Online Certificate in Applied Statistics using R

applied statistics using R students and code

The Online Certificate in Applied Statistics using R has been developed for working professionals who have a need for additional training in Statistics. This online program is an integrated extension of the highly respected traditional curriculum in Applied Statistics at Kennesaw State University. It provides much of the same instruction as our traditional courses — but with a more highly applied approach to course materials and exams — all of which are self-paced to fit your schedule.

If you are currently enrolled in one of these Masters or PhD programs, please contact Cara Reeve to enroll in the Online Certificate in Applied Statistics using R: 

  • Masters in Data Science and Analytics
  • Masters in Computer Science
  • PhD in Data Science and Analytics
  • PhD in Computer Science

To complete the Certificate, you must pass five modules. These modules and their associated learning objectives are provided below. Please note that all work is completed using R version 3.1.0 (or newer).

Module 1: Foundations in Statistical Analysis

  1. Understand and identify the different types of variables and the general format of a data table.
  2. Understand and utilize the R programming environment.
  3. Get data into an R data frame.
  4. Work within the R environment to generate and manipulate datasets and variables.
  5. Use and create functions to execute combinations of calculations or transformations.
  6. Generate and interpret appropriate statistical graphics for different types of variables.
  7. Summarize data with descriptive statistics and frequency & contingency tables
  8. Apply concepts to a specified dataset.

Module 2: Statistical Methods I

  1. Develop Confidence Intervals for Parameters.
  2. Develop Confidence Intervals for Proportions.
  3. Determine an appropriate sample size to achieve a desired margin of error.
  4. Develop a hypothesis testing matrix.
  5. Understand the implications of Type 1 and Type 2 errors.
  6. Identify when and how to correctly execute a
    1. Hypothesis test:One Sample Proportion (z-test)
    2. Two Sample Proportion (z-test)
    3. One Sample Parameter (t-test)
    4. Two Sample Parameter (t-test)
    5. Paired Sample Parameter (t-test)
  7. Apply the concepts to a specified dataset

Module 3: Statistical Methods II

  1. Identify when and how to correctly perform one-way ANOVA
  2. Select an appropriate Post-Hoc test for significance
  3. Correctly test for normality and execute transformations where needed
  4. Understand, calculate and interpret Power calculations.
  5. Identify when and how to correctly utilize Non-
    1. Parametric Methods, including:Wilcoxon Rank
    2. Sum/Mann Whitney
    3. Wilcoxon Signed Rank
    4. Kruskal Wallis
    5. Execute a basic regression model.
      Identify when and how to correctly execute a Chi-Square test.
  6. Apply the concepts to a specified dataset. 

Module 4: Advanced Topics in Regression

  1. Understand the simple linear regression model, the usage and the assumption
  2. Understand the method of least squares, know the least square estimate (LSE) and the properties of the fitted line
  3. Know the relationship between LSE and MLE under Normal model
  4. Know how to make and interpret
  5. inferences/predictions in Simple Regression
  6. Understand the difference between Correlation Model and regression model
  7. Apply graphic methods as well as formal statistical tests for studying the appropriateness of a model
  8. Understand the need for joint estimation and simultaneous inferences
  9. Be aware of the additional remedial measures to deal with unequal error variances, a high degree of multicollinearity, and influential observations.
  10. Apply the concepts to a specified dataset.

Module 5: Nonlinear Regression

  1. Introduction to categorical data analysis:
    1. Obtain and interpret relative risk, odds ratios, and confidence intervals for odds-ratios.
  2. Binary Logit Models I:
    1. Fit and interpret a simple and multiple logistic regression model.
    2. Assess model fitness & classification rates.
    3. Perform model diagnostics & selection.
  3. Survival Analysis
    1. Develop nonparametric estimates of the survival function.
    2. Fit and interpret Cox Proportional Hazards models.
    3. Assess model fitness 
 
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