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Pedagogical foundation and features

  • Examples are placed strategically throughout the text to show students the step-by-step process of interpreting and solving statistical problems. To keep the text relevant for students, the examples are drawn from a broad spectrum of practical topics; these include examples about college life and learning, health and medicine, retail and business, and sports and entertainment.
  • Try It practice problems immediately follow many examples and give students the opportunity to practice as they read the text. They are usually based on practical and familiar topics, like the Examples themselves .
  • Collaborative Exercises provide an in-class scenario for students to work together to explore presented concepts.
  • Using the TI-83, 83+, 84, 84+ Calculator shows students step-by-step instructions to input problems into their calculator.
  • The Technology Icon indicates where the use of a TI calculator or computer software is recommended.
  • Practice, Homework, and Bringing It Together problems give the students problems at various degrees of difficulty while also including real-world scenarios to engage students.

Statistics labs

These innovative activities were developed by Barbara Illowsky and Susan Dean in order to offer students the experience of designing, implementing, and interpreting statistical analyses. They are drawn from actual experiments and data-gathering processes, and offer a unique hands-on and collaborative experience. The labs provide a foundation for further learning and classroom interaction that will produce a meaningful application of statistics.

Statistics Labs appear at the end of each chapter, and begin with student learning outcomes, general estimates for time on task, and any global implementation notes. Students are then provided step-by-step guidance, including sample data tables and calculation prompts. The detailed assistance will help the students successfully apply the concepts in the text and lay the groundwork for future collaborative or individual work.


  • Instructor’s Solutions Manual
  • Webassign Online Homework System
  • Video Lectures delivered by Barbara Illowsky are provided for each chapter.

About our team

Senior contributing authors

Barbara Illowsky De Anza College
Susan Dean De Anza College

Contributing authors

Abdulhamid Sukar Cameron University
Abraham Biggs Broward Community College
Adam Pennell Greensboro College
Alexander Kolovos
Andrew Wiesner Pennsylvania State University
Ann Flanigan Kapiolani Community College
Benjamin Ngwudike Jackson State University
Birgit Aquilonius West Valley College
Bryan Blount Kentucky Wesleyan College
Carol Olmstead De Anza College
Carol Weideman St. Petersburg College
Charles Ashbacher Upper Iowa University, Cedar Rapids
Charles Klein De Anza College
Cheryl Wartman University of Prince Edward Island
Cindy Moss Skyline College
Daniel Birmajer Nazareth College
David Bosworth Hutchinson Community College
David French Tidewater Community College
Dennis Walsh Middle Tennessee State University
Diane Mathios De Anza College
Ernest Bonat Portland Community College
Frank Snow De Anza College
George Bratton University of Central Arkansas
Inna Grushko De Anza College
Janice Hector De Anza College
Javier Rueda De Anza College
Jeffery Taub Maine Maritime Academy
Jim Helmreich Marist College
Jim Lucas De Anza College
Jing Chang College of Saint Mary
John Thomas College of Lake County
Jonathan Oaks Macomb Community College
Kathy Plum De Anza College
Larry Green Lake Tahoe Community College
Laurel Chiappetta University of Pittsburgh
Lenore Desilets De Anza College
Lisa Markus De Anza College
Lisa Rosenberg Elon University
Lynette Kenyon Collin County Community College
Mark Mills Central College
Mary Jo Kane De Anza College
Mary Teegarden San Diego Mesa College
Matthew Einsohn Prescott College
Mel Jacobsen Snow College
Michael Greenwich College of Southern Nevada
Miriam Masullo SUNY Purchase
Mo Geraghty De Anza College
Nydia Nelson St. Petersburg College
Philip J. Verrecchia York College of Pennsylvania
Robert Henderson Stephen F. Austin State University
Robert McDevitt Germanna Community College
Roberta Bloom De Anza College
Rupinder Sekhon De Anza College
Sara Lenhart Christopher Newport University
Sarah Boslaugh Kennesaw State University
Sheldon Lee Viterbo University
Sheri Boyd Rollins College
Sudipta Roy Kankakee Community College
Travis Short St. Petersburg College
Valier Hauber De Anza College
Vladimir Logvenenko De Anza College
Wendy Lightheart Lane Community College
Yvonne Sandoval Pima Community College

