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Master Statistics & Probability 2020: Beginner-To-Advanced

Name: Master Statistics & Probability 2020: Beginner-To-Advanced
Rating: 4.0 (278 reviews)

Includes 100+ Meaningful Lectures, Homework Assignments, Quizzes, Comprehensive Exams & An Online Statistics Textbook!

Created byMath Rules

Last updated 6/2020

English

What you'll learn

Define various terms and concepts in Statistics, including samples, statistics and variables
Contrast various sampling methods along with various biases and invalid arguments
Organize data into structures that appeal to the eye
Measure data with various measuring tools, including measures of central tendency and measures of dispersion
Develop a deep productive conceptual understanding of Probability in a new unique way
Draw conclusions about random discrete variables based on their various probability distributions
Identify normal probability distributions along with their general properties
Synthesize and interpret normal distributions using the standard normal distribution
Apply and connect properties of the standard normal distribution
Analyze and apply various interpretations of The Central Limit Theorem to sample distributions
Construct a sound and coherent Philosophy of Science and Statistics
Interpret and apply different one sample tests to data and create conclusions from the results
Compare and contrast two different samples mathematically using two sample hypothesis testing
Contrast the variation between multiple various samples using the One-Way ANOVA test
Coordinate multiple numerical variables with linear and exponential models
Coordinate multiple categorical variables using Chi-Squared distributions and tests

Course content

19 sections • 138 lectures • 13h 36m total length

Welcome To Statistics & Probability!2:59
Master statistics and probability from basics to advanced topics, including data analysis, distributions, central limit theorem, hypothesis testing, and key methods like z and t tests, anova, and chi-square.
Here's What You Will Learn About In This Course4:18
Take a complete in-depth statistics and probability course from basics to advanced topics, covering sampling, distributions (including normal), hypothesis testing, and means, medians, standard deviations, with calculator guidance.
How To Get Your Free Textbook3:20
How To Get Free Homework, Quizzes & Exams4:16
Learn to access free homework, quizzes, and exams on myopenmath.com by entering course id 46272 and leaving the enrollment key blank.
Who is This Course Designed For? What Materials Do You Need?1:58

What is Statistics?3:13
Samples and Populations1:15
Examples of Samples and Populations2:20
Examine samples and populations using real examples like Texas cats and Houston samples. Learn that a sample can be a subset or even the entire population.
Statistics, Parameters and Inferences4:43
Examples of Statistics, Parameters and Inferences4:33
Descriptive Statistics vs Inferential Statistics4:22
Examples of Good Inferences vs Bad Inferences6:03
Examine good inferences vs bad inferences by comparing how well a sample represents the population. Choose a representative sample to yield accurate inferences, while biased samples misestimate parameters.
What is a Variable?5:37
Explore what a variable is and how observations differ from statistics. See how data, samples, and measurements reveal variables that vary across observations, like height or gender.
Types of Variables in Statistics2:58
Identify and distinguish the types of variables in statistics, including numerical (discrete and continuous) and qualitative variables, with examples such as age, shoe size, gender, and measured units.
Examples of Types of Variables in Statistics3:49
Independent and Dependent Variables3:15
Define independent variables and dependent variables, explain their relationship, and show how dependent variables change together while independent variables may not, including non-linear connections.
Examples of Independent and Dependent Variables1:54

Association vs Causation1:40
Explore the difference between association and causation, showing how dependent variables relate easily but proving causation is much harder.
Examples of Association and Causation2:19
Explore the difference between association and causation using the hours of sleep and test grades example, and examine how one may cause the other or share a hidden influence.
Anecdotal Evidence2:21
Examples of Anecdotal Evidence3:10
Examine how anecdotal evidence relies on small samples and personal stories, and contrast it with broader data from many countries using universal health care to illustrate why representative evidence matters.
Response to "Why Anecdotes Trump Data"1:26
Anecdotes can illuminate a sample and personalize findings in qualitative research, but should not be used to generalize to populations. Sometimes anecdotes trump data by shaping perception of research.
Census0:53
Define the census as a sample that is the entire population, and explain why large populations make data collection difficult. Preview upcoming detailed examples.
Examples of a Census2:48
Explore the concept of a census through examples like the US census and classroom averages. Distinguish between parameter and statistic when the population itself is the sample.
Sampling Methods2:07
Explore sampling methods for gathering a sample from a population, including simple random, stratified, cluster, and systemic sampling, with visuals illustrating random versus organized selection.
Examples of Sampling Methods2:25
Bias5:46
Define bias as a sample that does not represent the population. Explore selection bias, non-response bias, response bias, volunteer response bias, and convenience bias, and why random sampling improves inference.
Examples of Bias3:54
Explore common biases in data collection, including selection, response, volunteer response, non-response, and convenience biases, through practical examples that sharpen bias identification.
Experiments and Observational Studies2:02
Differentiate experiments from observational studies by showing that observational studies observe and measure variables without interference, while experiments manipulate and measure variables in treatment and control groups.
Examples of Experiments and Observational Studies2:58
Explore examples showing how giving a drink creates a treatment group while asking questions may be observational, and how a control group illustrates manipulation of variables defining an experiment.

