<< Chapter < Page Chapter >> Page >

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.

Ancillaries

  • 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

calculators
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

Write a short note on skewness
Saran Reply
and on kurtosis too
Hiren
What is events
Manish Reply
who is a strong man?
Desmond Reply
Can you sir plz provide all the multiple choice questions related to Index numbers.?
Hiren Reply
about probabilty i have some questions and i want the solution
asad Reply
What is hypothesis?
Rosendo Reply
its a scientific guess
ted
A hypothesis in a scientific context, is a testable statement about the relationship between two or more variables or a proposed explanation for some observed phenomenon. In a scientific experiment or study, the hypothesis is a brief summation of the researcher's prediction of the study's findings.
Hamzah
Which may be supported or not by the outcome. Hypothesis testing is the core of the scientific method.
Hamzah
statistics means interpretation analysis and representation of numerical data
Ramzan
To check the statment or assumption about population parameter is xalled hypothesis
Ali
hypothesis is simply an assumption
Patrick
what is the d.f we know that how to find but basically my question is what is the d.f? any concept please
asad Reply
Degrees of freedom aren’t easy to explain. They come up in many different contexts in statistics—some advanced and complicated. In mathematics, they're technically defined as the dimension of the domain of a random vector.
Hamzah
d.f >> Degrees of freedom aren’t easy to explain. They come up in many different contexts in statistics—some advanced and complicated. In mathematics, they're technically defined as the dimension of the domain of a random vector.
Hamzah
But we won't get into that. Because degrees of freedom are generally not something you needto understand to perform a statistical analysis—unless you’re a research statistician, or someone studying statistical theory.
Hamzah
And yet, enquiring minds want to know. So for the adventurous and the curious, here are some examples that provide a basic gist of their meaning in statistics.
Hamzah
The Freedom to Vary First, forget about statistics. Imagine you’re a fun-loving person who loves to wear hats. You couldn't care less what a degree of freedom is. You believe that variety is the spice of life Unfortunately, you have constraints. You have only 7 hats. Yet you want to wear a different
Hamzah
hat every day of the week. On the first day, you can wear any of the 7 hats. On the second day, you can choose from the 6 remaining hats, on day 3 you can choose from 5 hats, and so on.
Hamzah
When day 6 rolls around, you still have a choice between 2 hats that you haven’t worn yet that week. But after you choose your hat for day 6, you have no choice for the hat that you wear on Day 7. You must wear the one remaining hat. You had 7-1 = 6 days of “hat” freedom—in which the hat you wore
Hamzah
That’s kind of the idea behind degrees of freedom in statistics. Degrees of freedom are often broadly defined as the number of "observations" (pieces of information) in the data that are free to vary when estimating statistical parameters.
Hamzah
please help me understand binomial distribution
Nnenna Reply
binomial distribution and poisson both are used to estimate the number of successes probable against the. probable failures. the difference is only that BINOMIAL dist. is for discrete data while POISSON is used for continuous data.
Salman
What do you need to understand?
Angela
The binomial distribution is frequently used to model the number of successes in a sample of size n drawn with replacement from a population of size N. If the sampling is carried out without replacement, the draws are not independent and so the resulting distribution is a hypergeometric distribution
Hamzah
poisson distribution is also for discrete data set. The difference is when the probability of occurring an event is very little and the sample size is extra large then we use poisson distribution.
Neil
Neil yes you got it and very interested answer you gave
jamilu
How to know if the statement is 1 tail or 2 tail?
Coleen Reply
1 tail if greater than pr less than.2 tail if not equal.
Jojo
in such a case there is no sufficient information provided to develop an alternative hypothesis and we can decide between only two states i.e either the statement is EQUAL TO or NOT EQUAL TO under given conditions
Salman
for 1tail there must be certain criteria like the greater than or less than or some probability value that must be achieved to accept or reject the original hypothesis.
Salman
for example if we have null hypothesis Ho:u=25 Ha:u#25(not equal to 25) it would be two tail if we say Ho:u=25 Ha:u>or Ha:u<25 it would be consider as one tail I hope you will be understand #Coleen
Shabir
yes its true. now you have another problem. so share.
ibrar
what is z score
Esperanza Reply
How to find z score through calculator
Esperanza
Different data sets will have different means and standard deviations, so values from one set cannot always be compared directly with those from another. The z-score standardizes normally distributed data sets, allowing for a proper comparison and a consistent definition of percentiles across data s
Hamzah
what are random number
Saif Reply
how to compute the mean with a long method
Fria Reply
there is a shortcut method for calculating mean long methid doesn't make any sense.
Neil
what are probability
Saif
probability mass function
Saif
probability density function
Saif
there are many definitions of probability. which one is, the ratio of favourable outcomes & total outcomes.
suhail
distribution used for modeling/(find probabilities) of discrete r.v. is called p.m.f
suhail
distribution used for modeling/(find probabilities) of continued r.v, called p.d.f
suhail
lets use short method using calculator.... store yo data n smply get your mean
Flavian
if 1 calorie =4.12 kj, what is the total kj value of this dish
Jacqueline Reply
summation of values of x1 x2 x3 ,,,,xn divided by total number n if it is with frequency its like this summation of values of x1f1+x2f2+x3f3+xnfk divided by summation of frequencies like f1+f2+f3+fk
farah Reply
Please keep in mind that it's not allowed to promote any social groups (whatsapp, facebook, etc...), exchange phone numbers, email addresses or ask for personal information on our platform.
QuizOver Reply

Get the best Introductory statistics course in your pocket!





Source:  OpenStax, Introductory statistics. OpenStax CNX. May 06, 2016 Download for free at http://legacy.cnx.org/content/col11562/1.18
Google Play and the Google Play logo are trademarks of Google Inc.

Notification Switch

Would you like to follow the 'Introductory statistics' conversation and receive update notifications?

Ask