If you are writing a dissertation or an individual or group report, you will encounter with statistics at one point or another in your studies. Statistical analysis can be very challenging for students as it requires both technical knowledge and familiarity with specific software. However, without good quality statistical analysis, many dissertations cannot get a first or even 2:1. For this reason, we provide a wide range of statistical analysis services to help students with challenging tasks. There are many types of statistical analysis. Below are a few examples of what we offer:
Regression analysis is employed when you need to assess determinants of the performance of a variable or to forecast its future performance based on the main factors affecting the variable. Depending on the type of data we deal with various types of regressions such as:
- Ordinary Least Squares (OLS) Regression
- Cross-Section Regression
- Panel Regression with Fixed Effects
- Panel Regression with Random Effects
- GMM Regression
- GLS Regression
- Probit and Logit Regression Models (Binary Regressions)
- Instrumental Variable (IV) Regressions
- Two Stage Least Squares Regressions and more.
Another method of statistical analysis is correlation analysis used to examine the strength of the connection between two variables, which can be numerically measured. Since numerical variables can be discrete and continuous, we offer different types of correlation analysis including:
- Pearson correlation analysis
- Spearman correlation analysis
- Kendall’s tau analysis or rank correlation analysis and more.
Correlation analysis will not show the causal links between variables but it will demonstrate the linear associations that exist between the analysed variables. Depending on the numerical values, the correlation can be either positive or negative. A positive correlation exists when a value of one variable increases along with the value of the other variable. Conversely, a negative correlation exists when growth in one variable is associated with a decline in the other variable.
Hypothesis testing is widely used in economic, financial and even medical studies. Hypothesis testing is associated with setting the null and alternative hypothesis, choosing appropriate confidence intervals and applying statistical tests that would be relevant in specific contexts. Depending on the context and type of data, we perform the following tests to accept or reject the null hypothesis:
- ANOVA (Analysis of Variance)
- MANOVA (Multivariate Analysis of Variance)
- Independent samples t-test
- One sample t-test
- Paired sample t-test
- One-tailed t-test
- Two-tailed t-test
- Chi-squared test
- Type I and Type II error assessment
- Runs test
- Kruskal-Wallis test
- Mann-Whitney test
- Wilcoxon signed rank test and more
The most common confidence intervals chosen for the tests are the 90%, 95% and 99% intervals. The estimations of p-values allow for testing whether the null hypothesis can be accepted or rejected. Whereas regression analysis and correlation analysis are concerned with relationships between variables, hypothesis testing is concerned with assessing the differences between variables.
Principal Component Analysis
Principal Component Analysis (PCA) is a widely used dimension-reduction tool which helps in reducing a large set of variables to a smaller set that comprises the information similar to the large set. In other words, it is a mathematical procedure which transforms a large number of variables into a small number of variables known as the principal components. PCA analysis is widely employed in business and economic research. However, it is also popular in other social science areas. We offer PCA and factor analysis as a part of statistical services. We usually perform PCA in SPSS.
If you study finance, one of the topics you come across is the volatility modelling. We offer customized volatility analysis in several statistical software such as Matlab, R, Excel, Stata and Eviews. The most popular volatility models we offer to our customers are:
- ARCH (p,q) models
- GARCH (p,q) models
- EGARCH (p,q) models
- GJR-GARCH models
- GARCH in mean models
- Threshold GARCH (TGARCH) models
- Stochastic Volatility (SV) models
- Markov Chain Monte Carlo (MCMC) Simulation
- Stochastic Volatility Jump Diffusion (SVJD) Models and more
This type of statistical analysis is often used for the assessment of risk, portfolio management and forecasting purposes.
As a part of statistical services, we offer event study methodologies to analyse effects of specific events such as earnings announcements, mergers and acquisitions (M&A) announcements or other events on stock market performance. We estimate abnormal returns (AR), cumulative abnormal returns (CAR), cumulative average abnormal returns (CAARs), buy and hold abnormal returns (BHAR) and average buy and hold abnormal returns (ABHAR) using several event windows. We perform event studies in Stata and Excel.
Last but not least, we provide a rich choice of methods to do graphical analysis to add visuals to your statistical research. Graphical analysis we offer includes but is not limited to:
- Scatter plots
- Line charts
- Bar charts
- Histograms (probability distribution plots)
- Box plots
- Pie charts
- Stacked bar charts
- Heat map and more.
This list is not exhaustive as many statistical analysis papers will include assessment of descriptive (summary) statistics, frequency tables and cross-tabulations. We customize the research to the client’s needs and provide not only the output and results but also all raw data, workings, codes written for statistical analysis. As a free gift, we developed a product on our website that can help you access more than 1500 variables on almost 200 countries in panel form. You no longer have to do data transformation manually. This tool will automatically convert the variables you choose into a panel form ready to be fed into statistical software such as Eviews or Stata.
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