GEOG3006 Quantitative Methods

GEOG3006 Quantitative Methods

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GEOG3006 Quantitative Methods

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GEOG3006 Quantitative Methods

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Course Code: GEOG3006
University: Norwegian University Of Science And Technology

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Country: Norway

Question:
Describe about the Quantitative Methods For the Mississippi Data.

Answer:
Epifanio and Ventura-Campos (2011) define class interval as the size of each class of a variable of which a range of a variable is divided into.  In our case, the class interval that has been used is 500,000 as required.     
The population of people living in the State of Mississippi and who are aged 65 years and above is 380,000 inhabitants which falls under the range of 500,000 to 1,000,000 (first class). Based on the distribution of the data in Figure 2 above, it is observed that the median of the number of residents 65 years and older in the USA based is 1,000,000 people. Based on this, it can be concluded that the population of residents 65 and older in the living State of Mississippi is close to typical.  
Li, Zhang, Yu, and Li (2018) describes an outlier as an observation that lies outside the overall pattern of a distribution. Figure 2 above had gaps and outliers as well. From the chart, it can be observed that no State in USA had 2,000,000 to 2,500,000 residents 65 years and older and 3,500,000 to 4,000,000 r residents 65 years and older. In a rare case, some States had 2,500,000 and 3,000,000; 3,000,000 and 3,500,000, and 4,000,000 and 4,500,000 residents 65 years and older. 
Having an optimal histogram class interval as explained by Chandler and Lukesh (2006), offers the most efficient and unbiased estimation a of the probability density function. The new histogram has no outliers and gaps.  
Clearly, the percentage of residents 65 years and older in the State of Mississippi is less than the percentage of other states; the shape of the histogram indicates that the percentage is below the mean. 
Figure 4 is can bell-shaped implying a normal distribution of data. When this is the case, the mean and the median are usually equal (Chandler & Lukesh, 2006). Besides, there is a gap between the 8% and 9%. Two outliers also exist i.e. between the 7% and the 8%.  
The class interval selected for figure 5 above is 1,000,000.   
There features of the histogram are obvious i.e. the histogram is skewed to the right, the mean is greater than the median and it is unimodal (has one peak). In addition, there are outliers and gaps in this chart. 
A review of the provided data shows that the data breaking strengths vary.  The variation in strength according to xx is usually be pointed out from the minimum and the maximum. In our case, the maximum breaking strength was 168 whereas the minimum breaking strength was 115 
The class interval 160 -165 has the most data with 6 observations followed by the class interval 150 -55 with 4 observations.  
There is no effect as the shape of the two figures i.e. 6 and 7 remains the same even after the changes. 
The distribution is skewed to the left implying that its mean is less than the median. The distribution is has a single peak i.e. its unimodal, and has more gaps and outliers.  It can be deduced the breaking strength was between the class intervals 160-165.  
References
Chandler, S., & Lukesh, G. (2006). RHINO: Real-time histogram interpretation of numerical observations. High-Power Laser Ablation VI, 14(7), 14-28. doi:10.1117/12.668488
Epifanio, I., & Ventura-Campos, N. (2011). Functional data analysis in shape analysis. Computational Statistics & Data Analysis, 55(9), 2758-2773. doi:10.1016/j.csda.2011.04.003
Li, Y., Zhang, Y., Yu, M., & Li, X. (2018). Drawing and studying on histogram. Cluster Computing, 6(4), 12-46. doi:10.1007/s10586-018-2606-0
Lokwani, D. (2013). Chapter-07 General Principle of Histogram Generation and Interpretation. The ABC of CBC: Interpretation of Complete Blood Count and Histograms, 4(2), 82-86. doi:10.5005/jp/books/11929_7

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