Frequency, Relative Frequency and Cumulative Frequency4:10
Explore frequency, relative frequency, and cumulative frequency through a simple 10-person age example, learning how to compute counts, proportions, and running totals in data.
Organization of Data6:56
Learn to organize data with bar graphs, histograms, line and scatter plots, pie charts, and box plots, plus a brief note on stem and leaf plots.
Histograms8:32
Skewness2:19
Explore how histograms show right and left skewness by where most data clusters and where the tail extends. Learn to distinguish skewed distributions from a bell curve.

Measure of Central Tendency6:36
Examples of Measures of Central Tendency12:36
Explore central tendency by computing the mode, median, and mean from the numbers 2, 2, 5, 6, 9; the mode is the most frequent, the mean is 4.8.
The Measures of Dispersion7:02
Explore dispersion measures, including range, mean average deviation, and standard deviation, to quantify data spread, with standard deviation offering a clean mathematical approach.
Why n-1 and Why Square Root?7:22
Examples of Measures of Dispersion16:13
Explore examples of measures of dispersion—range, mean absolute deviation, and standard deviation—using data 2, 2, 5, 6, 9, and learn when to use sample standard deviation.
Box Plots and Five Number Summaries2:50
Examples of Box Plots and Five Number Summaries8:45

Probability Terminology5:59
Law Of Large Numbers6:34
Examine the law of large numbers through coin flips, comparing observed outcomes to theoretical probability, and explain objective versus subjective probability as relative frequency converges to probability with trials.
Probability Rectangles Make Probability So Much Easier To Understand!21:41
Discover probability rectangles, a visual method to compute card probabilities without memorizing formulas. It helps learners understand simple to complex scenarios, with and without replacement, using a five-card deck.
Conditional Probability2:55
Independent and Mutually Exclusive Events4:25
Master independent events and mutually exclusive events, and apply probability rules, including P(A|B)=P(A) for independence and P(A and B)=P(A)P(B); illustrate with replacement card draws and king probabilities.
Two Major Rules Of Probability4:59
Explore the two major probability rules: the multiplication rule for conditional probability using P(A|B)=P(A and B)/P(B), and the addition rule P(A or B)=P(A)+P(B)-P(A and B), illustrated with Venn diagrams.
Response to "Would You Take This Bet"2:06
Learn how probability governs daily decisions, emphasizing opportunity cost, outcome probabilities, and how ignoring probability can lead to bad choices, including evaluating bets.
Response to "What is Random?"1:53
Response to "What is NOT Random"1:31
Explore how the universe sits between randomness and not randomness, revealing a hybrid world. Learn how many variables labeled as random are actually slightly predictable, not truly random.
Introduction to Random Variables1:44
Examples of Random Variables3:31
Explore random variables through real examples like income and the sum of two dice. Link probability distributions to histograms to understand likely outcomes and their shapes.

Binomial Distribution8:26
Geometric Distribution7:59
Hypergeometric Distribution5:07
Explore hypergeometric distributions with two groups, sampling without replacement, and counting successes (X) in a fixed sample, highlighting non-independence and discrete, non-Bernoulli behavior.
Poisson Distribution6:08
Discover how the Poisson distribution models the number of independent events in a fixed time interval using the average rate, and its relation to the binomial.

Continuous Probability Functions6:03
The Uniform Distribution1:58
Learn the uniform distribution, a continuous distribution where every value within an interval is equally likely. For example, a number between 1 and 10 has height 1/9 and area 1.
The Exponential Distribution4:01
Explore the exponential distribution, a continuous model of time until an event occurs, with examples like earthquakes and light bulb failures, and applications in reliability.
Introduction to Bell Curves1:29
Examples of Bell Curves4:19

How to Read a Bell Curve2:38
68-95-99.7 Rule11:04
Examples of The 68-95-99.7 Rule11:00
Apply the 68-95-99.7 empirical rule to ACT scores using a bell-curve example, identify the mean, compute the standard deviation, and determine regions corresponding to 68%, 95%, and 99.7%.
Why Understanding Bell Curves is Hard4:32
Discover why bell curves are hard to master: each curve has a unique mean and standard deviation, and you convert any curve to the standard normal distribution.

