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1. An estate agent realises that prospective buyers are looking to buy larger houses that can accommodate an extended family. In a small study of 150 properties, he recorded the last year's sales, considering the duration from the date of entering the market up to the sale date. He denoted this duration as 'on market' and recorded it in months. He also recorded the size in square meters of each of the houses that were sold and the previous selling price. A partial data set is given below. Table: SALE Sale 0 1 1 0 On market (in months) 13 X₂ = SIZE X₂= PREPRICE 4 3 8 Size (in 100sq. meters) 1.15 2.32 3.50 1.30 The data of SALE was derived by using the following coding scheme X₁ MONTHS PrePrice (in R1000.00) 950 500 600 800 P = SALE (1= quick sale and 0= slow sale) The estate agent modelled the data with a logistic regression function and SAS provided the following maximum likelihood estimates of Bo. Bland B₂. bo=1.177, b₁ = -0.073 b₂ = 0.098 and b₂ = 0.005 1.1. Interpret all the estimated regression coefficients. 1.2. Find the odds ratio for b,and b₂ and give an interpretation of each. 1.3. What is the estimated probability of quick sale for a house that has been on the market for 3 months and has a size of 200 sq. meters and sold previously for R360 000.00? 1.4. Refer to Question 1.3. Find the odds of 3 months and then for 4 months and explain if sq m is still 200 and the previous price is still R360 000.00. Comment on the results. 1.5. Estimate the square meters of a house that is on the market for 2 months and the previous price was R300 000.00 for which there is an 80% chance of having a quick sale.

1. An estate agent realises that prospective buyers are looking to buy larger houses that can accommodate an extended family. In a small study of 150 properties, he recorded the last year's sales, considering the duration from the date of entering the market up to the sale date. He denoted this duration as 'on market' and recorded it in months. He also recorded the size in square meters of each of the houses that were sold and the previous selling price. A partial data set is given below. Table: SALE Sale 0 1 1 0 On market (in months) 13 X₂ = SIZE X₂= PREPRICE 4 3 8 Size (in 100sq. meters) 1.15 2.32 3.50 1.30 The data of SALE was derived by using the following coding scheme X₁ MONTHS PrePrice (in R1000.00) 950 500 600 800 P = SALE (1= quick sale and 0= slow sale) The estate agent modelled the data with a logistic regression function and SAS provided the following maximum likelihood estimates of Bo. Bland B₂. bo=1.177, b₁ = -0.073 b₂ = 0.098 and b₂ = 0.005 1.1. Interpret all the estimated regression coefficients. 1.2. Find the odds ratio for b,and b₂ and give an interpretation of each. 1.3. What is the estimated probability of quick sale for a house that has been on the market for 3 months and has a size of 200 sq. meters and sold previously for R360 000.00? 1.4. Refer to Question 1.3. Find the odds of 3 months and then for 4 months and explain if sq m is still 200 and the previous price is still R360 000.00. Comment on the results. 1.5. Estimate the square meters of a house that is on the market for 2 months and the previous price was R300 000.00 for which there is an 80% chance of having a quick sale.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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1. An estate agent realises that prospective buyers are looking to buy larger houses that can accommodate an extended family. In a small study of 150 properties, he recorded the last year's sales, considering the duration from the date of entering the market up to the sale date. He denoted this duration as 'on market' and recorded it in months. He also recorded the size in square meters of each of the houses that were sold and the previous selling price. A partial data set is given below. Table: SALE Sale 0 1 1 0 On market (in months) 13 X₂ = SIZE X3 = PREPRICE 4 3 8 Size (in 100sq. meters) 1.15 The data of SALE was derived by using the following coding scheme X₁ = MONTHS 2.32 3.50 1.30 1.1. Interpret all the estimated regression coefficients. PrePrice (in R1000.00) 950 500 600 800 Y = SALE (1= quick sale and 0 = slow sale) The estate agent modelled the data with a logistic regression function and SAS provided the following maximum likelihood estimates of Bo. Bland B₂. bo = -1.177, b₁ = -0.073 b₂ = 0.098 and b3 = 0.005 1.2. Find the odds ratio for b,and b₂ and give an interpretation of each. 1.3. What is the estimated probability of quick sale for a house that has been on the market for 3 months and has a size of 200 sq. meters and sold previously for R360 000.00? 1.4. Refer to Question 1.3. Find the odds of 3 months and then for 4 months and explain if sq m is still 200 and the previous price is still R360 000.00. Comment on the results. 1.5. Estimate the square meters of a house that is on the market for 2 months and the previous price was R300 000.00 for which there is an 80% chance of having a quick sale.
Transcribed Image Text:1. An estate agent realises that prospective buyers are looking to buy larger houses that can accommodate an extended family. In a small study of 150 properties, he recorded the last year's sales, considering the duration from the date of entering the market up to the sale date. He denoted this duration as 'on market' and recorded it in months. He also recorded the size in square meters of each of the houses that were sold and the previous selling price. A partial data set is given below. Table: SALE Sale 0 1 1 0 On market (in months) 13 X₂ = SIZE X3 = PREPRICE 4 3 8 Size (in 100sq. meters) 1.15 The data of SALE was derived by using the following coding scheme X₁ = MONTHS 2.32 3.50 1.30 1.1. Interpret all the estimated regression coefficients. PrePrice (in R1000.00) 950 500 600 800 Y = SALE (1= quick sale and 0 = slow sale) The estate agent modelled the data with a logistic regression function and SAS provided the following maximum likelihood estimates of Bo. Bland B₂. bo = -1.177, b₁ = -0.073 b₂ = 0.098 and b3 = 0.005 1.2. Find the odds ratio for b,and b₂ and give an interpretation of each. 1.3. What is the estimated probability of quick sale for a house that has been on the market for 3 months and has a size of 200 sq. meters and sold previously for R360 000.00? 1.4. Refer to Question 1.3. Find the odds of 3 months and then for 4 months and explain if sq m is still 200 and the previous price is still R360 000.00. Comment on the results. 1.5. Estimate the square meters of a house that is on the market for 2 months and the previous price was R300 000.00 for which there is an 80% chance of having a quick sale.
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