Stress-Free Statistics For College And Ibdp/Ap Students

A Stress-Free Way to Learn Statistics

What you'll learn

College Statistics

Statistics and Probability

Requirements

An open mind, a positive spirit, and a willingness to leave math-anxiety behind

Knowledge of arithmetic (addition, subtraction, multiplication, division) of whole numbers.

Knowledge of basic algebraic operations and simple equation solving.

Description

Stress-Free Statistics Learning These materials will help students master Probability and Statistics and will test their knowledge with 100+ practice questions and solutions. Students can use this curriculum as a stand-alone course or as a supplement to any statistics courses they are currently taking. This course will help students master statistics in colleges and universities.Created by Dr. Laura Roberts, Ph.D.EnglishSyllabusChapter 1 – The Purpose of StatisticsChapter 2 – Cases, Variables, and QuestionsChapter 3 – Frequency Distributions and Visual Displays of DataChapter 4 – Measures of Central TendencyChapter 5 – Measures of VariabilityChapter 6 – The Normal Distribution and Standard ScoresChapter 7 – The Bivariate Normal DistributionChapter 8 – Regression and PredictionChapter 9 – ProbabilityChapter 10 – Statistical InferenceChapter 11 – Introduction to Hypothesis TestingChapter 12 – Estimation Issues and the t DistributionChapter 13 – Statistics for Categorical Dependent VariablesChapter 14 – Inferences about Correlations and Fisher’s Z TransformationThe chapters are aligned with those in the following text:Glass, G. V. & Hopkins, K. D. (1996). Statistical Methods in Education and Psychology. Needham Heights, MA: Allyn & BaconThis course includes· 17+ hours of on-demand video· 68+ powerpoint presentations including notes/transcripts· Over 100 quiz and exam questions plus answer keys and explanations!· 100+ downloadable resources· Access on mobile and TVCollege students and IBDP/AP students in any major will benefit from this course.  It is designed to be user-friendly for all students, including college students in non-math majors, non-STEM majors, arts, humanities, liberal arts, EDUCATIONAL LEADERSHIP STUDENTS, education majors, and nursing students. It is especially helpful for math-anxious students.For courses that are offered fully online, these materials can be used as an extra resource to add to the good things instructors are already doing. Or, it can be used as a stand-alone statistics curriculum.Often, educators want to describe a set of characteristics of a group of people. For example, teachers may want to describe how much time their students spend on homework, or how resilient and empathic their students are, or any number of other characteristics. In this course, educators will learn how to describe characteristics of people in the following ways:· suppose an educator could boil down set of numbers to a single number (e.g. how many minutes a typical student spends on homework each night); what would be the best number to represent the typical values? These statistics could be used: mean, median, or mode.· Of course, not all students study the same amount each night. So, one may want to know how much students' scores tend to vary around the typical value. These statistics could be used: range, variance, and standard deviation.· Suppose an educator wants to show time spent on homework to a parent group. Instead of presenting a long list of numbers and watching the parents’ eyes glaze over, the educator may want to create visual or graphic "pictures" of the numbers (data). These statistical methods could be used: bar graphs, histograms, and scatterplots.· With these foundational ideas (typical value, variation around the typical value, visual “pictures” of the data, educators can then understand and effectively use the following higher level statistics:· using a sample mean and standard deviation to estimate a population mean and standard deviation. In other words, using a typical value (such as a mean) and typical variation (such as a standard deviation) of a small group of students to estimate a typical value and typical variation of a large group of students. Think of how much time (and money) an educator can save by gathering data on a small group and using that information to make estimates for a larger group (with a precise degree of confidence). For example, an educator could assess the time spent on homework from a small number of students and derive an estimate of time spent on homework for the whole school.· Suppose an educator wanted to know if time spent on homework was correlated with the extent to which parents value learning. An educator can use statistics to find out how much these two characteristics (or variables) are correlated. In other word, is time spent on homework greater when parents place a higher value on learning?