Descriptive Analysis Visualization Assignment Help

Descriptive Analysis Visualization Assignment Help

Descriptive Analysis Visualization Assignment Help

This is a solution of descriptive analysis visualization assignment help in which we discuss objective of carrying out exploratory, descriptive and regression analysis for gaining comprehensive understanding of house price in city

Introduction

This report has been undertaken with the main objective of carrying out exploratory, descriptive and regression analysis for gaining comprehensive understanding of house price in the Shiraz region. It is also going to lay down understanding of most important factors that has been laying down impact over the housing prices in the area of Shiraz. The different analysis that has been carried out in this regard are as follows descriptive statistics, factors influencing house prices, development of multiple regression model and time series analysis.

Task One-Summary of House Price

Table 1: Descriptive statistics

Price($'000)

 

 

 

Mean

886.575

Standard Error

29.66343575

Median

852

Mode

811

Standard Deviation

324.9466579

Sample Variance

105590.3305

Kurtosis

-0.14778497

Skewness

0.426005063

Range

1569

Minimum

192

Maximum

1761

Sum

106389

Count

120

From the above descriptive statistics table it could be interpreted that arithmetic mean value for selling price of house in $'000 stood to be 886.575. It indicates that in the Shiraz local government area within greater Melbourne, Australia the average price stood to be 886.57 ($’000). On the other hand Median and Mode value for the selling price of house in $'000 was seen to be around 852 ($’000) and 811 ($’000).  The skewness result indicates that variable is positively skewed as selling price of house value is seen to be 0.42.
The above line graph indicates that there is high variation in the selling price of house in $'000 at Shiraz local government area within greater Melbourne, Australia. It could be seen from the above graph that there is high fluctuation in the prices that are being offered to the clients by Shiraz.

Task Two-Factors Influencing House Prices

From the above scatter diagram it could be interpreted that there is positive correlation between house price and area of the house in square meters as there is rightward movement and a straight line if drawn is going to originate out to high x- and y-values.

From the above scatter diagram it could be interpreted that there is positive correlation between house price and Street appeal as evaluated by the real estate agency as there is rightward movement and a straight line if drawn is going to originate out to high x- and y-values.

The above figure indicates that there is perfect positive linear relationship between the variables that are between house price and number of storey’s or levels in the house. It is because both the variables are moving towards the rightward direction and are seen to be away from each other.

Task Three-Development Of a Multiple Regression Model For House Price Correlation

Table 2: Correlation ship

 

Price($'000)

Price($'000)

1

Rooms

0.505469281

Street

0.722570048

Storey’s

0.565098455

Weekly Rent $

0.665590917

Bedrooms

0.539744531

Bathrooms

0.331222685

From the above correlation table it could be interpreted that all the variables selected has got positive relationship with the selling price of house in $'000. However, some of the variables has got higher correlation such as street (0.72) and weekly rent (0.66).

Model 1

Linear regression model

This model represents the independent variable that has been identified in order to present the dependent variable.

Y=α + βX1+BX2+BX3+BX4+BX5+ BX6…………..………………………. (Model 1)

Y= -386.984+ 0.174X1+0.212X2+89.86X3+188.42X4+106.80X5+-13.37X6

Dependent variable= Selling price of house in $'000

Independent variable= Rooms, Weekly rent, Street, Storey’s, Bedrooms and Bathrooms

Table 3: Multiple regression model 1

Regression Statistics

 

 

 

 

 

 

Multiple R

0.913271

 

R Square

0.834063

Adjusted R Square

0.825253

Standard Error

135.8368

Observations

120

 

ANOVA

 

df

SS

MS

F

Significance F

 

Regression

6

10480214

1746702

94.66378

9.95E-42

 

Residual

113

2085036

18451.64

 

 

 

