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Statistics with R

Program Highlights:
Praxis Certification, Placement Support

Statistics with R

This Short-Term Program – designed by veterans in the Analytics industry; helps you master ‘Statistics with R’, which is predominantly used in Data Analytics. This uniquely blended Program is brought to by Praxis, a Top-ranked Analytics B-School in India.

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INR 12,500

Program Summary

  • 2 credits
    Credits

    With this course, you are 5 credits short of an assured placement.

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  • Duration 3 Months
  • 15 Hours of projects/Assignments
  • 30 Hours of online sessions

Course Topics

  • 1

    Statistics with R

    • Introduction to Data

      • Data Basics
      • Overview of data collection principles
      • Experiments - Principles of experiment design
      • Examining Numerical and Categorical data
      • Comparing numerical data across groups
    • Introduction to Probability

      • Introduction
      • Conditional probability
      • Bayes’ Rule
    • Distributions

      • Discrete Distributions
      • Continuous Distributions
    • Introduction to linear regression

      • Correlation
      • Line fitting
      • Fitted values
      • Residuals
      • Basic introduction to multiple regression
    • Foundations for inference and estimation

      • Variability in estimates
      • Sampling distribution
      • Confidence intervals
      • Margin of error and ascertaining a sample size
    • Foundations for inference and hypothesis testing

      • Nearly normal population with known SD
      • Hypothesis testing framework
      • Two Tailed and One Tailed tests
      • Testing hypothesis using confidence intervals and Critical Z values
      • One-sample means with the t distribution with unknown population SD
      • Inference for a single proportion
      • Decision errors (Type 1 and 2)
      • Hypothesis testing using p-values
      • Choosing a significance level
      • Power and the type 2 error rate
    • Linear Regression and Multiple Regression

      • Introduction to F-statistic
      • Hypothesis Tests
      • Intervals
      • Coefficient of Multiple Determination
      • Interpreting the model output

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