Statistics 101: Principles of Statistics
About This Course
Whether you'd like a refresher on the differences between descriptive and inferential statistics or are looking to hone your ability to analyze residuals, this course's video lessons have got you covered. Instructors discuss topics ranging from sample variance and box plots to conditional probabilities and zscores. Areas of study include:
 Statistical types, data models and levels of measurement
 Methods for interpreting data tables and plots
 Random sample types and sampling distributions
 Uses of population parameters and confidence intervals
 Problem solving with probability and linear regression
 Theories and steps associated with hypothesis testing
If statistics lessons on confidence intervals and tdistributions sound a bit daunting, rest assured that our experienced instructors can walk you through the ins and outs of this discipline with plenty of graphical representations, examples and illustrations. Moreover, the video tags and transcripts allow you to quickly access lesson content covering difficult subjects as many times as you need. If you feel you'd also benefit from sample problems and review questions, there are selfassessment quizzes to help you out in this area as well.
Course Topics
Course Chapter  Objectives 

Overview of Statistics  Learn to identify the differences between inferential and descriptive statistics and populations and parameters. Explore various data types and levels of measurement alongside methods for selecting experiment models. Get tips for recognizing biased or misleading uses of statistics. 
Summarizing Data  Find out how to calculate mean, median and mode and describe the shape of a data set. Study procedures for finding maximums, minimums, outliers and percentiles. Identify standard deviations and shifts in the mean as well as methods for ordering and ranking data. 
Tables and Plots  Discover joint, marginal and conditional frequencies and identify the differences between bivariate and univariate data. Determine how to interpret stem and leaf displays, histograms and frequency polygons as well as dot and box plots, bar graphs, pie charts and twoway tables. Use data to make predictions and calculate percent increase. 
Probability  Understand basic set theory and classical approaches to probability. Learn how to find the probability of simple, compound and complementary events as well as independent and dependent events. Observe how simple conditional probabilities can be applied to realworld scenarios. 
Discrete Probability Distributions  Practice graphing and interpreting discrete probability distributions used to find expected values. Examine binomial probabilities and learn to recognize properties of normal and binomial distributions. 
Continuous Probability Distributions  Learn to graph continuous probability distributions and find their expected values. Explore the uses of these distributions to estimate area and population percentages. 
Sampling  Explore simple, stratified, cluster and systematic random samples. Study the law of large numbers and discuss the central limit theorem, sampling distributions and sample means. 
Statistical Estimation  Identify the relationships between confidence intervals, sample size and sample means. Examine properties of the tdistribution and learn the differences between biased and unbiased estimators and point and interval estimation. 
Hypothesis Testing  Follow steps in hypothesis testing for small and large independent samples, matched pairs and proportions. Understand the differences between type I and type II errors. 
Regression and Correlation  Learn how to create and interpret scatterplots, solve problems using linear regression and analyze residuals. Practice finding the correlation coefficient and the coefficient of determination. Distinguish between correlation and causation and find out how to transform nonlinear data. 
48% developed We are actively adding lessons to this course. You can study for exams and supplement your learning right now. For students preparing for an exam, we'll label this course 100% developed when it is ready to prepare you for the exam on its own.
