SAS Data Analysis Services Conducted by Professionals
Excellent SAS data analysis services help scholars, businesses, or researchers in retrieving data, analyzing it, and reporting to relevant target audiences. We are a company with proficient analysts who perform statistical analysis using programming language and SAS commands.
We have the best statistical analysis system that can boost business intelligence and help students or researchers with excellent data management and predictive analytics for their projects and research papers.
This article provides a detailed discussion on the factors we consider to provide the best SAS data analysis help to our clients; who may be students, researchers, or businesses.
Factors we Consider When Analyzing Data Using SAS
Over the years, our SAS data analysis services have been helpful to many professionals and scholars. We assist in data management, retrieval, operations research, and statistical analysis among other functions. The SAS analytics solutions transform data into meaningful and actionable information that can be used in business planning, forecasting future trends, or informing important decisions.
We are proficient in using the SAS software to analyze data in different fields and for various purposes. Below are some of the factors we consider when analyzing data using SAS.
1. Exploratory data analysis(EDA) with SAS
Every time we receive data from clients to analyze, it is a must to perform EDA to gain a better understanding by visualizing and summarizing the information contained in the dataset. In so doing, we identify variables, data types, relevant descriptive statistics, missing or unique values, and any abnormalities in the datasets.
With the research question and objective in mind, our SAS experts carefully conduct EDA to determine whether we have the right data for the specific purpose and eliminate any abnormalities that may hinder effective analysis.
2. Cleaning and validating data
The excellent understanding of the requirements for SAS datasets enables us to correctly identify and eliminate any invalid values that are not helpful in the analyses.
Our SAS data analysis experts use the appropriate procedures to identify invalid values and clean the data in preparation for advanced analytics. All data validation rules must be adhered to so that no invalid values are stored in SAS datasets.
3. Data types for SAS analysis
Data types in SAS include fixed-length character strings and real numbers. The SAS software stores time and dates internally as numbers. We must, therefore, examine the type of data provided by the client before commencing the analysis process.
In case the provided dataset is in a format that is not compatible with the SAS requirement, our experts know how to convert them into acceptable and workable forms by commanding the relevant functions. The type of data influences the size of memory that we allocate for the values in a variable.
4. Definition of variables in a SAS dataset
Rows of observations and columns of variables characterize SAS datasets. It is essential to define and understand the properties of variables in a dataset before starting to analyze it. The most common properties for variables taken into account in our SAS data analysis services include the name, length, type, label, and format.
We carefully define all the characteristics of variables in a given dataset before analysis; with complete compliance with the SAS requirements on each property. Once the variables are correctly identified and defined and the appropriate analytical procedures performed on the dataset, the process is likely to produce reliable results and outputs.
5. Basic types of variables in SAS data
SAS data is comprised of numeric and character variables. Numeric variables store numbers and arithmetic calculations such as addition or subtraction can be performed on them. Indicator variables representing categorical data, time, and dates are also considered numeric in SAS software.
Character variables store text in SAS statistical analysis systems. The characters may include letters, parentheses, signs, or numbers that represent labels with no significant arithmetic calculations required on the dataset.
We, therefore, define and categorize the types of variables depending on the nature of the solution being sought in SAS data analysis.
6. The SAS windows
Some of the important windows in the SAS software suite that is useful in our data analytics include:
a). The explorer/results window
The results window is fundamental when exploring the program files, results of analytical procedures in conjunction with the output window, and SAS libraries. Our expert analysts use the results window to locate datasets and other relevant related files.
b). Editor window
With the editor window, one can effectively create, execute, and edit SAS programs. Our SAS data analysis experts use the editor window to perform statistical analysis procedures and manipulations using written codes that instruct the software on how to process the information. The window can also be used when saving the editing code created by the analyst or producing a record of a particular analysis in the future.
c). The output window
The output window is used in viewing the results generated from a submitted SAS software program. We save the output as a text file that is editable in other applications, viewable, or printable. The results and output windows work together in organizing information in the files. One can retrieve data saved in the output window by clicking the relevant icons for display.
d). Log window
In addition to containing important summaries, the log window can be effectively used to determine whether a particular program may have been submitted with errors. Our SAS data analysis experts accurately differentiate the appearance of the log window comments and error messages to correctly analyze data using the software.
7. Data entry in SAS
Before entering data for analysis, it must be converted into a format that is acceptable to SAS; an excel file with appropriate rows and columns. After saving the data to be analyzed in an excel file, we import it into the SAS software by clicking the relevant icons and complying with the program instructions. The file must be closed in all programs of the computer before one attempts to import it.
8. The SAS essentials to perform statistical analysis
To run the SAS stat to analyze data accurately, it is fundamental to understand the different versions and statements supported by the software suite when offering descriptive visualization, reporting, data mining, machine learning, or time series analysis among other functions. The two common statements that form the basis for our SAS data analysis services include the data steps and the procedures as discussed below.
a). Data steps in the statistical analysis system
This step involves describing and modifying all the data to be analyzed and managed using SAS software. In the data step, our experts in statistics command the SAS program in the way it should read the data, delete or generate variables and/observations. We are well-trained and experienced in transforming the raw data from research into an understandable SAS dataset.
Using the essential statements in data steps, we import the right data, conduct descriptive analysis, and prepare reports on variables.
b). Procedure/PROC steps
The procedure steps tell the SAS software the type of analysis that was performed on the given dataset. Such analyses may include the analysis of variance(ANOVA), regression, or computation of means depending on the nature of the data and the research objectives. PROC keywords start all procedure statements in the analysis process.
With the best SAS data analysis help from experts like us, one can rest assured of sophisticated reports, charts, and statistical analyses. We indicate the procedure name, variables to be used, datasets, and any other relevant parameters or instructions.
9. SAS programming and data importation
To import a dataset from an excel file, the PROC import function on the SAS software is used. In addition to uploading data into the SAS environment, the software also contains in-built libraries where users can store helpful datasets for analytics. We can store both temporary and permanent data in the in-built libraries as may be deemed important to help in the analysis and reporting processes.
10. Statistical descriptive analysis
One is assured of the best SAS data analysis help after ordering our service, with logically calculated statistical descriptive analyses to gain insights and make meaning from a given dataset.
With our experienced analysts on board, the client would never have to worry about how to deal with missing values, calculation of mean, correlations, frequencies, and any other descriptive statistics on a dataset. We have gained expertise and prowess in software data management and analytics, thus, assuring a perfectly performed process that yields reliable results for those who may wish to purchase the services of a SAS data analyst from us.
11. Graphs and visualization
Correctly using the graphics function provided in the SAS tool helps in analyzing and reporting data. Depending on the type of data at hand, visual aids such as simple bar graphs, histograms, scatter, and box plots, can be effectively used to represent variables. We ensue that each graph or visual aid used is accurately presented when reporting, well-labeled, and captioned appropriately for readers to interpret with ease.
Carefully considering these factors in different data contexts ensures that clients receive a customized SAS analysis process that perfectly fits their needs and fields. We are, therefore, the best option for anyone wishing to hire a statistician to analyze data using SAS.
Our 24/7 availability and accessibility enable us to reach out to clients, facilitate timely delivery of orders, and responsive customer support services. We assist researchers, students, and businesses in different industries such as finance and healthcare to analyze and deduce insights from big data for various purposes. One can purchase the services of a SAS data analyst from our company through the website at any time they may be in need.