· make predictions about the future using past information. For example, suppose a growing number of teachers were calling in sick due to a virus spreading through the school. For example, suppose the number of absentees double each day. The principal needs to know how many substitute teachers they will need next week and the week after. A principal could estimate the number of absentees next week and the week after (assuming no steps were taken to stop the virus.)Students will also understand the following statistical methods (the usefulness of these statistics will become clearer as the course progresses):· working with special characteristics of normal distributions and z-scores.· understanding probability with fun games of chance such as cards and dice.· understanding important probability-related concepts such as union, intersection, independent events, dependent events, and Bayes' theorem.· make inferences about nonparametric variables (i.e. grouping variables, e.g. “special needs students” versus “not special needs students.”)Requirements· The most important requirements are a willingness to keep an open mind, a ready spirit, and dedication to the topic.· The second most important requirement is a willingness to let go of math anxiety.The following math skills are a plus, but, be assured, explanations will begin at a basic level and build gradually to higher and higher levels of complexity:· Knowledge of arithmetic (addition, subtraction, multiplication, division) of whole numbers.· Knowledge of basic algebraic operations and simple equation solving.DescriptionStatistics for Math-Anxious College StudentsThis 70-lesson course includes video and text explanations of all statistical topics, and it includes more than 200 quiz and exam questions (with solutions) to help students monitor their understanding at each step along the way. Lessons are short and tight and easy to digest.EACH LESSON INCLUDES:Video Tutorials: I present each lesson in short, easily-digestible chunks. I begin with an overview of the lesson; then I explain each concept in careful detail including concrete examples; and I conclude with a summary and segue to the next lesson. Students will also find each lesson has a fun, entertaining aspect because I have illustrated each one with abstract art from around the world. The great thing about learning with narrated video tutorials is students can stop the action at any point and go over the concept again if it doesn’t “land” the first time through. My modus operandi when I created these videos was to distill the topic down to the essential concepts and to present a streamlined, elegant version of each statistical concept. Many stats textbooks present unnecessary overkill and students become overwhelmed and discouraged. I think students will find my method a refreshing alternative to the traditional method.Notes: Students will also get an inside peek into all the powerpoint slides with notes and transcripts for each lesson. These lessons are to statistics what Sparknotes are for books. Students will find the essential concepts without the showy overkill that appears in lots of textbooks.Quizzes: At each step along the way, students can test their knowledge with quizzes, a midterm exam, and a final exam. I have also provided them with the answer keys, worked problems, and careful explanations of each answer. I have confidence that students will have a great experience with my teaching method.HERE'S WHAT SOME STUDENTS HAVE TOLD ME ABOUT “STRESS-FREE STATISTICS FOR COLLEGE STUDENTS” AND MY TEACHING APPROACH:“I think Dr. Roberts is the BEST at teaching practical statistics to graduate students. The materials she has developed are useful and help make the complex simple and understandable.”Dr. George White,Professor of Education at Lehigh University, Bethlehem, PA,Coordinator of the Educational Leadership ProgramDr. Laura Roberts has been an exceptional resource for my study. She is a stellar statistician and dissertation consultant. She raised the quality of my paper by helping me with the design of the study and then, working with me through the subsequent data analysis. David Harris, Oxford Learning CentreCambridge, OntarioDr. Roberts is one of the best professors I have had in my higher education in terms of both her teaching strategies and her content expertise in statistics and research methods. Statistics and mathematics have always been challenging subjects for me. Although I passed my university statistics courses with top grades, I did not truly understand most statistical methods. In addition, the complexity of statistical methods I used