Statistical Analysis With R Practice Exam

Statistical Analysis With R Practice Exam

Statistical Analysis with R refers to the practice of using the R programming language, for statistical data analysis. The practice involves using the R programming language and its tools for data manipulation, statistical modeling, hypothesis testing, and visualization. The practice is used for analyzing data, make predictions, and develop insights.
Certification in Statistical Analysis with R certifies your skills and knowledge in using R for statistical data analysis and visualization. This certification assess you in statistical concepts, datasets processing, and analysis using R.
Why is Statistical Analysis With R certification important?

• The certification validates your skills and knowledge of statistical analysis using R.
• Validates your ability to handle and analyze datasets.
• Enhances your credibility in data-driven decision-making roles.
• Showcases your knowledge of advanced statistical methods and tools.
• Opens your opportunities in data science careers.
• Provides you a competitive edge in the job market.

Who should take the Statistical Analysis With R Exam?

• Data Analyst
• Data Scientist
• Business Analyst
• Statistician
• Research Scientist
• Financial Analyst
• Market Research Analyst

Skills Evaluated

Candidates taking the certification exam on the Statistical Analysis With R is evaluated for the following skills:

• R programming for statistical tasks.
• Data cleaning, manipulation, and transformation.
• Regression, ANOVA, and hypothesis testing.
• Data visualization using ggplot2.
• Interpret statistical results.
• Handle large datasets efficiently.
• Develop reports and analysis using R Markdown.

Statistical Analysis With R Certification Course Outline
The course outline for Statistical Analysis With R certification is as below -

Domain 1. Introduction to R

• Basics of R programming

• Setting up the R environment

Domain 2. Data Manipulation

• Importing datasets from various sources

• Data cleaning and wrangling using dplyr and tidyr

Domain 3. Exploratory Data Analysis (EDA)

• Summary statistics

• Data visualization techniques

Domain 4. Statistical Methods

• Hypothesis testing (t-tests, chi-square tests)

• Regression analysis (linear and logistic regression)

• Analysis of Variance (ANOVA)

Domain 5. Advanced Statistical Techniques

• Time series analysis

• Multivariate analysis (PCA, clustering)

• Bayesian statistics

Domain 6. Data Visualization

• Plotting data with ggplot2

• Customizing visualizations

Domain 7. Reporting and Documentation

• Creating reproducible reports using R Markdown

• Automating analysis workflows