Financial Development in Statistical Analysis...

The statistical approach is applied to a wealth of information obtained from data analysis of a financial development. The financial activity most often has an element of uncertainty or variability and statistics helps to ascertain the risk in such financial undertaking or decision making. There are some caveats or serious limitation to this approach. Financial development is linked to economic development and the statistical method sheds a light on the global or local welfare of such a decision making process. Statistical analysis is undoubtedly interlinked to financial development. Statistics has a ubiquitous presence in all financial development. For instance, credit modeling uses different statistical concepts such as correlation, logistic regression, weights of evidence and more. Statistics plays a significant role in measuring operational risk. It is also paramount in understanding pricing derivatives and market risk. Pricing and portfolio management also use statistical methods to understand the risk associated with a portfolio. Such estimation of risks allows people to take the appropriate measures. Statistical modeling techniques are frequently used in fraud detection, in managing attrition rates and in identifying serious prospects during sales campaigns. Statisticians are developing their core skill set to give validation to different financial data that are used by investors, sales team, and HR. Statistics empowers decision makers to draw objective conclusions from financial data where the results are not concrete but probable. Statistics is required for many aspects of the business. It helps in monitoring of budgets, measuring the performance of an organization, helps in the formulation of policies, and also helps in preparing documents for submission to regulatory authorities. Statistics are equally important in understanding the economic affairs and development of a country, which falls under the category of macro finance. Statistics helps to understand...

The Misuses of Statistics in Research Papers You Should Avoid...

Misusing statistics deliberately is unacceptable and inexcusable and if discovered by your supervisor, the retribution can be quite severe. Since you are inexperienced, you might make errors in presenting your statistics in your research paper such as using inappropriate tests, bias, making inappropriate inferences, and the like. Here are some of the misuses of statistics you should avoid in your research paper. Bias Bias refers to prejudice in ordinary terms. It could be that the data you have collected has some biases in it. In other words, maybe those who responded to you were prejudiced in their responses. In statistical terms, it is considered an “error” and if you do not rectify it, it might result in problems such as your being barred by the university for using inappropriate data collection methods or your facing some other penalty such as getting lower marks for not rectifying this systematic error. You should, therefore, use the different statistical methods to rectify this error. Improper inferences Most of the statistical reasoning includes drawing inferences about populations from the data collected by you. You may draw an inference by using inductive reasoning or reasoning that goes from particular to general. If you want to avoid any errors, you may need to define the population from whom you are going to collect the data carefully and use an appropriate probability sampling method or technique, failing which the error can result in an improper conclusion. It can also tempting to relate two factors to one another in research or to show that one of these factors caused changes in the other. However, you need to be careful not to conclude causation of a certain event from the correlations. This can also result in a systematic...

Different Types of Statistical Tests...

As we know that inferential statistics are the set of statistical tests we use to prepare inferences about data. These statistical tests help us to make inferences as they make us aware of the prototype; we are monitoring is real, or just by chance. Types of statistical tests: There is an extensive range of statistical tests. The research design, the distribution of the data, and the type of variable help us to make decision for the kind of test to use. Generally, if the data is usually distributed we choose parametric tests. If the data is non-normal we can choose from the set of non-parametric tests. Described below are the tests and their uses. Co relational: The tests look for an association between variables. Pearson correlation: It tests the strength of association between two continuous variables. Spearman correlation: It tests the strength of association between two ordinal variables. Chi-square: It tests the strength of association between two categorical variables. A) Comparison of Means: Gaze for the dissimilarity between the means of variables Paired T-test: It tests the difference between two related variables. Independent T-test: It tests the difference between two independent variables. ANOVA: It tests the difference between set means after any other variance in the resulting variable is accounted for. B) Regression: Evaluate if change in one variable depicts change in another variable Simple regression: It tests how change in the predictor variable depicts the change in the outcome variable. Multiple regressions: It tests how change in the grouping of two or more predictor variables depicts change in the outcome variable. C) Non-parametric: Used when the data does not assemble conjecture required for parametric tests Wilcoxon rank-sum test: It tests the difference between two independent variables – accounting...

Statistics and Forecasting...

Forecasting implies assessing the future from the present. Since ancient times man has been in search of forecasting methods for reducing uncertainties and planning, in modern times due to progress in statistical methods we can now forecast with much better precision and horizon. Forecasting is used in several fields, such as in management. Managers may seek sales forecast or forecast of availability of future supply of inputs. Major trends in the macro environment like political, technological, economic and natural environment can be predicted using forecasting techniques. Statistical forecasting techniques include estimation methods, time series analysis, cross sectional and longitudinal analysis, causal and econometric methods and probabilistic...

SPSS as A Tool for Data Analysis...

SPSS is an information technology program used for studying and examining statistics and was bought in 2009 by International Business Machines (IBM) Corporation. It was created long back by SPSS Inc., a software organization based in Chicago. SPSS plays a very big role in analyzing data and is also used by different organizations, investigators, government and assessment companies. The many characteristics of SPSS can be planned with a privately owned 4 GL control syntax language and are reachable through ‘pull-down’ menus. This software (SPSS) computes a broad range of statistics and controls statistical information. It is a tool designed around the encoding language of SPSS and helps trainees with achieving the majority of fundamental arithmetic breakdown through dialog boxes and menus without gaining any knowledge about the language. As the analysis proceeds ahead, the menus and dialog boxes offer reminders of the majority of the alternatives and seem very practical. There are several undertakings that are carried out quite fast by printing a small number of code words rather than running in the course of a lengthy sequence of dialogs and menus. There are a number of other undertakings also that can’t be completed from the menus. This is where SPSS comes into use and acts as a great tool for analyzing data for...