in my dissertation study far exceeded any methods I learned about in my doctoral courses. Dr. Roberts is patient, she uses visual models, and she gives detailed and precise explanations that make even the most complex statistics easier to understand. To be clear, however, she also did not spoon-feed me with answers nor do the hard work for me. Rather, she would give examples, then she would ask deep, thought provoking questions, requiring me to explain my thinking and demonstrate my understanding. Dr. Roberts is skilled at designing assessments and giving valuable feedback. Every time I sent another draft of my dissertation, Dr. Roberts offered a thoughtful balance of authentic compliments and detailed critique. Without coming off as demanding in a negative way, she was able to push, prod and convince me to keep at my writing and improve it beyond any level I would have previously imagined I was capable. Dr. Roberts has a passion for research that is contagious. Before I started the doctoral program, I really did not intend to continue in scholarly research. I was motivated to earn the degree for professional reasons, but I did not necessarily value being a scholar researcher. As a direct result of Dr. Roberts’s encouragement and enthusiasm, I have presented my dissertation findings at professional conferences, and I am working towards publishing articles in peer-reviewed journals. Dr. Laura Roberts has my highest recommendation for anyone looking for an outstanding mentor in research/statistical methods and dissertation writing. She also has my unreserved recommendation for any teaching or research position in higher education. She is an exceptional professor and researcher. Any higher research and education institution would be lucky to have her on their faculty.Sean Areias, Ed.D.Interim SuperintendentAmerican International School of LagosLaura’s greatest skill is her work with statistics - not only bringing the highest level of quantitative analysis to the data, but "translating" it into layman's terms for her collaborators and a wider audience to appreciate.Steve MancusoSuperintendent of ColegioInternacional Puerta La CruzDr. Laura Roberts is a multi talented researcher, doctoral coach, professor of qualitative and quantitative statistics courses and many many other talents. She has been a doctoral mentor to a variety of international school heads, superintendents of schools and hundreds of educators. She has presented at and attended AAIE and the CP Task Force meeting we held at the US Department of State. She is a scholar of high repute and…a wonderful mentor and guide.Christine Brown, Ed.DUS State Department Liaison to the European Council of International SchoolsIs this course a good match for you?Are you a non-math major who is required to take statistics? Do you break into a cold sweat at the thought of facing another required stat assignment? Have you searched the web looking for help and found nothing that really works for you?Relax. You’re in the right place.My name is Dr. Laura Roberts and I was a student just like you. I was a nervous wreck class after stat class. Dropping wasn’t an option – I needed those classes to graduate. And what I discovered after I earned my doctorate and began teaching my own classes … was this.Learning statistics doesn’t have to be scary.Over time, I developed teaching methods that my students found effective. They learned without the frustration and the stress – and they passed their classes with flying colors!I’m the stat guru behind Right Angle Educators. I’ve put together a series of video tutorials that will help you grasp statistical concepts quickly and easily.What do my video tutorials have that other online statistics videos do not have?· Unit objectives· Lesson objectives· Type-written slides (believe it or not, many online videos offered by other companies are in hand-written script that is very hard to read.)· Clearly sequenced and integrated lesson presentationsUser friendly lessons that are based on my 30+ years of experience as a statistics professor. I found many students have high math anxiety. High anxiety interferes with learning. My approach is intentionally designed to lower students’ anxiety and increase their learning.And if you’re wondering how well my methods actually work, here’s a statistic for you.Half of the people who start a doctorate never finish.There are many reasons for this, but one of the biggest? Students don’t “get” the statistical skills necessary for original research. Now here’s another stat – I have a 95% success rate for getting grad students from ABD to PhD.Okay, so let’s get started. My materials will calm your fears and get you feeling like top-notch number cruncher in no time at all.

