{\sigma_2^{2} }{\sigma_1^{2} }$ ~ $\text{F}_(_n__1-1, n_2-1_)$