Analyzing Models And Observing Factors

To find the coefficient of skewness (a measure of the degree of skewness), the mean, mode and standard deviation was needed. Due to the large data size, a computer program was used to obtain the necessary information. The data set was inserted into the program (One Variable Analysis by Haese and Harris Publications) which then analysed it and produce the required result. The information collected is displayed below with the result for the mean rounded to 87 from 86.964 and the standard deviation to 8.2 from 8.2375. This was done for convenience however it did reduce the precision of the

All parties of construction project will benefit from IPD. The integrated process required more participants' involvement at early stage, because the IPD structure will recognize the compensation and reward at early involvement. The compensation will be based on adding value to the project and it will rewards what's the best for the project instead of individual benefit. For example providing incentives tied to achieving project objectives and goals. The concept is to use innovative business models to enhance collaboration and efficiency.

There are top 25 works can be done by BIM in Construction Industry and every BIM use has different frequency and benefit. The works done by using BIM in construction industry are as follows:

These two authors analyse data in their own parts which avoid the different measurements. This is also one of the strengths of data analysis because these two authors can analyse the data according to what they are familiar with. This paper develops a thematic analysis process and clearly defines the stages such as coding, review and thematic developed. The final themes are presented in the paper using a mind map. The authors also give an example of the process of analysis to provide evidence that they analyse deeply. In the results and discussion part, some data are used as examples to support the authors’ ideas. This reinforces the convinces of the paper. Although the authors do not discuss that they take contradictory data into account, it can be assumed that they check and refine the final model after testing in Urbchester. As a result, the ideas that the authors come up with are credibility enough to trust. In conclusion, the data analysis is sufficiently rigorous in the whole

To begin the statistical analysis of the data provided, we need to present the statistical description of it, take a general look at the means and medians for each sector and test the normal distribution of the data to see if we can undertake any further test.

-Quantitative data were described as means (SD) or medians, as appropriate. They were tested for normality by Kolmogorov-Smirnov test. In the normally distributed variables, one way

During concept design and developed design stages, BIM technology and processes were used to develop and establish building performance and the basis of design in accordance with the client’s standards. The models were required to be interoperable with analytic tools for, including but not limited to, building envelope, orientation, daylighting, energy consumption, Building Management System (BMS), renewable energy strategies, life cycle cost analysis, and spatial requirements (Andrian X Sanchez 2015). Thus the main stakeholders of the PCH project established a series of BIM-related objectives:

Well there were the 'prom rulers,' the 'goth gathering,' the 'Mexican posse,' the "athletes," and the "performers." I arranged them, unwittingly, by the gathering they hung out with, the garments they wore and the segment they sat in.

The aim of this Literature Review is to gain an understanding of what Building Information Modelling (BIM) is, what SMEs are, establish the importance of BIM, why BIM should be implemented and to explore the key issues associated with the adoption and implementation of BIM by SMEs in a live construction environment.

In this project, we compare and contrast how lean manufacturing principles and its best practices have been applied in the Food industry (manufacturing industry) and the Health Care and Hospital systems (service industry).

In services, Lean must focus on eliminating waste differently than manufacturing. Minimizing lead times between customer demand and fulfillment can be one of the ways to reduce waste. The services sector is now moving towards enhancing the customer experience. With this goal, there should be proper alignment between efforts to improve operational efficiency and achieving more customer satisfaction. (Ganapathy, 2015, p. 5).

Ryan is a construction company that specializes in integrated project delivery, building information modeling (BIM), Lean construction practices and sustainable design for office, retail, industrial, public sector, alternative energy (biomass and gasification,[2] wind, solar), health care, higher education, hospitality, mission critical, multi-family and senior housing and mixed-use projects. With in-house construction, design, development, capital markets and real estate management, Ryan uses integrated project delivery (IPD) as a preferred method for and delivering design and construction projects and organizing project teams.

Time management of the “seamless integration and flow” from one value adding step to the other highlights the efficiency concept of lean construction.

After executing two-step method of data transformation (Templeton, 2011), the researcher attained probable means and medians for both CPI (1.00) and SPI (1.05). Likewise, the Skewness figures comply with logically normalizing rules, in which the plausible range of normal and near-normal distribution is from -1 to 1. Conforming to histogram and normal Q-Q plots above, the researcher successfully reconstructed CPI and SPI data and prepared for the further inferential statistics examination. Descriptive statistics for CPI and SPI after transforming to normal and near-normal distribution are as in Table 5.7.

The summary statistics used which explains the statistical properties of the data are mean, median, standard deviation, skewness, kurtosis and test for normal distribution of the data. The mean and the median measure the central tendency of the financial data which shows the location or the clustering of the data around a certain central value. The standard deviation measures the dispersion of the data from this central value and the strength of the central tendency can therefore be judged based on the standard deviation. The skewness measures the asymmetry of the probability distribution of the data about its mean. Kurtosis is measure of the ‘peakedness’ or the shape of the probability distribution of the data. A high kurtosis distribution has a sharper peak and fatter tails, while a low kurtosis distribution has a more rounded peak and thinner tails. The normal distribution is a function which helps to find the probability that an observation will fall between any two real limits or real numbers.