Overview

Section 1: Chapter 1: Introduction

Lecture 1 Chapter 1: The Purpose of Statistics

Section 2: Chapter 2: Variables and Measurement Scales

Lecture 2 Chapter 2: Variables and Measurement Scales

Lecture 3 Chapter 2A: The Right Angle Research Alignment Table for Research Organization

Lecture 4 Chapter 2B: The Right Angle a la Carte Table for Selecting the Correct Test

Section 3: Chapter 3: Frequency Distributions and Visual Displays of Data

Lecture 5 Unit 3.1: Tabular Methods

Lecture 6 Unit 3.2: Graphical Methods

Lecture 7 Unit 3.3: Clustering Data

Lecture 8 Unit 3.4: Percentile Scores and Percentile Ranks

Section 4: Chapter 4: Measures of Central Tendency

Lecture 9 Unit 4.1: Mode, Median, and Mean

Lecture 10 Unit 4.2: Summation Notation

Lecture 11 Unit 4.3: Linear Transformations

Lecture 12 Unit 4.4 - Other Properties of the Mean

Lecture 13 Unit 4.5 - Closing Thoughts on Measures of Central Tendency

Section 5: Chapter 5: Measures of Variability

Lecture 14 Unit 5.1 - Range and Q

Lecture 15 Unit 5.2 - Variance and Standard Deviation

Lecture 16 Unit 5.3 - Linear Transformations

Lecture 17 Unit 5.4 - Other Properties of s and variance

Lecture 18 Unit 5.5 - Closing Thoughts on Measures of Variability

Section 6: Chapter 6 - The Normal Distribution and Standard Scores

Lecture 19 Unit 6.1 - Standard Scores

Lecture 20 Unit 6.2 - Scaled Scores

Lecture 21 Unit 6.3 - The Normal Distribution

Lecture 22 Unit 6.4 - Some Examples

Lecture 23 Unit 6.5 - Shape and Skew

Lecture 24 Unit 6.6 - Shape and Kurtosis

Section 7: Chapter 7 -The Bivariate Normal Distribution

Lecture 25 Unit 7.1 - Qualitative Aspects

Lecture 26 Unit 7.2 - The Correlation Coefficient

Lecture 27 Unit 7.3 - Interpretation

Lecture 28 Unit 7.4 - Properties

Lecture 29 Unit 7.5 - Bivariate Normal Distributions

Section 8: Chapter 8 - Regression and Prediction

Lecture 30 Unit 8.1 - Equation of a Line

Lecture 31 Unit 8.2 Regression Line

Lecture 32 Unit 8.3 - Alternate Forms

Lecture 33 Unit 8.4 - Prediction Model

Section 9: Chapter 9 - Probability

Lecture 34 Unit 9.1 - Basic Concepts

Lecture 35 Unit 9.2 - Fundamental Rules

Lecture 36 Unit 9.3 - Conditional Probabilities

Lecture 37 Unit 9.4 - Permutations and Combinations

Lecture 38 Unit 9.5 - Looking Ahead

Section 10: Chapter 10 -Chapter 10 Statistical Inference: Sampling and Interval Estimation

Lecture 39 Unit 10.1 - Sampling Methods

Lecture 40 Unit 10.2 - Sampling Distributions

Lecture 41 Unit 10.3 - Sampling Limits

Lecture 42 Unit 10.4 - Confidence Limits

Lecture 43 Unit 10.5 -Estimator Properties

Section 11: Chapter 11 - Part 1: Introduction to Hypothesis Testing

Lecture 44 Unit 11.1 - The Logic of Hypothesis Testing

Lecture 45 Unit 11.2 - Hypothesis Types

Lecture 46 Unit 11.3 - Some Examples

Lecture 47 Unit 11.4 - Level of Significance

Lecture 48 Unit 11.5 - Critical Values and Critical Regions

Lecture 49 Unit 11.6 - Directional Tests

Lecture 50 Unit 11.7 - More Examples of Hypothesis Testing

Lecture 51 Unit 11.8 - Errors of Inference

Section 12: Chapter 11 - Part 2: Introduction to Hypothesis Testing

Lecture 52 Unit 11.9 - A Type II Overview

Lecture 53 Unit 11.10 - Influential Factors, part a

Lecture 54 Unit 11.11 - Influential Factors, part b

Lecture 55 Unit 11.12 - Power Analysis

Lecture 56 Unit 11.13 - Some Examples

Lecture 57 Unit 11.14 - Closing Thoughts

Section 13: Chapter 12 -Estimation issues, the t-distribution, degrees of freedom, t-tables

Lecture 58 Unit 12.1 - t distribution

Lecture 59 Unit 12.2 - Independent vs. Related Samples

Lecture 60 Unit 12.3 - Independent Sample Case

Lecture 61 Unit 12.4 - Model Assumptions

Lecture 62 Unit 12.5 - Related Sample Case

Lecture 63 Unit 12.6 - Difference Score Method

Section 14: Chapter 13 - Statistics for Categorical Dependent Variables

Lecture 64 Unit 13.1 - The Single Sample Case

Lecture 65 Unit 13.2 - Goodness of Fit Test

Lecture 66 Unit 13.3 - An Example

Lecture 67 Unit 13.4 - Test of Independence

Lecture 68 Unit 13.5 - McNemar's Test

Section 15: Chapter 14 - Inferences about Correlation Coefficients

Lecture 69 Unit 14.1 - A Single Sample Case

Lecture 70 Unit 14.2 - Fisher's Z Transformation

Lecture 71 Unit 14.3 - Two Independent Sample Case

Lecture 72 Unit 14.4 - Two Dependent Correlations

All statistics students at the graduate or undergraduate level, Including math-anxious students