Management Data Analysis Assignment Sample

  • 95032+ Project Delivered
  • 503+ Experts 24x7 Online Help
Download Full Sample
Downloads 2183 Words
This is a Sample Assignment Submitted by Student. Contact our Experts for 100% Plagiarism Free Assignment Solutions

Management Data Analysis Assignment Sample

INTRODUCTION

Construction sector is one of the growing field in the UK however, due to impact of recession slowdown is observed in this sector. Sign of recovery are observed in the sector and it is identified that in mentioned sector construction of building for people residential purpose increased at fast pace. However, commercial purpose building construction does not get that momentum which is seen in respect to residential buildings construction growth rate. It can be said that revival comes in the UK construction sector but it does not happened at fast rate and there are lot more things that need to be seen in the mentioned industry. In the present research study data related to offers, square feet of premises, number of bedrooms and number of bathrooms are analyzed (Gurranand Bramley, 2017). Price of premises and whether bricks are used in construction of building or not are also explained in detail. Data analytics is the one of the important key tool that can be used by the firms for data analysis purpose and it help them to identify number of factors that need to be consider while making busines decisions. In present research study varied statistical tests are applied on data and their results are interpreted in proper manner. In this regard, Gretl and excel software is used for data analysis purpose. By using mentioned softwares t test aere applied and it is identified whether variable sample mean is different from standard. This is done for multiple variables in the dataset. It can be said that systmatic approach is followed to carry out research work and to analyze data in proper manner in order to develop broad understanding of the variable. At end of the report conclusion is formed and in this way entire research work is carried out (Shuyun and Yin, 2015). It must be noted that analysis of current data set will help firm in making strong business decisions. It can be seen from table that different price levels are given in the dataset and due to this reason it is very important to identify that what trends are going on in terms of square feet and bedrooms as well as bathrooms. After doing thorough analysis finally conclusion section is prepared in the report.

Sample

In present research study sample of 129 people is taken and different questions were asked from them in relation to neighbiurhood, number of offers that are made in the house, number of bedrooms and bathrooms there are in the house. Apart from this, price related information and bricks whether used or not are given in dataset. While analyzing dataset it is identified that there are no missing values in dataset and due to this reason its cleaning is not required. Hence, it can be said that data is already prepared. There is no need to do any sort of modifications in dataset as it is already perfect and can be used for analysis purpose directly.

Findings

Descriptive statistics

  • NBHD: Mean value of variable neighbourhood is 1.96 and its standard deviation is 0.80 which means that neighbourhood is in traditional area. Mode value is 2 and this also reflect that similar results are revealed by mode. Hence, it can be said that most of respondents homes are in tradtional area.
  • Offers: In case of offers it can be observed that mean value is 2.57 and standard deviation value is 1.06 which is reflecting that number of offers made on home purchase is 3 which means that most of home owners receive offers for home sale three times in specific duration.
  • Square foot: Square foot mean value is 2000 and standard deviation is 211.57. On other hand, mode value is 1920 and it is reflecting that on average basis people homes are of square foot 2000 and apart from this there are many other homes where square foot size is 1920.
  • Bedroom: Mean value of bedroom is 3.092 and its standard deviation is 0.72 which reflect that on average basis people have 3 bedrooms in their homes. Mode value is also 3 and this is also indicating that on average basis and most of times respondents give similar reponse.
  • Bathroom: It can be seen from table that mean value of variable is 2.44 and its standard deviation is 0.51. On other hand, mode value is 2 which means that most of homes have two bathrooms and this number is deviating at very low rate.
  • Price: Mean value of price is 260854 and standard deviation value is 53737 which make it clear that most of times price of house remain 260854 and this value is deviating at at very low pace. It can be said that price of product is flucutating at low or moderate rate.
  • Brick: Table that is given in appendix is clearly reflecting that mean value of brick is 1.32 and mode value is 1 which means that majority of homes are not made of bricks. Instead stones are used to prepare homes.

Management Data Analysis

It can be observed that number of bedrooms and bathrooms matched to each other and there are few cases where number of bedrooms are higher then number of bathrooms.

Management Data Analysis

It can be observed that price of properties are fluctuating consistently and are not stable at particular point. Hence, it can be said that other variables that are in data set does not play any role in determinng price of property.

Management Data Analysis

It can be seen from chart that number of observations are mostly in case of square feet size of 2071.81, 2175.45 and 1864.54. Apart from this, in other class there are less number of observations.

