Exploratory data analysis is an important foundation of data analysis for preparation of publications and abstracts, and guiding marketing decisions. It helps people explore the patterns in the data with an open mind. Exploratory data analysis is often the first step to get a general insight by detecting and revealing the underlying structure in the data. It has a great significance in guiding the future data collecting, the following analysis, and summarizing information for publication and abstract writing. Data plays a crucial role in marketing, and exploratory data analysis could provide a clear visualization of the data for reporting and decision making.
We provide exploratory data analysis for publications, abstracts, and marketing for our clients. We engage to help our clients to understand and gain insights into the data for publication and abstract writing, and marketing through our professional and reader-friendly exploratory data analysis. Our service could help our clients to interpret the data and recognize patterns without dealing with the difficult raw data.
Our Services
We extract specific items through quantitative and graphical methods as the followings to present a clearer structure and a sense of the data set. Our professional statisticians have advanced skills and rich experience in dealing with data from different areas, which could make sure data from our clients in different disciplines could all have tailored expletory data analysis.
a. Listing mistakes and missing values.
b. Spotting outliers and anomalies.
c. Ranking important variables.
d. Visualization of the underlying structure of the data set.
e. Building a parsimonious model.
f. Estimating parameters and choosing confidence intervals.
g. Testing assumptions for following statistical analysis.
h. Testing hypotheses associated with models.
i. Gaining a sense of robustness of conclusions.
j. Optimal settings.
Statistical graphics are the heart of exploratory data analysis. We analyze the data and adopt the most appropriate methods of graphical techniques such as the followings for displaying the key information from the data set.
a. Plotting the raw data (such as scatter plots, histograms, bihistograms, probability plots, residual plots, box plots, and block plots).
b. Plotting basic statistics (such as graphing the means, standard deviations and standard errors).
c. Optimization of the layout of plots for people to read the plots easier (such as judicious plots in one page with a reasonable order for readers to follow).
We guarantee the confidentiality and sensitivity of our customers' data. We are committed to providing you timely and high-quality deliverables. At the same time, we guarantee cost-effective, complete and concise reports.
If you are unable to find the specific service you are looking for, please feel free to contact us.
References:
1. Velleman, Paul and Hoaglin, David (1981) The ABC's of EDA: Applications, Basics, and Computing of Exploratory Data Analysis, Duxbury.
2. Tukey, J. W. (1977) Exploratory data analysis (Vol. 2), Addison-Wesley.