Total

119

12565249

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-386.984

102.3464

-3.78111

0.000251

-589.75

-184.217

Rooms

0.17444

25.05619

0.006962

0.994457

-49.4664

49.81528

Weekly Rent $

0.212072

0.07814

2.714014

0.007689

0.057263

0.36688

Street

89.8622

7.429578

12.0952

4.13E-22

75.14287

104.5815

Storey’s

188.4243

26.93576

6.995319

1.98E-10

135.0597

241.7889

Bedrooms

106.8086

31.44359

3.396831

0.000942

44.51315

169.104

Bathrooms

-13.3787

40.95154

-0.3267

0.744502

-94.5111

67.75365

Findings

R-Square

R-square value for the following model is 83.4%, indicates that 83.4% total variance in selling price of house in $'000 can be explained by independent variable Rooms, Weekly rent, Street, Storey’s, Bedrooms and Bathrooms

F-Value

The calculated F-value is greater than critical value of F thus it can be said that model is accepted. Even it can be said that value ration of explained to unexplained variance is seen to be very high. Hence, it can be said that regression variables are significant for explaining the dependent variable.

P-Value

Rooms, Street, Storey’s and Bathrooms have no influence on selling price of house in $'000 as it is not statistically significant because Rooms, Street, Storey’s and Bathrooms P-value is greater than 0.01% at 1% level of significance. On the other hand, weekly rent and bedrooms have influence on selling price of house in $'000 as it is statistically significant because weekly rent and bedrooms P-value is less than 0.01% at 1% level of significance.

Coefficients

Coefficient value indicates the rooms (0.17), weekly rent (0.21), street (89.8), storey’s (188.42) and bedrooms (106.8) have got dependability on selling price of house in $'000. In a case if Rooms, Weekly rent, Street, Storey’s and Bedrooms changes by one unit then selling price of house in $'000 will increase by 17%, 21%, 8900%, 18800% and 10600%. On the other hand, Bathrooms $ -13.37 have got no dependability on selling price of house in $'000. In a case if Bathrooms changes by one unit then selling price of house in $'000 will decrease by 1300%.

Model 2

Linear regression model

This model represents the independent variable that has been identified in order to present the dependent variable.

Y=α + βX1+BX2+BX3…………..………………………. (Model 2)

Y= -409.72+ 97.7X1+200.19X2+127.61X3

Dependent variable= Selling price of house in $'000

Independent variable= Street, Storey’s and Bedrooms

Table 4: Multiple regression model 2

Regression Statistics

 

 

Multiple R

0.906903

 

R Square

0.822474

Adjusted R Square

0.817882

Standard Error

138.6718

Observations

120

 

ANOVA

 

df

SS

MS

F

Significance F

 

Regression

3

10334586

3444862

179.1413

2.25E-43

 

Residual

116

2230663

19229.86

 

 

 

Total

119

12565249

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-409.729

58.34096

-7.023

1.58E-10

-525.28

-294.177

Street

97.711

7.009674

13.93945

1.52E-26

83.82745

111.5945

Storey’s

200.197

27.0691

7.395776

2.39E-11

146.5833

253.8108

Bedrooms

127.6109

11.84395

10.77435

3.52E-19

104.1525

151.0693

Findings

R-Square

R-square value for the following model is 82.24%, indicates that 82.24% total variance in selling price of house in $'000 can be explained by independent variable Street, Storey’s, Bedrooms

F-Value

The calculated F-value is greater than critical value of F thus it can be said that model is accepted. Even it can be said that value ration of explained to unexplained variance is seen to be very high. Hence, it can be said that regression variables are significant for explaining the dependent variable.

P-Value

Street, Storey’s and Bedrooms have no influence on selling price of house in $'000 as it is not statistically significant because Street, Storey’s and Bedrooms P-value is greater than 0.01% at 1% level of significance.

Coefficients

Coefficient value indicates the street (97.71), storey’s (200.19) and bedrooms (127.61) have got dependability on selling price of house in $'000. In a case if Street, Storey’s and Bedrooms changes by one unit then selling price of house in $'000 will increase by 9700%, 20000%, and 12700%.

