Analysis Of Data Analysis

The first artefact for my portfolio is Assignment 2 in Mdde 602, titled: Analyzing Quantitative Data. Part two of this assignment included an Excel spreadsheet which displayed the data. This assignment consisted of an Excel data spreadsheet containing multiple sets of data to perform the statistical analysis. The second component consisted of creating a response document on Microsoft Word in order to record the written answers to the assignment problem. The solution to each problem was only possible to solve after performing the data analysis in the Excel sheet first. The most challenging and exciting part of this assignment was explaining the concept to a person who had no understating of statistics and breaking down the material to be easily comprehensible.

One-Sample T: Sleep Variable N Mean StDev SE Mean 95% CISleep 20 6.950 1.572 0.352 (6.214, 7.686)95% of students get between 6.2 hour to 7.69 hour of sleep.

It is done by comparing the confidence of the rule with a benchmark confidence value.

Percentages and percentile rank are the beginning descriptive data. I am very comfortable with central tendency, normal distribution, variable, frequency distribution, and measures of variability. The mean of any set of numbers is the average and the median is the exact middle of a distribution. The advantage of the mean measure is it captures the full data set but is influenced by outliers. The measures of variability such as range, variance, and standard deviation, tells me how the data is spread out or clumped together.

The choices for the selection criterion have several model fit statistics that are useful for model

Creating a data analysis based on a sample population can become complicated if you are basing the frequency on exact numerical values that are continuous such as GPA, distance, weight and height. Creating a categorical analysis based on identical data, would display numerous bars within the charts and make the analysis nearly impossible. In a position where you have sampled a population that provides a wide range of variables to analyze, it would be more beneficial for the results to be analyzed in specified ranges to provide a more concise frequency. In this instance, is it is best to create a frequency distribution table where data can be put into numerical ranges instead of individual frequency’s. A frequency distribution table

Cutting stock: In industrial application of cutting stock, optimization methods are carefully programmed for each new task. ATLBO algorithm could be used to give better suggestions.

The data for this project was taken from the World Bank’s World Development Indicators (WDI) database. The WDI database contains a variety of information that includes economic, environmental, and social variables. This data comes from every country in the world, ranging anywhere from the year 1960-2016. However, each variable’s data differs by country, depending on the year the data was acquired. For this project, we will solely focus on the set of variables acquired in the year 2010 because there are nearly no missing values. The ones with missing values have been expunged from this project. The final dataset consists of 175 countries, with 42 countries having been removed.

Also, method is performing well for C4.5 algorithm is likely to perform well for ID3 algorithm. Previous studies show that CFS method increases accuracy for CART algorithm although not as much as the C4.5 algorithm does (Doraisamy et al., 2008).

Data Transformation are often very complex and is the most costly section of the ETL process. Transformations are often achieved outside the database using flat files, but mostly occurs within an Oracle database. The transform step applies rules or functions to the extracted data. These rules or functions will decide on the analysis of data and can involve transformations like the following:

Each step contains multiple effective and efficient techniques. The following table provides an overview of dominant techniques applied in each step and next part further illustrates two of the cutting edge – Expected Monetary Value (decision tree) and Monte Carlo Simulation.

Trees based algorithm can be used with which type of Target variable/Dependent variable/Response variable in the dataset?

The quality of estimation is measured by the insensitive loss function. High performance accuracy of the model can be achieved by tuning C, kernel and epsilon parameters. Parameter C Fig 4: Correlation of Canopy height taken manually with canopy height extracted from Image data 5B. Growth pattern analysis from

51 0.4265 0.4279 0.4292 0.4306 0.4319 1.5 0.4332 0.4345 0.4357 0.4370 0.4382 0.4394 0.4406 0.4418 0.4429 0.4441 1.6 0.4452 0.4463 0.4474 0.4484 0.4495 0.4505 0.4515 0.4525 0.4535 0.4545 1.7 0.4554 0.4564 0.4573 0.4582 0.4591 0.4599 0.4608 0.4616 0.4625 0.4633 1.8 0.4641 0.4649 0.4656 0.4664 0.4671 0.4678 0.4686 0.4693 0.4699 0.4706 1.9 0.4713 0.4719 0.4726 0.4732 0.4738 0.4744 0.4750 0.4756 0.4761 0.4767 2.0 0.4772 0.4778 0.4783 0.4788 0.4793 0.4798 0.4803 0.4808 0.4812 0.4817 2.1 0.4821 0.4826 0.4830 0.4834 0.4838 0.4842 0.4846 0.4850 0.4854 0.4857 2.2 0.4861 0.4864 0.4868 0.4871 0.4875 0.4878 0.4881 0.4884 0.4887 0.4890 2.3 0.4893 0.4896 0.4898 0.4901 0.4904 0.4906 0.4909 0.4911 0.4913 0.4916 2.4 0.4918 0.4920 0.4922 0.4925 0.4927 0.4929 0.4931 0.4932 0.4934 0.4936 2.5 0.4938 0.4940 0.4941 0.4943 0.4945 0.4946 0.4948 0.4949 0.4951 0.4952 2.6 0.4953 0.4955 0.4956 0.4957 0.4959 0.4960 0.4961 0.4962 0.4963 0.4964 2.7 0.4965 0.4966 0.4967 0.4968 0.4969 0.4970 0.4971 0.4972 0.4973 0.4974 2.8 0.4974 0.4975 0.4976 0.4977

Among the available clustering methods, K-Means algorithm is generally used to divide learners into natural groups based on their behavior for a larger dataset. In the K Means clustering method, the number of clusters, denoted by K is needed to be predefined to apply the technique. This is one of the simplest and the most used unsupervised learning algorithm for clustering.