Data Analysis for Decision-Making - Essay Example

DATA ANALYSIS FOR DECISION MAKING Name Institution Date Introduction Organizations and other business premises need timely, concise accurate and objective information in order to assist in the process of effective decision making. One approach from which such details can be derived is from a business report. In general terms a business report can be defined as an organized information presentation to a particular audience for the objective of assisting an organization attain a goal. Whereas business reports share same features, there is also the need for customizing reports for specific purpose (Silverston, L. 2011). Purpose of this report The main objective of this report is in regard to decision making on the best alternative of purchase to make in relation to the data provided. The data provided consists of pricing of different housing properties including their different qualities in terms of style, age, parking space and other basic and luxury issues when it comes to real estate business. As much as fair pricing is going to be used in most part of the report it is in order to critically define fair pricing as applied throughout the report. Fair pricing in this case refers to a price that is comprehensively reflected on the quality of housing of the property being considered for purchase options. Fair price is a term that should be used cautiously as it does not by any means necessarily imply to small pricing (little cash). In this regard therefore, it is crucial that every real estate property be revaluated fully before any purchase so as to avoid unnecessary hitches in the future. This is to imply that the more properties get old the more they are expected to depreciate thus less housing should be purchased at lower prices than new properties which are likely to cost higher prices. Let’s consider the information below MS Zoning RL Lot Area 14000 Utilities AllPub All public Utilities( electric, gas and water- EGW&S House style 2 stories Overall quality 8 Very good- rates the overall material and finishing of the house Overall condition 7 Good- rates the overall condition of the house Age 20 Gr liv Area 1100 Full bath 2 Full bathroom TotRms AbvGrd 5 Total rooms above grade- bathroom not included Fireplaces 1 Garage cars 2 The question that is to be discussed is if it is recommendable to buy this property for $200,000. Given all the features that have been shown in the table above, it is only rational to recommend the purchase of this property at 200 000 US dollars. This might be considered a fair price given the qualities of the house. The overall condition and the quality of the house are all impressive given the age 20 the house has been in existence. As professionals dealing with real estates, it is only possible to say that the quality and the condition of the house at age 20 might be as a result of good maintenance and above all the superior construction materials used to put up the house. With such qualities provided, $200 000 is a good bargain in the real estate industry As done in the report, in the study of the data provided -with respect to one in which we are interested it is possible to obtain a huge number of annotations. There is no possibility of grasping any idea in regard to the characteristic when we consider all the observations. In this case thus it is better to derive one number for one group. What should be noted is that number ought to be a good representative one for all the observations so as to provide a flawless picture of that characteristic. As the report indicates, such representative number can be a central value for all these observations of all the data provided. This central value is known as measure of central tendency otherwise a measure or an average of locations (Frye, 2010). Generally, there exist five averages. Those considered in this report among them include mean, median and mode also referred to as simple averages while the other two averages harmonic mean and geometric mean are referred to as special averages. Arithmetic mean or mean As calculated in the report, Arithmetic mean or simply the mean of a variable is described as the sum of the observations divided by the number of observations. In terms of interpretation the given mean provides the stakeholders with a chance to know the average of two more than one investment. This is important in making decisions as one may consider investment on two or more projects depending on the theoretically obtained mean. In this case the mean sale price is 174.9246. This means that suppose we are to purchase considering the sale prices, the best sale price would be around that which falls near the mean obtained (Silverston, L. 2011). Merits and demerits of the Arithmetic mean Obtained Merits 1. It has been inflexibly demarcated. 2. It is easy to calculate and easy to understand. 3. Suppose the numbers of items are to be sufficiently large as in our case it is more reliable and more accurate. 4. It is a premeditated value and is not always on the basis on its position within the series. 5. There is a possibility of calculating mean even if some of the details of the data are missing. 6. Considering all averages, it is affected least by variations of sampling. 7. Finally, mean that has been calculated delivers a good platform basis for comparison. Demerits encountered a. It cannot be acquired through inspection nor sited by application of a frequency graph. b. There is no possibility of it being in the study of qualitative spectacles not capable of mathematical measurement i.e. beauty, Intelligence, honesty etc, c. It can ignore any single item only at the risk of losing its accuracy. d. It is affected very much by extreme values. e. Mean cannot be premeditated for the cases of open-end classes. 6. The final demerits is that it may lead to erroneous deductions, if the details of the data from which it is computed are not given. Median The median is the middle most items that divides the group into two equal parts, one part comprising all values greater, and the other, all values less than that item. Merits of Median 1. Median is not influenced by extreme values because it is a positional average. 2. Median can be calculated in case of distribution with open-end intervals. 3. Median can be located even if the data are incomplete. Demerits of Median 1. A slight change in the series may bring drastic change in median value. 