The Standard Normal Distribution5:11
How Every Bell Curve is The Standard Normal Distribution6:36
Demonstrate that every bell curve is essentially the standard normal distribution in disguise, with numbers representing standard deviations from the mean and z-scores mapping to any other curve.
Developing a Formula8:59
Develop a formula to translate between IQ scores and z-scores using the standard normal distribution, mapping between mu=100, sigma=16 and mu=0, sigma=1; derive z=(x-mu)/sigma.
Example Using The Formula9:13
Convert sat scores to z-scores with z = (X - mean)/standard deviation; examples 1700, 1750, and 1900 show z = 1, 1.25, and 2 on standard normal curve.
Notation of Probability and Area Under The Curve2:22
Explore probability notation with P(x) for X > 5 or 0 < X < 5, and learn that the area under the bell curve equals 1, representing 100 percent.
Examples of Probability Notation8:49
Learn to read probability notation like p(X<5) and p(IQ≥100), and explore Z-scores and the standard normal distribution, including why exact outcomes have zero probability.
What is Percentile?3:08
Understand percentile as your position relative to the data, from 0 to 99. Explain that the 67th percentile means 67 percent did worse and 33 percent did better, rounded down.
Examples Using Percentile1:48
Learn how percentile measures relative standing by comparing a score to others, illustrated with 1200 out of 1600 and the 84th percentile being top 16%.
Reading a Z-table17:16
Read the z table to find the area to the left of a z score within the standard normal distribution, using a two-dimensional chart and practical examples.
Examples of Using The Z-table4:57
Using The Z-table Backwards1:03
Master z-table backwards by determining z scores from given left-tail areas, using the z chart and interactive computer examples.
Using Z-tables to Find The Area to The Right5:08
Using The Z table to Find The Z Score Given The Area to The Right7:42
use the z table to find the z score from the area to the right, noting the left and right areas sum to 1.
Using The Z-table to Find Area Between Two Z-scores13:26
Learn how to use the z table to find the area between two z-scores on the standard normal distribution, using geometry and left-area concepts.
Challenge Problem11:09

Requirements

You should be comfortable with high school algebra
Be ready to learn an insane amount of awesome stuff
Prepare to succeed in any first semester college level Statistics & Probability course
Brace yourself for tons of content

Description

***This course has been updated in 2020. Check out some of our lectures below!***

Included in the purchase of this course are:

100+ Comprehensive & Meaningful Lectures
Dozens Of Homework Assignments (Autograded) Via MyOpenMath
Several Quizzes (Autograded) To Assess Understanding Of Each Section
Comprehensive Midterm Exams And Final Exams (Autograded) To Assess Understanding Of The Entire Curriculum
A Complete Open-Source Online Statistics Textbook

You are probably asking yourself the question, "When and where will I use statistics and/or probability?" If you read any newspaper, watch television, or use the Internet, you will see statistical/probabilistic information. There are statistics about crime, sports, education, politics, and real estate. Typically, when you read a newspaper article or watch a television news program, you are given sample information and predictions. With this information, you may make a decision about the correctness of a statement, claim, or "fact." Statistical and probabilistic methods can help you make the "best educated guess."

Since you will undoubtedly be given information relating to statistics/probability at some point in your life, you need to know some techniques for analyzing the information thoughtfully. Think about buying a house or managing a budget. Think about your chosen profession. The fields of economics, business, psychology, education, biology, law, computer science, police science, and early childhood development require at least one course in statistics and probability.

MASTER STATISTICS & PROBABILITY 2020 IS SET UP TO MAKE STATISTICS & PROBABILITY EASY:

Master Statistics & Probability 2020 will teach you how to:

Recognize and differentiate between key terms.
Apply various types of sampling methods to data collection.
Create and interpret frequency tables.
Display data graphically and interpret graphs: stemplots, histograms, and box plots.
Recognize, describe, and calculate the measures of location of data: quartiles and percentiles.
Recognize, describe, and calculate the measures of the center of data: mean, median, and mode.
Recognize, describe, and calculate the measures of the spread of data: mean average deviation, standard deviation, and range.
Understand and use the terminology of probability.
Determine whether two events are mutually exclusive and whether two events are independent.
Calculate and interpret expected values.
Recognize and understand continuous probability density functions in general.
Recognize the normal probability distribution and apply it appropriately.
Recognize the standard normal probability distribution and apply it appropriately.
Compare normal probabilities by converting to the standard normal distribution.
Recognize central limit theorem problems.
Classify continuous word problems by their distributions.
Apply and interpret the central limit theorem for means.
Apply and interpret the central limit theorem for sums.
Calculate and interpret confidence intervals for estimating a population mean and a population proportion.
Interpret the Student's t probability distribution as the sample size changes.
Discriminate between problems applying the normal and the Student's t distributions.
Calculate the sample size required to estimate a population mean and a population proportion given a desired confidence level and margin of error.
Describe hypothesis testing in general and in practice
Conduct and interpret hypothesis tests for a single population mean, population standard deviation known.
Conduct and interpret hypothesis tests for a single population mean, population standard deviation unknown.
Conduct and interpret hypothesis tests for a single population proportion.

