Statistical inference for everyone
Item
Title (Dublin Core)
Statistical inference for everyone
Creator (Dublin Core)
Blais, Brian
Date (Dublin Core)
2014
pages (Bibliographic Ontology)
1-238
Publisher (Dublin Core)
Save the broccoli publishing
Description (Dublin Core)
I would like to propose a new introductory statistical inference textbook, which I believe takes a fresh look at a course that fits into nearly every quantitative major at universities.
Initial Motivation
My motivation for this project stems from my dissatisfaction with traditional approaches to the topic, and my belief that there is a better way. A first semester statistics course is generally divided into the following four parts:
I. Basic Statistical Concepts
• Basic statistical concepts including population, parameter, sample, and statistic
• Types of data (ordinal, time-series, etc...), and sampling methodology
• Organizing the data visually or graphically - including histograms, pie graphs, box plots, and stem-and-leaf plots
• Statistical computations including mean, median, mode, standard deviation, and percentiles
II. Probability
• Properties of unions, intersections, conditional probability, independence and mutual exclusivity
• Permutations and combinations
• Discrete distributions
• Continuous distributions
• Normal distribution
III. One-sample Statistics
• Confidence intervals
• Sampling distributions
• Computations involving the normal distribution, t-distribution, and binomial distribution (for proportions)
• Hypothesis testing
IV. Two-sample Statistics
• Two sample problems - expanding topics from Part III to two variables
Initial Motivation
My motivation for this project stems from my dissatisfaction with traditional approaches to the topic, and my belief that there is a better way. A first semester statistics course is generally divided into the following four parts:
I. Basic Statistical Concepts
• Basic statistical concepts including population, parameter, sample, and statistic
• Types of data (ordinal, time-series, etc...), and sampling methodology
• Organizing the data visually or graphically - including histograms, pie graphs, box plots, and stem-and-leaf plots
• Statistical computations including mean, median, mode, standard deviation, and percentiles
II. Probability
• Properties of unions, intersections, conditional probability, independence and mutual exclusivity
• Permutations and combinations
• Discrete distributions
• Continuous distributions
• Normal distribution
III. One-sample Statistics
• Confidence intervals
• Sampling distributions
• Computations involving the normal distribution, t-distribution, and binomial distribution (for proportions)
• Hypothesis testing
IV. Two-sample Statistics
• Two sample problems - expanding topics from Part III to two variables
Subject (Dublin Core)
Mathematics
Language (Dublin Core)
English