2. In case of even number of items or continuous series, median is an estimated value other than any value in the series. 3. It is not suitable for further mathematical treatment except its use in calculating mean deviation. 4. It does not take into account all the observations. Mode The mode refers to that value in a distribution, which occur most frequently. It is an actual value, which has the highest concentration of items in and around it. It shows the center of concentration of the frequency in around a given value. Therefore, where the purpose is to know the point of the highest concentration it is preferred. It is, thus, a positional measure. Its importance is very great in agriculture like to find typical height of a crop variety, maximum source of irrigation in a region, maximum disease prone paddy variety. Thus the mode is an important measure in case of qualitative data. Quartiles The quartiles divide the distribution in four parts. There are three quartiles. The second quartile divides the distribution into two halves and therefore is the same as the median. The first (lower).quartile (Q1) marks off the first one-fourth, the third (upper) quartile (Q3) marks off the three-fourth. It may be noted that the second quartile is the value of the median and 50th percentile (Silverston, L. 2011). Skewness and Kurtois A fundamental task in many statistical analyses is to characterize the location and variability of a data set. A further characterization of the data includes skewness and kurtosis. Measure of Dispersion tells us about the variation of the data set. Skewness tells us about the direction of variation of the data set. Definition as considered in the report: Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. Positive vs. Negative Skewness These graphs illustrate the notion of skewness. The one on the left is positively skewed. The one on the right is negatively skewed. Measure of Skewness: 1. Karl Pearson coefficient of Skewness Sk = 3(mean - median) / Standard Deviation. = 3(X –Me) / S 2. The skewness of a random variable X is denoted or skew(X). It is defined as: where and are the mean and standard deviation of X. Interpretation: 1. If Sk = 0, then the frequency distribution is normal and symmetrical. 2. If Sk 0, then the frequency distribution is positively skewed. 3. If Sk 0, then the frequency distribution is negatively skewed. KURTOSIS Kurtosis is a parameter that describes the shape of a random variable’s probability distribution. About Kurtosis The ExcelTM help screens tell us that "kurtosis characterizes the relative peakedness or flatness of a distribution compared to the normal distribution. Positive kurtosis indicates a relatively peaked distribution. Negative kurtosis indicates a relatively flat distribution" (Microsoft, 1996). And, once again, that definition doesn't really help us understand the meaning of the numbers resulting from this statistic. Normal distributions produce a kurtosis statistic of about zero (again, I say "about" because small variations can occur by chance alone). So a kurtosis statistic of 0.09581 would be an acceptable kurtosis value for a mesokurtic (that is, normally high) distribution because it is close to zero. As the kurtosis statistic departs further from zero, a positive value indicates the possibility of a leptokurtic distribution (that is, too tall) or a negative value indicates the possibility of a platykurtic distribution (that is, too flat, or even concave if the value is large enough). Values of 2 standard errors of kurtosis (sek) or more (regardless of sign) probably differ from mesokurtic to a significant degree (Ross, 2013) Correlation and Regression Analysis These tools can be used to find out if the outcome from one variable depends on the value of the other variable, which would mean a dependency from one variable on the other. Regression and correlation analysis can be used to describe the nature and strength of the relationship between two continuous variables. Scatterplot The first step in the investigation of the relationship between two continuous variables is a scatterplot! Creating a scatterplot for the two variables and evaluating the quality of the relationship is an important step. If the scatterplot shows a reasonable linear relationship (straight line) it is important to calculate Pearson's correlation coefficient to evaluate the strength of the linear relationship. Pearson's correlation coefficient (named after Karl Pearson, 1857-1936) is a number between -1 and, that measures the strength of a linear relationship between two continuous variables. The absolute value of the coefficient measures how closely the variables are related. The closer it is to 1 the closer the relationship. A correlation coefficient over 0.8 indicates a strong correlation between the variables. Recommendations Recommendation 1: To support local authorities in evolving Fair price Housing, in addition to planning as well as accomplishing their responsibilities to deliver reasonably priced housing, subsequently a procedural assistance suite ought to be established so as to enable delivery of professional advice as well as consultation facilities to local authorities prices considered to be a fair deal. Recommendation 2: extra exploration/ research ought to be custom made and instigated in order to define the degree to which the deficiency of reasonably priced housing in some regions of the state has brought by an adversative effect in regards to the capability of members of protected classes to seek and sustain decent, reasonably priced housing. A disparate proportion of protected class members, particularly people with disabilities together with racial minorities can only meet the expense of lower cost housing. References Vanderstoep, S. W., & Johnston, D. D. (2009). Research methods for everyday life: blending qualitative and quantitative approaches. San Francisco, CA, Jossey-Bass. Cook, T. D., & Reichardt, C. S. (2009). Qualitative and quantitative methods in evaluation research. Beverly Hills, Calif, Sage Publications. Frye, C. 2010. “Business Rules Are Back.” Application Development Trends 9, 7 (July): 29–35. Ross, R. G.2013. Business Rule Concepts: The New Mechanics of Business Information Systems. Boston: Business Rule Solutions, Inc. Silverston, L. 2011a. The Data Model Resource Book, Volume 1, Rev. ed. New York: Wiley. Read More