Boston Housing Data analysis.

Boston Housing Data
analysis

Background
Implementation of Regression analysis will ensure that business
organization receives proper benefits regarding the management of the
predictive analysis, Boston

Chosen Algorithm
The algorithm that is chosen includes Linear regression algorithm will be
performed. Linear regression is considered as the integrated as per the
machine learning can be performed. Random data sets are used and
hence management of the diagnosis of the stock price is provided.

Methodology
The methodology that is used in this project is secondary data analysis
method. The data that are present in the internet in peer reviewed
journals are taken into consideration. This is the major reason that the
processing of the data analysis gets performed in a better manner and
data that are collected are

Data Analysis
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.737662726
R Square 0.544146298
Adjusted R Square 0.543241826
Standard Error 6.215760405
Observations 506
ANOVA
df SS MS F Significance F
Regression 1 23243.914 23243.914 601.6178711 0
Residual 504 19472.38142 38.63567742
Total 505 42716.29542
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 34.55384088 0.562627355 61.41514552 3.7431E-236 33.44845704 35.659225 33.44845704 35.65922472
lstat -0.950049354 0.038733416 -24.52789985 5.08E-88 -1.0261482 -0.8739505 -1.0261482 -0.87395051

References
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application based on SAS software. Part I: Linear and logistic regression. arXiv preprint arXiv:1808.02387.
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 Muralidharan, S., Phiri, K., Sinha, S.K. and Kim, B., 2018. ANALYSIS AND PREDICTION OF REAL ESTATE PRICES: A CASE
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