Part A The next questions are in relation to recent study of house prices in Sydney. The variables investigated are: 1 SalePrice Selling Price in Thousands of dollars 2 Distance Distance from Sydney CBD in Kilometers 3 LandSize Land size in square meters 4 Building Area Building Area Construction in square meters Research Question: Is there a relation between Price of the house and land size? The output below presents the relation between Price of the house (in thousands of dollars) and land size.The >results1 <- lm(SalePrice ~ LandSize) >results1 Call: lm(formula = SalePrice ~ LandSize) Coefficients: (Intercept) LandSize 493.4233 1.5821 >summary(results1) Call: lm(formula = SalePrice ~ LandSize) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 493.42 119.47 4.13 0.0001 LandSize 1.5821 0.1926 **** ***** Residual standard error: 483.86 on 182 degrees of freedom Multiple R-squared: 0.52, Adjusted R-squared: 0.2665 - What is the p-value? What is the correct decision for this test? (Choose one from below) -Since p-value < 0.05, do not reject H0 -Since p-value < 0.05, reject H0 -Since p-value > 0.05, do not reject H0 -Since p-value > 0.05, reject H0 -An appropriate conclusion for this test is: (Choose one from below) -There is a significant positive linear relation between PS and LS and If PS increases by 1 unit, we expect LS to increase by 1.58 units, on average. -There is a significant negative linear relation between PS and LS and If LS increases by 1 unit, we expect PS to increase by 1.58 dollars, on average. -There is a significant positive linear relation between SP and LS and If LS increases by 1 unit, we expect PS to increase by 493.42 dollars, on average. -There is a significant positive linear relation between SP and LS and If LS increases by 1 unit, we expect SP to increase by 1.58 dollars, on average. - Calculate the value of the correlation coefficient? (2 decimal places) = - How would you interpret the correlation coefficient. (Choose one from below) -There is a very strong negative linear relation between PS and LS -There is a moderate strong positive linear relation between PS and LS -There is a very strong positive non-linear relation between PS and LS -There is a very strong negative non-linear relation between PS and LS -There is a weak positive linear relation between PS and LS - Can we use the regression to predict LS when PS is 1500? (Choose one from below) -Yes, we can make prediction as the value 1500 is within the range of the data. -No, we can only predict PS from LS no the other way round. -No, because we did not reject the null hypothesis - No, we can't make prediction as there is no linear relation. - Predict the PS if LS is 450. ( 2 decimal places) =

Part A The next questions are in relation to recent study of house prices in Sydney. The variables investigated are: 1 SalePrice Selling Price in Thousands of dollars 2 Distance Distance from Sydney CBD in Kilometers 3 LandSize Land size in square meters 4 Building Area Building Area Construction in square meters Research Question: Is there a relation between Price of the house and land size? The output below presents the relation between Price of the house (in thousands of dollars) and land size.The >results1 <- lm(SalePrice ~ LandSize) >results1 Call: lm(formula = SalePrice ~ LandSize) Coefficients: (Intercept) LandSize 493.4233 1.5821 >summary(results1) Call: lm(formula = SalePrice ~ LandSize) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 493.42 119.47 4.13 0.0001 LandSize 1.5821 0.1926 * Residual standard error: 483.86 on 182 degrees of freedom Multiple R-squared: 0.52, Adjusted R-squared: 0.2665 - What is the p-value? What is the correct decision for this test? (Choose one from below) -Since p-value < 0.05, do not reject H0 -Since p-value < 0.05, reject H0 -Since p-value > 0.05, do not reject H0 -Since p-value > 0.05, reject H0 -An appropriate conclusion for this test is: (Choose one from below) -There is a significant positive linear relation between PS and LS and If PS increases by 1 unit, we expect LS to increase by 1.58 units, on average. -There is a significant negative linear relation between PS and LS and If LS increases by 1 unit, we expect PS to increase by 1.58 dollars, on average. -There is a significant positive linear relation between SP and LS and If LS increases by 1 unit, we expect PS to increase by 493.42 dollars, on average. -There is a significant positive linear relation between SP and LS and If LS increases by 1 unit, we expect SP to increase by 1.58 dollars, on average. - Calculate the value of the correlation coefficient? (2 decimal places) = - How would you interpret the correlation coefficient. (Choose one from below) -There is a very strong negative linear relation between PS and LS -There is a moderate strong positive linear relation between PS and LS -There is a very strong positive non-linear relation between PS and LS -There is a very strong negative non-linear relation between PS and LS -There is a weak positive linear relation between PS and LS - Can we use the regression to predict LS when PS is 1500? (Choose one from below) -Yes, we can make prediction as the value 1500 is within the range of the data. -No, we can only predict PS from LS no the other way round. -No, because we did not reject the null hypothesis - No, we can't make prediction as there is no linear relation. - Predict the PS if LS is 450. ( 2 decimal places) =

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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Question

Part A

The next questions are in relation to recent study of house prices in Sydney. The variables investigated are:

1 SalePrice Selling Price in Thousands of dollars

2 Distance Distance from Sydney CBD in Kilometers

3 LandSize Land size in square meters

4 Building Area Building Area Construction in square meters

Research Question: Is there a relation between Price of the house and land size?

The output below presents the relation between Price of the house (in thousands of dollars) and land size.The

>results1 <- lm(SalePrice ~ LandSize)

>results1

Call: lm(formula = SalePrice ~ LandSize)

Coefficients:

(Intercept) LandSize

493.4233 1.5821

>summary(results1)

Call: lm(formula = SalePrice ~ LandSize)

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 493.42 119.47 4.13 0.0001

LandSize 1.5821 0.1926 **** *****

Residual standard error: 483.86 on 182 degrees of freedom

Multiple R-squared: 0.52, Adjusted R-squared: 0.2665

- What is the p-value? What is the correct decision for this test? (Choose one from below)

-Since p-value < 0.05, do not reject H₀

-Since p-value < 0.05, reject H₀

-Since p-value > 0.05, do not reject H₀

-Since p-value > 0.05, reject H₀

-An appropriate conclusion for this test is: (Choose one from below)

-There is a significant positive linear relation between PS and LS and If PS increases by 1 unit, we expect LS to increase by 1.58 units, on average.

-There is a significant negative linear relation between PS and LS and If LS increases by 1 unit, we expect PS to increase by 1.58 dollars, on average.

-There is a significant positive linear relation between SP and LS and If LS increases by 1 unit, we expect PS to increase by 493.42 dollars, on average.

-There is a significant positive linear relation between SP and LS and If LS increases by 1 unit, we expect SP to increase by 1.58 dollars, on average.

- Calculate the value of the correlation coefficient? (2 decimal places)

- How would you interpret the correlation coefficient. (Choose one from below)

-There is a very strong negative linear relation between PS and LS

-There is a moderate strong positive linear relation between PS and LS

-There is a very strong positive non-linear relation between PS and LS

-There is a very strong negative non-linear relation between PS and LS

-There is a weak positive linear relation between PS and LS

- Can we use the regression to predict LS when PS is 1500? (Choose one from below)

-Yes, we can make prediction as the value 1500 is within the range of the data.

-No, we can only predict PS from LS no the other way round.

-No, because we did not reject the null hypothesis

- No, we can't make prediction as there is no linear relation.