Openintro Statistics 4th Edition 3.6 Solutions
OpenIntro Statistics
OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League
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Getting Started
Japanese Translation + Other International Distribution
A Japanese translation has been created by a team of Japanese faculty! This translation is available below in both PDF (on Dr. Kunitomo's page) and as an affordable paperback (via the Japanese Statistical Association). For those using this version, please send your warm wishes to the team!
FREE -- Japanese translation of OpenIntro Statistics PDF
Translation by Naoto Kunitomo, Yasushi Yoshida, & Atsuyuki Kogure
Japanese translation, B&W paperback for ¥1980
Translated by Naoto Kunitomo, Yasushi Yoshida, & Atsuyuki Kogure
Paperbacks for Canada, UK, India, Germany, and more Click to see all international options
English B&W paperback on Amazon.co.jp
See also the Japanese translation option
Amazon.ca -- B&W paperback
Hello, northern neighbor
Amazon.co.uk -- B&W paperback
Hello, from across the Atlantic
Notion Press (India) -- B&W paperback
Price includes shipping cost
Amazon.de -- B&W paperback
Book is in English
Amazon.fr -- B&W paperback
Book is in English
Amazon.es -- B&W paperback
Book is in English
Amazon.it -- B&W paperback
Book is in English
Teachers: General Resources
Resources for teachers, some of which are restricted to Verified Teachers only. Slides, labs, and other resources may also be found in the corresponding chapter sections below.
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Available to Verified Teachers, click here to register
OpenIntro Statistics exercise solutions
Available to Verified Teachers, click here to register
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MyOpenMath: online course software
Free course software, OpenIntro course templates are available
MyOpenMath: setting up an OpenIntro course
Course templates exist for some OpenIntro books
OpenIntro Statistics, info on past editions
Content, prices, and availability details
Teachers page with additional resources
Some public resources, others restricted to Verified Teachers
Request OpenIntro stickers
Available to teachers, click here to register
Teachers: Sample Exams
What is Statistics?
Chapter 1: Intro to Data
Chapter 2: Summarizing Data
Summarizing Data: 3 videos Videos for each section
2.1 - Examining numerical data
Mean, standard deviation, histograms, box plots, and more
2.2 - Considering categorical data
Table proportions, bar graphs, mosaic plots, and more
2.3 - Case study: gender discrimination
Early inference ideas: testing using randomization
Google Slides & LaTeX variants available Slides for each section
Slides 2 - Summarizing data
LaTeX slides for full chapter on Github
Slides 2.1 - Examining numerical_data
Google Slides version, can export to Powerpoint
Slides 2.2 - Considering categorical data
Google Slides version, can export to Powerpoint
Slides 2.3 - Case study: gender discrimination
Google Slides version, can export to Powerpoint
Lab - Introduction to data
Software: R (Base), R (Tidyverse), Rguroo, Python, SAS, Stata
Class Activity: Descriptive Measures
Collecting and exploring Instagram data
Weighted mean
Supplemental section: How and when to use weighting
Chapter 3: Probability
Probability: 3 videos Videos for some sections
3.1 - Defining probability
Core concepts, explained in detail
3.2 - Probability trees
Useful tool for conditional probability
Would you take this bet?
Thinking through probability and risk
Google Slides & LaTeX variants available Slides for each section
Slides 3 - Probability
LaTeX slides for full chapter on Github
Slides 3.1 - Defining probability
Google Slides version, can export to Powerpoint
Slides 3.2 - Conditional probability
Google Slides version, can export to Powerpoint
Slides 3.3 - Sampling from a small population
Google Slides version, can export to Powerpoint
Slides 3.4 - Random variables
Google Slides version, can export to Powerpoint
Slides 3.5 - Continuous distributions
Google Slides version, can export to Powerpoint
Lab - Probability
Software: R (Base), R (Tidyverse), Rguroo, Python, SAS, Stata
Chapter 4: Distributions
Distributions: 3 videos Videos for some sections
4.1 - Normal distribution
Core concepts and several examples
4.3A - Binomial distribution
Introduction to the binomial distribution
4.3B - Normal approximation to binomial
A useful technique for some binomial situations
Google Slides & LaTeX variants available Slides for each section
Slides 4 - Distributions
LaTeX slides for full chapter on Github
Slides 4.1 - Normal distributions
Google Slides version, can export to Powerpoint
Slides 4.2 - Geometric distribution
Google Slides version, can export to Powerpoint
Slides 4.3 - Binomial distribution
Google Slides version, can export to Powerpoint
Slides 4.4 - Negative binomial distribution
Google Slides version, can export to Powerpoint
Slides 4.5 - Poisson distribution
Google Slides version, can export to Powerpoint
Lab - Normal distribution
Software: R (Base), R (Tidyverse), Rguroo, Python, SAS, Stata
Class Activity: Sampling Distributions
As presented at Women in Stat and DS Conference
Normal distribution calculator
Online tool for normal distribution calculations
Chapter 5: Foundations for Inference
Foundations for Inference: 4 videos Videos for each section
5.1 - Variability of the sample proportion
Introduces the Central Limit Theorem
5.2 - Confidence intervals
Reporting a range, not just a point estimate
5.3 - Hypothesis testing
Introduced using numerical data (means)
Inference for other estimators
Generalizing the tools of inference
Why do we use 0.05 as a significance level?
