Objectives
Part 1: An Introduction to Data Analysis Using Excel To interpret, summarize and present numerical data using the digital tool Microsoft program Excel. To plot numerical data as a graph and determine an equation of a line. In addition, using the appropriate formatting functions to label your graph and creating a best fit line.
Part 2: Lab Report Writing Using LaGCC Institutional Data To communicate your interpretations of research data. This is done writing discussions and conclusions (using scientific language) and is often accompanied by data tables and graphs. To use your Microsoft Excel graphing skills to interpret, inquire and extrapolate meaning data to support your lab report conclusions To structure your written lab report in the format of: Abstract, Introduction, Material, Methods,Results, Discussion/Conclusion and References

This activity introduces students to loading and plotting data in MATLAB. Students explore scalar and vector time series and profile data commonly used in the field of Oceanography using data sets from publicly available sources or that they collected in local waters.

This is the website for Introduction to Modern Statistics, First Edition by Mine Çetinkaya-Rundel and Johanna Hardin. Introduction to Modern Statistics, which we’ll refer to as IMS going forward, is a textbook from the OpenIntro project.
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Copyright © 2021.
First Edition.
Version date: June 27, 2021.

This textbook and its supplements, including slides, labs, and interactive tutorials, may be downloaded for free at
openintro.org/book/ims.

This textbook is a derivative of OpenIntro Statistics 4th Edition and Introduction to Statistics with Randomization and Simulation 1st Edition by Diez, Barr, and Çetinkaya-Rundel, and it’s available under a Creative Commons Attribution-ShareAlike 3.0 Unported United States License. License details are available at the Creative Commons website: creativecommons.org.

Source files for this book may be found on GitHub at github.com/openintrostat/ims.

lecture slides for a calculus-based course in Introduction to Probability & Statistics; suitable for sophomore or junior level of an undergraduate program

Students learn about video motion capture technology, becoming familiar with concepts such as vector components, magnitudes and directions, position, velocity, and acceleration. They use a (free) classroom data collection and processing tool—the ARK Mirror—to visualize and record 3-D motion. The Augmented Reality Kinematics (ARK) Mirror software collects data via a motion detector. Using an Orbbec Astra Pro 3D camera or Microsoft Kinect (see note below), students can visualize and record a robust set of data and interpret them using statistical and graphical methods. This lesson introduces students to just one possible application of the ARK Mirror software—in the context of a high school physics class. Note: The ARK Mirror is ported to operate on an Orbbec platform. It may also be used with a Microsoft Kinect, although that Microsoft hardware has been discontinued. Refer to the Using ARK Mirror and Microsoft Kinect attachment for how to use the ARK MIrror software with Microsoft Kinect.

This activity is a guided inquiry investigation where students gather data on which soil is the best for growing plants. Student will interpret their data, and develop a conclusion from the data. The student will determine which type of soil they would like to use in the next activity of making their own terrariums. The data collected could lead to further questions, which can be investigated in some extension activities.

Students will use tables and visualizations of data about geographic mobility to explore rates and patterns of migration within, and immigration to, the United States. Using Census Bureau data tools, students will learn about past reasons for migration and immigration and understand the internal and external stresses of fluctuations in population.

This learning video addresses a particular problem of selection bias, a statistical bias in which there is an error in choosing the individuals or groups to make broader inferences. Rather than delve into this broad topic via formal statistics, we investigate how it may appear in our everyday lives, sometimes distorting our perceptions of people, places and events, unless we are careful. When people are picked at random from two groups of different sizes, most of those selected usually come from the bigger group. That means we will hear more about the experience of the bigger group than that of the smaller one. This isn't always a bad thing, but it isn't always a good thing either. Because big groups ''speak louder,'' we have to be careful when we write mathematical formulas about what happened in the two groups. We think about this issue in this video, with examples that involve theaters, buses, and lemons. The prerequisite for this video lesson is a familiarity with algebra. It will take about one hour to complete, and the only materials needed are a blackboard and chalk.