Calories

Q1

222650

Median - Q1

29750

Q3-median

44800

mean

260,854.69

Management Data Analysis

It can be seen from box whisker plot that there is equal gap in error bars and point is in center which means that data is normally distributed in nature.

H0: There is no significent mean difference between mean value of population and 1500.

H1: There is significent mean difference between mean value of population and 1500.

Management Data Analysis

It can be seen from image given above that at 10%, 5% and 1% level of significence null hypothesis is rejected and this means that if we are 90% confident that mean value of variable exist in the market then in that case it is identified that there is no significent difference between sample mean and standard value which is 1500. On other hand, if one is 5% confident that population and sample mean are different then in that case also significent difference is observd. Similalry, at 1% significence value same thing is observed and no difference is oberved. On basis of all these things it can be said that sample is taken from population with specific mean and value of same statistics does not vary for mean (Zainal, Teoh and Shamsudin, 2015). This means that most of times mean value of variable does not deviate too mich from the standartd value. Hence, it can be said that most of times around standard value mean value of sample variable which is square foot move.

It can be also be seen from table given above that sample size is 128 and mean value is 2000.94 and standard deviation value is 211.57. This means that on average basis mean value of variable remain 2000.94 for square foot. Standard deviation value is 211.57 which is very low and it can be said that its value is very low. Hence, value is not deviating at fast rate and this is also proved from values of statistics that are obtained from t tet for different confidence intervals.

H0: There is no significent difference between standard value 2,50,000 and mean value of sample.

H1: There is no significent difference between standard value 2,50,000 and mean value of sample.

Management Data Analysis

It can be seen from table that mean value is 260855 and standard deviation value is 53737.5 whicb means that on an average basis price value remain nearby to 260855. Standard deviation value is 53737.7 which is high and is indicating that values are deviating at fast rate. Two tail p value is 0.023

H0: There is no significant impact of number of bedroom, bathroom and square feet on the prices charged.

H1: There is significant impact of number of bedroom, bathroom and square feet on the prices charged.

Test: Regression: In statistical modelling, regression is used for the predictive analysis that explains relationship among various variables. The measure is used as an attempt to determine the impact of various independent variables over one dependent variable. Thus, it is the best statistical measure that will help analyst to determine whether number of bedroom, bathroom and square feetsignificantly affects the prices charged or not.

Results attached in Appendix

Interpretation: The regression statistics found multiple R of 0.662 shows moderate association between prices and all the independent variables because the value of correlation coefficient of 0.662 is middle of 0.25-0.75. However, coefficient of determination (R square) is found to 0.439 for the given variable.

The statistical results present that for each unit increase in number of bedroom, prices changed by 20919.85 or vice-versa. However, with per square feet increase, prices increase by 71.28 whereas with the increase in bathroom number, it rises up by 27092.26. P-value indicates likelihood whether the results is real or occur by chance. In statistics, P value is used to determine that whether the impact is significant or not. Considering sq ft as an independent variable, P-value is 0.001 shows that it has a significant impact over price. Similarly, bedroom and bathroom shows P-value of 0.00047 and 0.0016 that is below 0.05 (5% level of significance). Hence, it can be said that all the independent variables affects the prices. However, on the other side, looking to overall significance F value, it is found that value goes beyond 0.05 as it is 1.53, hence, it can be interpreted that overall impact of all the independent variables, bedroom, bathroom and size does not have a significant impact over the prices charged.

CONCLUSION

On basis of above discussion it is concluded that there is significent importance of analytical tool for the firms because it help them in analyzing situation in better manner. It is also concluded that majority of respondents received offer 3 times in respect to their homes. Square feet of 2000 is observed in case of most of homes. It is also identified that there 3 bedrooms and 3 bathrooms in case of most of homes. Due to all these factors price of home remain in range of 260854. Regression results reflect that due to change in number of bedrooms no change comes in price of homes or properties. Means that there may be other components that may play important role in affecting sales price of property.

Further research

On basis of research done it is identified that there is further need to do research and under this an attempt must be made to identify whether with change in economic environment any sort of variation comes in price of residential properties.

Recommendation

On basis of above discussion it is recommended that focus must be on increasing number of bedrooms so that price of property can be increased at rapid rate. It is important to do because people give much importance to this factor while purchasing home or any flat.

REFERENCES

Books and Journals

Gurran, N. and Bramley, G., 2017. Housing, Property Politics and Planning in Australia. In Urban Planning and the Housing Market (pp. 259-290). Palgrave Macmillan UK.