Sample ti technology

Disclaimer: The original calculator image(s) by Texas Instruments, Inc. are provided under CC-BY. Any subsequent modifications to the image(s) should be noted by the person making the modification. (Credit: ETmarcom TexasInstruments)

Questions & Answers

discuss the roles of vital and health statistic in the planning of health service of the community
given that the probability of
can man city win Liverpool ?
Emmanuel Reply
There are two coins on a table. When both are flipped, one coin land on heads eith probability 0.5 while the other lands on head with probability 0.6. A coin is randomly selected from the table and flipped. (a) what is probability it lands on heads? (b) given that it lands on tail, what is the Condi
Nusrat Reply
what is gradient descent?
Saurav Reply
It should be a Machine learning terms。
it is a term used in linear regression
what are the differences between standard deviation and variancs?
what is statistics
Emmanuel Reply
statistics is the collection and interpretation of data
the science of summarization and description of numerical facts
Is the estimation of probability
mr. zaini..can u tell me more clearly how to calculated pair t test
do you have MG Akarwal Statistics' book Zaini?
Haai how r u?
maybe .... mathematics is the science of simplification and statistics is the interpretation of such values and its implications.
can we discuss about pair test
what is outlier?
Usama Reply
outlier is an observation point that is distant from other observations.
what is its effect on mode?
Outlier  have little effect on the mode of a given set of data.
How can you identify a possible outlier(s) in a data set.
The best visualisation method to identify the outlier is box and wisker method or boxplot diagram. The points which are located outside the max edge of wisker(both side) are considered as outlier.
@Daniel Adunkwah - Usually you can identify an outlier visually. They lie outside the observed pattern of the other data points, thus they're called outliers.
what is completeness?
I am new to this. I am trying to learn.
I am also new Dom, welcome!
please my friend i want same general points about statistics. say same thing
outliers do not have effect on mode
also new
I don't get the example
Hadekunle Reply
ways of collecting data at least 10 and explain
Ridwan Reply
Example of discrete variable
Bada Reply
sales made monthly.
I am new here, can I get someone to guide up?
dies outcome is 1, 2, 3, 4, 5, 6 nothing come outside of it. it is an example of discrete variable
continue variable is any value value between 0 to 1 it could be 4digit values eg 0.1, 0.21, 0.13, 0.623, 0.32
How to answer quantitative data
Alhassan Reply
what's up here ... am new here
sorry question a bit unclear...do you mean how do you analyze quantitative data? If yes, it depends on the specific question(s) you set in the beginning as well as on the data you collected. So the method of data analysis will be dependent on the data collecter and questions asked.
how to solve for degree of freedom
Quantitative data is the data in numeric form. For eg: Income of persons asked is 10,000. This data is quantitative data on the other hand data collected for either make or female is qualitative data.
Degree of freedom is the unconditionality. For example if you have total number of observations n, and you have to calculate variance, obviously you will need mean for that. Here mean is a condition, without which you cannot calculate variance. Therefore degree of freedom for variance will be n-1.
data that is best presented in categories like haircolor, food taste (good, bad, fair, terrible) constitutes qualitative data
vegetation types (grasslands, forests etc) qualitative data
I don't understand how you solved it can you teach me
Caleb Reply
solve what?
What is the end points of a confidence interval called?
lower and upper endpoints
Class members write down the average time (in hours, to the nearest half-hour) they sleep per night.
William Reply
how we make a classes of this(170.3,173.9,171.3,182.3,177.3,178.3,174.175.3)
why is always lower class bundry used
Assume you are in a class where quizzes are 20% of your grade, homework is 20%, exam _1 is 15%,exam _2 is 15%, and the final exam is 20%.Suppose you are in the fifth week and you just found out that you scored a 58/63 on the fist exam. You also know that you received 6/9,8/10,9/9 on the first
Diamatu Reply
quizzes as well as a 9/11,10/10,and 4.5/7 on the first three homework assignment. what is your current grade in the course?
the answer is 2.6
if putting y=3x examine that correlation coefficient between x and y=3x is 1.
Aadrsh Reply

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Source:  OpenStax, Introductory statistics. OpenStax CNX. May 06, 2016 Download for free at http://legacy.cnx.org/content/col11562/1.18
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