In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.

This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 & TI-89 calculators. A separate document is provided on the website with examples in
SPSS.

Students new to statistics are sure to benefit from this fully ADA accessible and relevant textbook. The examples resonate with everyday life, the text is approachable, and has a conversational tone to provide an inclusive and easy to read format for students.

This textbook incorporates the compilation of my notes over the years teaching introductory probability and statistics courses used to expand and enhance upon the open educational resource Statistics Using Technology by Kathryn Kozak, with some additional material adapted from OpenIntro Statistics by Diez,
Barr, and Çetinkaya-Rundel. Both texts are licensed under CC BY-SA 4.0. This textbook, except for the cover art, is licensed under a Creative Commons Attribution-Share Alike 4.0 International License (CCBY-SA 4.0).

Review and content editing by Jennifer Ward. Recommended definition edits and glossary terms by Dr. Naghmeh Daneshi. Alternative text for images provided by Whitney Cave. Additional exercises from Whitney Cave and from MyOpenMath: https://www.myopenmath.com/. Copy editing by Walter Benson. Cover art by James Tadlock https://www.artstation.com/redhedron.

For instructors, there is a complete solution manual and two companion online homework courses. An adaptive learning system called Realizeit which has content from the text, videos and homework problems. A free MyOpenMath course with problems divided by chapter. Email Rachel Webb for more information.

" This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology)."

Passion-Driven Statistics is an NSF-funded, multidisciplinary, project-based curriculum that supports students in conducting data-driven research, asking original questions, and communicating methods and results using the language of statistics. The curriculum supports students to work with existing data covering psychology, health, earth science, government, business, education, biology, ecology and more. From existing data, students are able to pose questions of personal interest and then use statistical software (e.g. SAS, R, Python, Stata, SPSS) to answer them. The e-book is presented in pdf format for ease of use across platforms. Check out our public website http://passiondrivenstatistics.com/.Email Kristin.Flaming@gmail.com for access to our FREE faculty resources.

The students will play a classic game from a popular show. Through this they will see the probabilty that the ball will land each of the numbers with more accurate results coming from repeated testing.

In this section, you will learn the introductory concepts of a simple linear regression analysis including a discussion on the explanatory and response variables in the study. You will learn how to analyze the strength and predictability of regression models.

In this section, you will learn how to analyze the strength and predictability of regression models and how to perform a significance test to statistically see if a linear relationship exists between the explanatory and response variables.

In this section, you will be introduced to important statistical terms. You will learn the difference between a population vs sample, parameter vs. statistic, descriptive vs inferential statistics. You’ll learn how to differentiate between quantitative and qualitative data. You will be introduced to a six-step data analysis process.

In this section you’ll learn how to create and interpret various graphical displays of data. You will be introduced to selecting the appropriate graphical display for the data given. You will learn how to construct and interpret graphs for qualitative data and graphs for quantitative data.

In this section you’ll learn how to calculate measures of spread to include sample variance and standard deviation. You will apply the Empirical Rule to normally distributed data and find measures of relative standing.

In this section, you will be introduced to numerical summaries that describe the center of the distribution to include the mean, median, and mode. You will learn how to construct and interpret a box-and-whisker plot as a way to summarize and display quantitative data.

In this section, you will be introduced to the concept of probability and how it applies to real-world problems. You will learn important vocabulary in probability theory including simple event, event, sample space. You will learn the difference between mutually exclusive and independent events.

In the previous section, you were introduced to the basic concepts of probability theory. In this section you’ll learn how to apply the theory to calculating various types of probabilities using two-way tables (contingency tables).

In this section, you will be introduced to the concept of probability distributions. You will be able to identify the properties and differences between discrete and continuous probability distributions. You will build discrete probability distributions from chance experiments.

In the previous section, you were introduced to the basic concepts of discrete probability distributions. In this section you’ll learn a specific case of discrete distributions called the binomial distribution. You will identify properties and chance experiments asocial with the binomial distribution and use the formula to calculate probabilities of events.

In this section, you will learn the properties of the normal distribution and the standard normal distribution. You will learn how to calculate probabilities by solving for the z-score and reading the z-table.

In this section, you will be introduced to the concept of sampling distribution including a discussion on the point estimate, standard error, and margin of error. You will learn how to construct and interpret a confidence interval for a population proportion.

In the previous section, you were introduced to the concepts of estimation and constructing confidence intervals. In this section, we will extend this to estimating population means.

In this section, you will be introduced to the process of performing a hypothesis test using a four-step testing procedure. You will also learn about the potential errors in hypothesis testing.