Model 3

Linear regression model

This model represents the independent variable that has been identified in order to present the dependent variable.

Y=α + βX1+BX2+BX3…………..………………………. (Model 3)

Y= -60.14+ 97.7X1+317.87X2+137.10X3

Dependent variable= Selling price of house in $'000

Independent variable= Storey’s and Bedrooms

Table 5: Multiple regression model 3

Regression Statistics

 

 

Multiple R

0.724641

 

R Square

0.525104

Adjusted R Square

0.516986

Standard Error

225.8353

Observations

120

 

ANOVA

 

df

SS

MS

F

Significance F

 

Regression

2

6598066

3299033

64.68494

1.21E-19

 

Residual

117

5967183

51001.56

 

 

 

Total

119

12565249

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-60.1449

85.78547

-0.70111

0.484627

-230.039

109.7487

Storeys

317.8746

41.88497

7.589228

8.52E-12

234.9236

400.8255

Bedrooms

137.1081

19.25664

7.120045

9.4E-11

98.97137

175.2449

Findings

R-Square

R-square value for the following model is 52.5%, indicates that 52.5% total variance in selling price of house in $'000 can be explained by independent variable Storey’s and Bedrooms.

F-Value

The calculated F-value is greater than critical value of F thus it can be said that model is accepted. Even it can be said that value ration of explained to unexplained variance is seen to be very high. Hence, it can be said that regression variables are significant for explaining the dependent variable.

P-Value

Storey’s and Bedrooms have no influence on selling price of house in $'000 as it is not statistically significant because Storey’s and Bedrooms P-value is greater than 0.01% at 1% level of significance.

Coefficients

Coefficient value indicates the Storey’s (317.87) and bedrooms (137.10) have got dependability on selling price of house in $'000. In a case if Storey’s and Bedrooms changes by one unit then selling price of house in $'000 will increase by 31700% and 13700%.

Task Four-Time Series Analysis

Table 6: Mean absolute percentage error calculation

Time Period

Quarter

Median House Price ($'000) (A)

Forecasted (F)

Deviation(A-F)

Absolute deviation(/A-F/)

Absolute percentage of error=100(/A-F/)/ A

1

2012-Q1

554

950

-396

396

71

2

2012-Q2

589

1320

-731

731

124

3

2012-Q3

661

1500

-839

839

127

4

2012-Q4

522

1090

-568

568

109

5

2013-Q1

610

950

-340

340

56

6

2013-Q2

700

1320

-620

620

89

7

2013-Q3

850

1500

-650

650

76

8

2013-Q4

592

1090

-498

498

84

9

2014-Q1

770

950

-180

180

23

10

2014-Q2

880

1320

-440

440

50

11

2014-Q3

1090

1500

-410

410

38

12

2014-Q4

725

1090

-365

365

50

13

2015-Q1

932

950

-18

18

2

14

2015-Q2

1150

1320

-170

170

15

15

2015-Q3

1330

1500

-170

170

13

16

2015-Q4

940

1090

-150

150

16

Total

 

 

 

 

 

943

A= actual valueWhere,

F= Forecasted value

n= Time period

From the above calculation of Mean absolute percentage error it could be interpreted that the measure of prediction accuracy of a forecasting value for Quarterly median house prices was seen to be around 58.9%. This shows price of the house vale at Shiraz to be accurate by 58.9%.

Task Five-Critique The Business Research Approach

The present research proposal have been taken on the basis of appropriate variable for measuring the suitability of selling price of house in $'000 on the basis of various factors selected for carrying out the study. Further, if this analysis need to be repeated in the future then the various other factors that can be used for studying the suitability of Selling price of house in $'000 to be charged could be through inflation, personal disposable income, connectivity of city and location of Shiraz city at highway.

Conclusion

From the study it has been found that majority of the variables that has been laying down influence over the house price are Street, Storey’s and Bedrooms. This shows that Shiraz local government area must these independent variables to be important while carrying out their development process.