Shuyun, C. and Yin, P., 2015. The Reconstruction and Optimization of Affordable Housing Property Management. China Real Estate, 24, p.009.

Zainal, R., Teoh, C.T. and Shamsudin, Z., 2015. A Review of Goods and Services Tax (GST) on Construction Capital Cost and Housing Property Price. In Proceeding of 4th International Conference on Technology Management, Business and Entrepreneurship.

APPENDIX

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.662991861

R Square

0.439558208

Adjusted R Square

0.425999132

Standard Error

40713.08969

Observations

128

ANOVA

df

SS

MS

F

Significance F

Regression

3

1.61204E+11

53734651297

32.41800696

1.5346E-15

Residual

124

2.05537E+11

1657555672

Total

127

3.66741E+11

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-11281.66654

34400.76921

-0.327948089

0.743503998

-79370.4242

56807.09109

-79370.4242

56807.09109

Sq Ft

71.28529808

21.33452877

3.341311114

0.001102302

29.05829004

113.5123061

29.05829004

113.5123061

Bedrooms

20919.85188

5824.6599

3.591600582

0.000472081

9391.218506

32448.48526

9391.218506

32448.48526

Bathrooms

27092.26867

8437.334644

3.210998474

0.001684859

10392.42018

43792.11715

10392.42018

43792.11715

Nbhd

Offers

Sq Ft

Mean

1.9609375

Mean

2.578125

Mean

2000.9375

Standard Error

0.071370808

Standard Error

0.094515819

Standard Error

18.70053761

Median

2

Median

3

Median

2000

Mode

2

Mode

3

Mode

1920

Standard Deviation

0.807468522

Standard Deviation

1.069324424

Standard Deviation

211.5724313

Sample Variance

0.652005413

Sample Variance

1.143454724

Sample Variance

44762.8937

Kurtosis

-1.460897111

Kurtosis

-0.129332847

Kurtosis

0.148130003

Skewness

0.071581369

Skewness

0.283942367

Skewness

0.07847916

Range

2

Range

5

Range

1140

Minimum

1

Minimum

1

Minimum

1450

Maximum

3

Maximum

6

Maximum

2590

Sum

251

Sum

330

Sum

256120

Count

128

Count

128

Count

128

Confidence Level(95.0%)

0.14122995

Confidence Level(95.0%)

0.187029749

Confidence Level(95.0%)

37.00498914

Bedrooms

Bathrooms

Price

Mean

3.0234375

Mean

2.4453125

Mean

260854.6875

Standard Error

0.064165643

Standard Error

0.045475119

Standard Error

4749.772433

Median

3

Median

2

Median

251900

Mode

3

Mode

2

Mode

235600

Standard Deviation

0.725951385

Standard Deviation

0.514492239

Standard Deviation

53737.54074

Sample Variance

0.527005413

Sample Variance

0.264702264

Sample Variance

2887723285

Kurtosis

-0.402680746

Kurtosis

-1.436907159

Kurtosis

-0.009908536

Skewness

0.215192926

Skewness

0.398592743

Skewness

0.472940137

Range

3

Range

2

Range

284200

Minimum

2

Minimum

2

Minimum

138200

Maximum

5

Maximum

4

Maximum

422400

Sum

387

Sum

313

Sum

33389400

Count

128

Count

128

Count

128

Confidence Level(95.0%)

0.126972229

Confidence Level(95.0%)

0.089987054

Confidence Level(95.0%)

9398.942475

Brick

Mean

1.328125

Standard Error

0.041664103

Median

1

Mode

1

Standard Deviation

0.471375521

Sample Variance

0.222194882

Kurtosis

-1.47446712

Skewness

0.740824106

Range

1

Minimum

1

Maximum

2

Sum

170

Count

128

Confidence Level(95.0%)

0.082445742

Save Time & improve Grades

just share your requirements and get customized solutions on time

- +
35% Off
£ 6.69
Estimated Cost
£ 4.35
Your Paper will Include
  • Title Page
  • Table of Contents
  • Body
  • Bibliography & References
  • Any Additional requirements that you specify!
Free Product features
  • Free Product Features
  • Free Quality Assurance Reports
  • Grammar Report
  • Compliance Report
  • Plagiarism Report
Free Service Features
  • Free Order Management
  • Free E-Guides
  • Free 24x7 exclusive customer support
Free Assignment Sample on New assignment help
Get best price for your work

offer valid for limited time only*

in a hurry-get a callback