This lesson explores if it is normal that world temperatures are rising at the currently observed fast pace.

This model-eliciting activity (MEA) challenges students to develop ideas about center and variability when making decisions based on data. Students examine data on departure delays for five airlines flying out of the Chicago O'Hare airport. The task is to develop a model to determine which airline has the best chance of departing on time. Students write a report that identifies the best airline and the reasoning behind their decision.

This model-eliciting activity has students determine how to create a fair judging scheme for a paper airplane contest while considering both the most accurate paper airplane and the best floater. Students are given a sample of data that includes multiple flights of paper airplanes by three different pilots. Each team writes a report describing how their judging scheme can be implemented by the judges of the contest. This activity could serve as an introduction to ideas of central tendency and variability. It can also set the stage for understanding the correspondence between data sets and their graphical representations. Alternatively, the activity could be the basis for student introduction to analysis of variance.

Students will analyze and compare census data on the earnings of people with different college majors. Then they will write their own word problems and draw conclusions about the data.

Students will count items in their classroom, record this information in a data table, and analyze and understand uses for such data.

An exercise in which students apply limiting reactants, mass ratios and percent yields to suggest an optimum industrial process. Cost figures are provided but students are told to come up with, and defend, their own criteria for their recommendation.

Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The collection provides student activity files with Excel instructions and Instructor Activity files with Excel instructions and solutions to problems.

Students will be able to perform linear regression analysis, find correlation coefficient, create a scatter plot and find the r-square using MS Excel 365. Students will be able to interpret data sets, describe the relationship between biological variables, and predict the value of an output variable based on the input of an predictor variable.

Students complete an exercise showing logarithmic relationships and examine how to find the linear regression of data that does not seem linear upon initial examination. They relate number of BMD scanners to time.

The 15.821 and 15.822 Sequence
Marketing research may be divided into methods that emphasize understanding “the customer” and methods that emphasize understanding “the market.” This course (15.821) deals with the customer and emphasizes qualitative methods (interviews, focus groups, Voice of the Customer, composing questions for a survey). The companion course (15.822) deals with the market and emphasizes quantitative methods (sampling, survey execution, quantitative data interpretation, conjoint analysis, factor analysis).
The methods covered in 15.821 are often used in the “front-end” of market research project, whose second-stage is a quantitative survey. The quality of information gathered in the second-stage is greatly enhanced in this way.
15.821 is designed for the nonspecialist, e.g., someone planning a career in general management, product or project management, R&D, advertising, or entrepreneurship. 15.822 teaches analytical techniques that are standard in consulting or marketing research, and is ideally suited for students planning careers in those fields.

After looking at census data, students will determine the birth years of children who were aged 8 through 11 in 2017. Then they will use their data to create a line graph, with an appropriate scale and axes labels, to compare and contrast the estimated number of births in their state and in another state during each year.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Amplicon sequencing has made the business of identifying bacteria directly from environmental samples routine work, but there’s much room for improvement. Long-read sequencing technologies have helped cover the information gaps that plague short-read sequencing, but obtaining accurate results from complex metagenomes remains a challenge. A new study reports how LoopSeq can help boost microbial detection accuracy. Building on previous synthetic long-read (SLR) technologies, LoopSeq uses barcode-based molecular counting to construct accurate long reads from short-read sequences. Experiments confirmed that LoopSeq produces long reads with low error rates. In fact, LoopSeq produced more error-free reads of different lengths compared with other commercially available long-read technologies, and in a real-world test of six samples of US retail meat. LoopSeq could differentiate between strains within species of bacteria identified by the CDC as potential foodborne pathogens..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This book introduces readers to methods of analyzing insurance data. The online version contains many interactive objects (quizzes, computer demonstrations, interactive graphs, video, and the like) to promote deeper learning.