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Statistics with R
Categories:
IT
Description

ABOUT THIS COURSE

Imagine a situation where you not only produce data analysis reports, but also use the R programming language to visualize the data and derive statistical conclusions like a boss. Statistics with R is the best of both worlds, equipping you with the specialization of both a strategist and a data scientist.

Learn the most in-demand technology and business-oriented skill and find a prosperous way to make a great career in the field of data and analytics.

Rated Top 3 in Analytics programs in the country, Praxis Business Schools has designed the program through its Industry veterans in the Analytics domain. 

LEARNING OBJECTIVES

  • Learn to use R to model statistical relationships using graphs, calculations, tests, and other analysis tools. Learn how to enter and modify data; create charts; examine outliers; calculate correlations; and compute regressions, bivariate associations, and statistics for three or more variables.

KEY TOPICS COVERED

  • Introduction to data; data basics; overview of data collection principles
  • Experiments - principles of experiment design
  • Introduction to 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
  • The 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
  • A coefficient of multiple determination
  • Interpreting the model output.

PRE-REQUISITES TO REGISTER FOR THE COURSE: 

  • R programming and Statistics

Faculty Profiles
Frequently Asked Questions