Inquiring minds want to know -- let's explore!
Google Slides & LaTeX variants available Slides for each section
Slides 5 - Foundations for inference
LaTeX slides for full chapter on Github
Slides 5.1 - Point estimates and sampling variability
Google Slides version, can export to Powerpoint
Slides 5.2 - Confidence intervals
Google Slides version, can export to Powerpoint
Slides 5.3 - Hypothesis testing
Google Slides version, can export to Powerpoint
Lab - Intro to inference
Software: R (Base), R (Tidyverse), Rguroo, Python, SAS, Stata
Lab - Confidence levels
Software: R (Base), R (Tidyverse), Rguroo, Python, SAS, Stata
One-page inference guide
Covers one-sample and diff of means and proportions
Chapter 6: Inference for Categorical Data
Chapter 7: Inference for Numerical Data
Inference for categorical data: 8 videos Videos for each section
7.1A - t-distribution
Useful new distribution for inference for means
7.1B - Inference for one mean
Covers confidence intervals and hypothesis tests
7.2 - Paired data
Special case for difference of two means
7.3 - Difference of two means
When we have two independent samples
7.4 - Power calculations
Covers the scenario of the difference of two means
7.5A - Intro to ANOVA
Key concepts and ideas
7.5B - Conditions for ANOVA
How to check if ANOVA is reasonable
7.5C - Multiple comparisons
How we determine which groups are different
Google Slides & LaTeX variants available Slides for each section
Slides 7 - Inference for numerical data
LaTeX slides for full chapter on Github
Slides 7.1 - One-sample means with the t-distribution
Google Slides version, can export to Powerpoint
Slides 7.2 - Paired data
Google Slides version, can export to Powerpoint
Slides 7.3 - Difference of two means
Google Slides version, can export to Powerpoint
Slides 7.4 - Power calculations for difference of means
Google Slides version, can export to Powerpoint
Slides 7.5 - Comparing many means with ANOVA
Google Slides version, can export to Powerpoint
Lab - Inference for numerical data
Software: R (Base), R (Tidyverse), Rguroo, Python, SAS, Stata
Class Activity: Correlation
Students compare and correlate movie ratings
Sample size and power (one-sample)
Supplemental section: on power in the one-sample scenario
Better understand ANOVA calculations
Supplemental section: Details behind ANOVA
Online app for Central Limit Theorem for means
This is a Shiny app for exploration
Chapter 8: Introduction to Linear Regression
Intro to linear regression: 4 videos Videos for each section
8.1 - Ideas of fitting a line
Also covers residuals and correlation
8.2 - Fitting a least squares regression line
The notion of a "best fitting" line
8.3 - Types of outliers in regression
Points of high leverage and influential points
8.4 - Inference for linear regresion
Using the t-distribution for inference in regression
Google Slides & LaTeX variants available Slides for each section
Slides 8 - Linear regression
LaTeX slides for full chapter on Github
Slides 8.1 - Line fitting, residuals, and correlation
Google Slides version, can export to Powerpoint
Slides 8.2 - Fitting a line by least squares regression
Google Slides version, can export to Powerpoint
Slides 8.3 - Types of outliers in linear regression
Google Slides version, can export to Powerpoint
Slides 8.4 - Inference for linear regression
Google Slides version, can export to Powerpoint
Lab - Linear regression
Software: R (Base), R (Tidyverse), Rguroo, Python, SAS, Stata
Chapter 9: Multiple and Logistic Regression
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Openintro Statistics 4th Edition 3.6 Solutions
Source: https://www.openintro.org/book/os/
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