Descriptive Statistical Analyses and an Important Aspect of Social Science - Essay Example

Financial Data Analysis Introduction Various methods are engaged in analyzing specific forms of data in abid to making sense out of the same. Statisticians and other stakeholders involve in data analysis in a bid to provide adequate understanding of the specific phenomena with respect to social behavior. Different statistical analysis tools are used in order to make sense of specific sets and forms of data (Rice, 2007). Examples of such tools include descriptive and inferential statistics. Whereas descriptive statistics include the measures of central tendencies and measures of dispersion whilst inferential statistics include the use of regression and correlation analysis. The following is an analysis of a data set involving the prices of wheat during 2008. Part One of the Data Analyses Summary of Actions The data analysis involves conducting a correlation and descriptive statistical analyses. Firstly, the data analysis process involved regrouping of the prices of the two categories of wheat. Then the relevant data was extracted from the whole data set for the 2008 and the two kinds of wheat. With the data regrouped, the next procedure was to draw a time series. The time series had the years as the X-variables and the prices of wheat as the Y-variables (Rice, 2007). After the drawing of the time series, the data analysis process engaged in describing the data through various components of the descriptive analysis. The descriptive analysis provides a general overview of the data set to be analyzed. After the descriptive data analysis, the next process was to conduct a correlation and a covariance analysis on the data set. The correlation and covariance analyses were aimed at finding out the correlation coefficient in order to ascertain the relationship between the two prices of the two kinds of wheat. Besides, the correlation and covariance coefficients were also used in assessing the direction and strength of the relationship between the prices of the two kinds of wheat within the data set. Findings The following are the findings obtained from the first part of the analysis in respect to time series, descriptive analysis, correlation, and covariance. Time Series From the time series it is evident that the prices of the two kinds of wheat have the same trend. The trend displays an increase in prices during February followed by a decrease in the prices throughout the remaining part of the year as displayed in the above figure. Correlation and Covariance   Wheat Soft Red Wheat Hard Kansas Wheat Soft Red 1 Wheat Hard Kansas 0.950684215 1 Wheat Soft Red Wheat Hard Kansas Wheat Soft Red 37166.29997 Wheat Hard Kansas 36656.19547 40001.18726 From the above correlation coefficient, it is evident that there is a strong positive correlation given the fact that the coefficient is positive and it is close to 1. Correlation coefficients usually range from -1 to +1 with the former indicating a strong negative correlation and latter showing a strong positive correlation (Rice, 2007). The covariance obtained from the data analysis also confirms the strong positive correlation. Descriptive Statistics WHEATSF(P) Value WHEATHD(P) Value Mean 631.1469466 Mean 860.2509542 Standard Error 11.93313105 Standard Error 12.37987293 Median 595.5 Median 875.5 Mode 438.5 Mode 620 Standard Deviation 193.1546002 Standard Deviation 200.3857493 Sample Variance 37308.69959 Sample Variance 40154.44851 Kurtosis -0.513131032 Kurtosis -0.464999548 Skewness 0.539762591 Skewness 0.2041105 Range 863 Range 920.5 Minimum 331.5 Minimum 486.5 Maximum 1194.5 Maximum 1407 Sum 165360.5 Sum 225385.75 Count 262 Count 262 Largest(1) 1194.5 Largest(1) 1407 Smallest(1) 331.5 Smallest(1) 486.5 Confidence Level (95.0%) 23.49746471 Confidence Level (95.0%) 24.37714176 The above graph provides an adequate analysis of the descriptive data regarding the two sets of data. The mean, standard deviation, sample variance, and skewness of the data set provide a description of the prices of two kinds of wheat provided (Rice, 2007). Part Two of Data Analyses Summary The second part of the data analyses involved forecasting. Forecasting is an important aspect with respect to making sense of the available data. The forecasting involved two forms; use of trend-lines and the application of regression analysis. The first aspect involved creation of different time series tables (Rice, 2007). After which, the time series were developed through a trend-line. The trend-line was effective in making sure that the statistical data analysis was able to forecast into the future for the purposes of obtaining further data and analysis. On the other perspective, the regression analysis was also used in forecasting the data. The forecasting of the data based on regression analysis used various components or tools. Therefore, the regression analysis was very useful in forecasting the data. Findings: From the forecasting analysis, the following are some of the findings obtained. Wheat Soft Red The regression equation for the wheat soft red is Y = - 1.647 + 65933, with R2 being 0.8191 indicating that with time, the prices of this type of wheat will definitely reduce significantly. Wheat Hard Kansas The regression equation for the wheat soft red is Y = - 1.6612 + 66696, with R2 being 0.7736 indicating that with time, the prices of this type of wheat will definitely reduce significantly. In addition to the forecasts, the following is a summary of the regression analysis that is used in forecasting the data. SUMMARY OUTPUT Regression Statistics Multiple R 0.905050101 R Square 0.819115686 Adjusted R Square 0.818419977 Standard Error 82.30743902 Observations 262 The multiple R, the R square, and the adjusted R square are good coefficients for forecasting the data within the financial analysis aspects. In addition, the following is a summary of the data that can be used in making sure that there is a possibility of forecasting the same.   df SS MS F Significance F Regression 1 7976196.818 7976196.818 1177.383 1.58E-98 Residual 260 1761373.774 6774.514517 Total 261 9737570.593       In addition, the following data can be used in forecasting the data.   Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 65932.50872 1903.114623 34.64452847 2E-99 62185.03 69679.99 62185.03 69679.99 X Variable 1 -1.647763435 0.048021533 -34.313012 1.58E-98 -1.74232 -1.5532 -1.74232 -1.5532 All the information contained above is adequate to enable in making a forecasting analysis. It is therefore important to note that forecasting can be achieved through trend-lines as well as the regression analysis. In conclusion, it is indisputable that data analysis is an important aspect of social science. Social scientists that include statisticians, economics, and other stakeholders employ data analysis in order to make sense of the available raw data. Bibliography Rice, J. A. (2007). Mathematical statistics and data analysis. Duxbury press. Read More