Overview of Statistics
All Videos in Overview of Statistics
Summarizing Data
All Videos in Summarizing Data
 1. What is the Center in a Data Set?  Definition, Lesson & Quiz
 2. Mean, Median & Mode: Measures of Central Tendency
 3. How to Calculate Mean, Median, Mode & Range
 4. Calculating the Mean, Median, Mode & Range: Practice Problems, Lesson & Quiz
 5. Visual Representations of a Data Set: Shape, Symmetry & Skewdness
 6. Unimodal & Bimodal Distributions: Definition, Examples & Quiz
 7. The Mean vs the Median: Differences, Uses, Lesson & Quiz
 8. Spread in Data Sets: Definition, Example, Lesson & Quiz
 9. Maximums, Minimums & Outliers in a Data Set: Lesson & Quiz
 10. Quartiles & the Interquartile Range: Definition, Formulate & Examples
 11. Finding Percentiles in a Data Set: Formula, Examples & Quiz
 12. Calculating the Standard Deviation
 13. The Effect of Linear Transformations on Measures of Center & Spread: Lesson & Quiz
 14. Population & Sample Variance: Definition, Formula & Examples
 15. Ordering & Ranking Data: Process, Example, Lesson & Quiz
Tables and Plots
All Videos in Tables and Plots
 1. Frequency & Relative Frequency Tables: Definition & Examples
 2. Cumulative Frequency Tables: Definition, Uses & Examples
 3. How to Calculate Percent Increase with Relative & Cumulative Frequency Tables
 4. Creating & Reading Stem & Leaf Displays
 5. Creating & Interpreting Histograms: Process & Examples
 6. Creating & Interpreting Frequency Polygons: Process & Examples
 7. Creating & Interpreting Dot Plots: Process & Examples
 8. Creating & Interpreting Box Plots: Process & Examples
 9. Understanding Bar Graphs and Pie Charts
 10. Making Arguments & Predictions from Univariate Data
 11. What is Bivariate Data?  Definition & Examples
 12. What is a TwoWay Table?
 13. Joint, Marginal & Conditional Frequencies: Definitions, Differences & Examples
Probability
All Videos in Probability
 1. Mathematical Sets: Elements, Intersections & Unions
 2. Events as Subsets of a Sample Space: Lesson & Quiz
 3. Probability of Simple, Compound and Complementary Events
 4. Probability of Independent and Dependent Events
 5. Probability of Independent Events: The 'At Least One' Rule
 6. How to Calculate Simple Conditional Probabilities
 7. The Relationship Between Conditional Probabilities & Independence: Lesson & Quiz
 8. Applying Conditional Probability & Independence to Real Life Situations: Lesson & Quiz
 9. The Addition Rule of Probability: Definition, Examples & Quiz
 10. The Multiplication Rule of Probability: Definition, Examples & Quiz
 11. Math Combinations: Formula and Example Problems
 12. How to Calculate a Permutation
 13. How to Calculate the Probability of Permutations
 14. Relative Frequency & Classical Approaches to Probability: Lesson & Quiz
Discrete Probability Distributions
All Videos in Discrete Probability Distributions
 1. Random Variables: Definition, Types & Examples
 2. Developing Discrete Probability Distributions Theoretically & Finding Expected Values
 3. Developing Discrete Probability Distributions Empirically & Finding Expected Values
 4. Dice: Finding Expected Values of Games of Chance
 5. Blackjack: Finding Expected Values of Games of Chance with Cards
Continuous Probability Distributions
All Videos in Continuous Probability Distributions
Sampling
All Videos in Sampling
 1. Simple Random Samples: Definition & Examples
 2. What is Random Sampling?  Definition, Conditions & Measures
 3. Stratified Random Samples: Definition, Characteristics & Examples
 4. Cluster Random Samples: Definition, Selection & Examples
 5. Systematic Random Samples: Definition, Formula & Advantages
 6. Understanding the Law of Large Numbers
Regression & Correlation
All Videos in Regression & Correlation
 1. Creating & Interpreting Scatterplots: Process & Examples
 2. Simple Linear Regression: Definition, Formula & Examples
 3. Problem Solving Using Linear Regression: Steps, Examples & Quiz
 4. Interpreting the Slope & Intercept of a Linear Model: Lesson & Quiz
 5. How to Interpret Correlations in Research Results

Education Portal Instructors
Education Portal's 53 instructors bring a diverse array of experience and expertise to each course. From teaching philosophy in Athens, Greece, to exploring the mystery of genetics, each instructor is uniquely qualified to bring students the best online learning experience possible. Meet them now!
Contact Information
If you have a general question about Education Portal, please contact customer support.