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.

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.

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%.

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%.