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

Videos: Watch engaging content involving interactive whiteboard lectures as I solve problems for every single math issue you’ll encounter in Statistics and Probability. We start from the beginning... I explain the problem setup and why I set it up that way, the steps I take and why I take them, how to work through the yucky, fuzzy middle parts, and how to simplify the answer when you get it.

Notes: The notes section of each lesson is where you find the most important things to remember. It’s like Cliff Notes for books, but for Statistics and Probability. Everything you need to know to pass your class and nothing you don’t.

Quizzes: When you think you’ve got a good grasp on a topic within a lecture, test your understanding with a quiz. If you pass, great! If not, you can review the videos and notes again or ask for help in the Q&A section.

Workbooks: Want even more practice? When you've finished the section, you can review everything you've learned by working through the bonus workbooks. These workbooks include tons of extra practice problems (all with detailed solutions and explanations for how to get to those solutions), so they're a great way to solidify what you just learned in that section.

YOU'LL ALSO GET:

Lifetime access to a free online Statistics & Probability textbook
Lifetime access to Master Statistics & Probability 2020
Friendly support in the Q&A section
Udemy Certificate of Completion available for download

So what are you waiting for? Learn Statistics and Probability in a way that will advance your career and increase your knowledge, all in a fun and practical way!

HERE'S WHAT SOME STUDENTS OF MASTER STATISTICS & PROBABILITY 2020 HAVE TOLD ME:

"Kody is a great teacher. He clearly presents complex ideas with great examples and explanations. His lectures are always very energetic and engaging. I have learned more in his class than I did in two semesters of statistics required for my BS in Software Engineering!" - Stew M.
“The videos are short and move along quickly, which lends itself to taking notes and quickly reviewing those notes between videos. The enunciation and clear and concise explanations are excellent. The class is coupled with an OpenText book online with its own video explanations and practice problems. All of this leaves me confident in the material!” - Calvin T.
"Great course. Good to learn all about statistics from very basic to advanced. The lectures are very easy to follow and understand. Nice instructor that explains clearly, he made the course interesting. I intend to finish the course ASAP." - Jugkapong C.
"So lovely the course as well as the teacher. I normally find it difficult to learn with Udemy but this course is easy to learn with." - Victoria S.
"Lecturer really clear and resources are really useful." - Xiao J.
"[Kody] very nicely defined all aspect from the basics of stats. Very helpful." - Pritinanda S.
"Great course. Simply explained. Covers a lot of areas in Statistics. Thanks Kody." - Fahad R.
"Excellent instructor and quality of course. Recommended strongly for beginners." - Laxman K.
"Thank you sir....the course was wonderful course....you designed it very well. It was meeting to my needs... Thank you :)" - Chanchala J.

Does the course get updated?

It’s no secret how Statistics and Probability curriculum is advancing at a rapid rate. New, more complex content and topics are changing Statistics and Probability courses across the world every day, meaning it’s crucial to stay on top with the latest knowledge.

A lot of other courses on Udemy get released once, and never get updated. Learning from an outdated course and/or an outdated version of Statistics and Probability can be counter productive and even worse - it could teach you the wrong way to do things.

There's no risk either!

This course comes with a full 30 day money-back guarantee. Meaning if you are not completely satisfied with the course or your progress, simply let Kody know and he will refund you 100%, every last penny no questions asked.

You either end up with Statistics and Probability skills, go on to succeed in college level Statistics and Probability courses and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…

You literally can’t lose. Ready to get started?

Enroll now using the “Add to Cart” button on the right, and get started on your way to becoming a master of Statistics and Probability. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, your Statistics & Probability class is waiting!)

Some content was used from Creative Commons, and attribution is provided within the curriculum of this course.

Who this course is for:

If you want to take ONE COURSE to learn everything you need to know about Statistics and Probability, take the full course
This course is for anyone who wants to learn about Statistics and Probability, regardless of previous experience
It's perfect for complete beginners with zero experience in Statistics and Probability
It's also perfect for students who have a decent understanding of Statistics and Probability but wish to learn even more advanced material

Master Statistics & Probability 2020: Beginner-To-Advanced

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 17min

Terminology In Statistics12 lectures • 44min

Sampling13 lectures • 34min

Organizing Data4 lectures • 22min

Measuring Data7 lectures • 1hr 1min

Introduction To Probability11 lectures • 57min

Probability Distributions4 lectures • 28min

Continuous Probability Distributions5 lectures • 18min

Understanding Normal Distributions (Bell Curves)4 lectures • 29min

The Standard Normal Distribution15 lectures • 1hr 47min

Requirements

Description

Who this course is for: