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Data Science & Statistics
Statistical Analysis with R
Statistical Analysis with R
Curriculum
4 Sections
19 Lessons
7 Weeks
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Introduction to R
6
1.1
Understanding R as a Calculator, Statistical Software, and Programming Language
1.2
R Preliminaries: Installation, Environment Setup, and Package Management
1.3
Accessing Help and Documentation in R
1.4
Data Input Methods: Direct Input and Importing from External Sources like Excel
1.5
Data Accessing and Indexing
1.6
Introduction to Graphics in R and Built-in Functions
Descriptive Statistics
4
2.1
Diagrammatic Representation of Data: Box Plots, Stem and Leaf Diagrams, Bar Plots, Pie Diagrams, Scatter Plots
2.2
Measures of Central Tendency: Mean, Median, Mode
2.3
Measures of Dispersion: Range, Standard Deviation, Mean Deviation
2.4
Summaries of Numerical Data
Normal Distribution and Probability
5
3.1
Understanding Normal Distribution
3.2
Plots to Check Normality: QQ Plots, Histograms
3.3
Plotting Probability Curves for Standard Distributions
3.4
Correlation Analysis: Pearson, Spearman, Kendall
3.5
Regression Analysis: Simple and Multiple Linear Regression
Inferential Statistics
4
4.1
Parametric Tests: One-sample, Two-sample, and Paired t-tests & Chi-square Tests
4.2
Analysis of Variance (ANOVA): One-way and Two-way
4.3
Non-Parametric Tests: Kruskal-Wallis Test; Wilcoxon’s Test
4.4
Non-Parametric Tests: Kruskal-Wallis Test; Wilcoxon’s Test
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