This sample will guide you through:
- Introduction on Statistical Analysis
- Major aspects of the variables through the SPSS software
- Common ways to reduce the biasness of this study
INTRODUCTION on Statistical Analysis
The statistics Assignment has an importance in the research of an industry. The statistics help to collect analysis and make a data of various forms. Now the mathematical part in case of the research is possible because of the statistical analysis. The main role of statistical analysis is as follows:-
- It helps to analysis the collected data.
- It helps to interpretation .of the data.
- It helps to conduct the data with the help of correlation and regression.
- It helps to analysis the data on the basis of the subject concerned.
- It helps for the validity of the data.
- It helps for the increasing the efficiency of the research.
This project carries the study of the statistical analysis of the four variables of the construction industry and also the correlation between the variables of the study. We have also defines the use of the hypothetical analysis that are required for the analysis of the data representation.
In this study we are going to discuss the major aspects of the variables through the SPSS software and tests of the research methodology. The research consists of the four variables and the utilization of the SPSS for the formulation of the graphical graphs. The graphs will represent the major nominal, ordinal, interval and ratio. All these are very important for the research of the statistical analysis. Every research consists of these four scales. The nominal scale is used to analyze the data on the basis of quantitative approach. During the analysis of the nominal values the data are generally differentiated on the basis of the quantity.
For example if we have to analysis the genders we can import the names as the male and female. Now in case of the ordinal scaling the satisfaction level, the happiness and the discomfort level of the variables can be analyzed. In case of the interval scaling the different numerical values or variables are analyzed. The difference between the variables is examined in this evaluation. And the last i.e. ratio scaling and it include the valuation of the coefficient of the variation and help to calculate the median, mean and the mode along with the dispersion ratio. Here we are analyzing the output spending on the construction industry. The data of 1996 till 2010 is helping to analyze the actual difference between the four variables which are actually putting an effect on the spending. Here we are also going to study the actual variables of the new developments that are inclusive of the public and private spending and
Infrastructure spending. As we are given the variables as:-
- Variable 1 as the new home public sector.
- Variable 2 as the new home private sector.
- Variable 3 as the repair and maintenance home public sector.
- Variable 3 as the repair and maintenance home private sector.
This study carries a hypothetical testing of all the four variables and the comparison of all the factors on the construction industry growth. The hypothetical study carries some limitations as the data are form a limited source and the evaluation may carry some bias behavior. The sample size may make restrain the study. The availability of the data in the other sources is also a limitation for the testing. The data that we are going to analyze is from the year 1996 to 2010. The major data chosen are new home public sector, the new home private sector, repair and maintenance home public sector and repair and maintenance home private sector. This study contains the major analysis of the year data on the basis of the quarterly basis.
Any limitations or potential biases in the data collection process
The research carries the major biasness as follows:-
- The research is the on the experimental basis and can contain the experimental error which up to some extend may make the content or the result of the research less weighted.
- The research may face an biasness from the subjects that are included for the benefit of the research.
- The research may include the some contents which may cause the qualitative research totally influencing by the quantitative research. The judgement of the research may also be on the dependency of the qualitative and quantitative.
- The research may have biasness on the basis of the design that may make the researcher to design a research project analysis.
- The research may also contain selection of the sampling as the data are on the basis of a hypothetical study.
- The research contains the full pressure of the various subjects or contents that are required for the management of the statistical analysis. Now this may lead to procedural biasness.
- The research may contain the measurement biasness also as the sample size is 60 and the variable are 4. So there can be the possibility of the error in the selection of the data.
- As the report is on the basis of the software SPSS. So the meta-analysis may cause biasness to the data selected.
Common ways to reduce the biasness of this study:
- The biasness should be considered as the art of the design and should not be included while analysing the results of the research.
- The biasness is a part of every research and there s no possibility to remove it from the study. The removal of the biasness may cause an reduction in the usefulness of the project.
- The biasness is kept in the mind while collecting the sample so there is already a less chance of choosing a biased data.
- The selection biasness and the informational biasness are always kept in the mind while the project is analysed.
The research has shown the main study of the hypothesis and has shown the impact of the New Home Public Sector and New Home Private Sector in the construction industry. Avail the top quality online homework help with outstanding coursework help service from the professional writer of Instant Assignment Help. The research has also shown the impact of the Repair and Maintenance Home Spending in Public Sector and Repair and Maintenance Home Spending in Private Sector on the construction industry. The research has also shown the impact of the null hypothesis on the construction industry.
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