Statistics for People Who (Think They) Hate Statistics Using By Neil J Salkind, Leslie A Shaw
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- 756
- File size
- 18.67 MB
- Format
- Digital PDF
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- Education
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- eBook[PDF]
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About this ebook
The Statistics for People Who (Think They) Hate Statistics Using R by Neil J. Salkind and Leslie A. Shaw gently guides readers through computing fundamentals and essential mathematical principles using a humorous, non-intimidating approach. [1]
The primary core topics and procedural concepts covered in this specific textbook version include:
📊 R and RStudio Basics
- Getting Started with R: Basic grounding in using the R language environment and computing labs.
- RStudio Interface: Familiarity with scripts, datasets, entering syntax to execute tests, and interpreting raw software output. [1, 2]
📉 Descriptive Statistics & Data Visualization
- Descriptive Statistics: Core fundamentals of organizing and summarizing clean data subsets.
- Graph and Chart Creation: Visually representing datasets using plots and graphical data layouts within R. [1, 2]
🔗 Correlation & Relationships
- The Correlation Coefficient: Evaluating direct connections, trends, and variables (humorously described in chapters as assessing whether variables are "cousins or just good friends").
- Linear Regression: Predicting future numerical behaviors and trend modeling based on ongoing variable tracking. [1, 2, 3]
🧪 Inferential Statistics & Hypothesis Testing
- Significance: Demystifying what statistical and operational significance actually means for practical research.
- The One-Sample Z-Test: Computing probabilities when evaluating a singular sample threshold against a known population benchmark.
- t-Tests (Independent & Dependent): Assessing standard mean differences across distinct external groups vs. connected or related trial groups.
- Analysis of Variance (ANOVA): Exploring multi-group means using standard one-way Analysis of Variance.
- Factorial ANOVA: An introductory framework covering multi-variable cross-examinations and foundational multi-factor trials. [1, 2]
🎲 Nonparametric Procedures
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