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...

When to hire a statistician for your dissertation?...

If you are reading the blog, it means that you are probably confused whether or not to hire a statistician for your dissertation. You may be trying to figure out the way through which you can effectively finish your dissertation. Let us put your mind to rest. Read on to know when to hire a statistician for your dissertation. Not sure of the research methodology Always remember that the research methodology which you choose to answer your research questions will set the pace for the findings and ultimately results and evaluations. If you do not choose a correct research methodology, all your effort in gathering the data, analyzing and evaluating them will become fruitless. So, if you are not sure which research methodology to choose, it is better to hire a statistician that will aptly study your dissertation and then suggest the best research methodology. Statistical Software is not cooperating To evaluate the findings and get results, PhD scholars often use statistical software. But statistical software is not full proof. There may be times when even after using high-end software, still you are unable to get the results. So, if the statistical software is not cooperating, instead of becoming frustrated or panicking, it is advisable to hire a statistician that will evaluate the findings much better than the software! Stats problems you are unable to figure out You may be faced with a situation when you may not be able to figure out some crucial stats issues on your own. You may ask your committee members, formal professors or mentor who is excellent in quantitative analytics, still you may not be able to resolve the issue. Eventually, it is easier and much viable to hire a dissertation statistician. The...

All about descriptive statistics for thesis...

Statistical analysis in thesis helps a lot of students in obtaining better grades. But many students make many errors in use of statistics and statistical analysis due to lack of knowledge and experience. One such analysis which requires minimal effort and helps in comprehending and representing large amounts of data is descriptive statistics which are widely used. The three tools of descriptive statistics which are widely used are the mean, median and the mode which is altogether frequently called measures of central tendency. Descriptive statistics are more used to group data rather than to do final analysis or to draw final conclusions. Descriptive statistics gives clarity and helps in clarifying the large volumes of data. Descriptive statistics gives a simple quantitative summary of the data set collected and helps in understanding the data set and helps in placing the data in perspective. Data collected can be also represented in the form of graphs, charts and diagrams which also come under descriptive kind of statistics. Descriptive statistics are often put at the beginning of the results page which normally gives a glimpse of the analysis. While presenting the descriptive statistics, it is necessary to present at least one of the measures of central tendency like mean or average and one form of variability like standard deviation which is more frequently used. At the same time descriptive statistics have some limitations. They can be used only for summations and cannot b used for generalization. Graphical methods of descriptive statistics are better than numerical methods to identify and study the different patterns in the data while numerical methods are more precise. But it is better to use both numerical and graphical methods because they complement each other. Apart from descriptive statistics, inferential...

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...

Becoming a Statistician

All students must be aware of the wonders of statistics, without this subject the information age as we call it would have seemed baseless. Statistics is very important to uncover precise information from data and progress in statistics has enabled attaining more information easily and swiftly. Courses in statistics are offered by several institutions of repute and students desirous of higher studies in the subject can choose from them. A career is statistics is highly rewarding, and statisticians are approached by almost all sectors for analysis and interpretation of data. After post-graduation students can also opt for PhD and research in statistics and make new contributions to methods and...