Goodness of Fit
Hypothesis Test - Single Mean
Hypothesis Test - Single Proportion
Spreadsheet-based Statistics Labs
This collection of spreadsheet-based labs was funded as part of the Digital Learning Research Network (dLRN) made possible by a grant from the Bill and Melinda Gates Foundation. The labs were adapted from the Statistics book, “Introduction to Statistics,” published by OpenStax College. The original labs used graphing calculators and were found within the book after each chapter. These interactive spreadsheet-based labs are effective for online and face-face courses. They may also be used with the book (see Resource: Lab Mapping to Book Chapters) or stand-alone.
Authors: Barbara Illowsky PhD, Foothill-De Anza Community College District; Larry Green PhD, Lake Tahoe Community College; James Sullivan, Sierra College; Lena Feinman,College of San Mateo; Cindy Moss, Skyline College; Sharon Bober, Pasadena Community College; Lenore Desilets, De Anza Community College.
Section 1: Univariarate Data
The students will design and carry out a survey.
- The students will analyze and graphically display the results of the survey.
Section 2: Normal Distribution
Compare the distribution of empirical data to the Normal Distribution.
Section 3: Central Limit Theorem
Students will examine properties of the Central Limit Theorem by randomly selecting 10 groups of 5 colleges and analyzing the distributions of the means of the student enrollment. Four students will collaborate on this lab. The group should be divided into two teams
Section 4: Hypothesis Test - Single Mean
Conduct a hypothesis test for a single mean and interpret the results.
Section 5: Hypothesis Test - Single Proportion
Conduct a hypothesis test for a single proportion and interpret the results.
Section 6: Goodness of Fit
Students will collect and evaluate birth Day data to determine if they fit a Uniform Distribution. Four students will collaborate on this lab.
Section 7: Linear Regression
Evaluate the relationship between two variables to determine the significance of the linear correlation.
If linear correlation exists, determine and construct the linear regression equation between two variables.