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Statistics with R
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IT Description

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