# Significantly Statistical Methods

#### a Free Online Statistics Course with JMP Software

Interested in learning about statistical inference, data analysis, and the philosophy of science? If so, this free online course is for you!

No previous statistical experience is necessary, and a 30-day trial for JMP to accompany this course is available at jmp.com/try. Start learning today!

###### Completely Free

###### DURATION: 15h30m

###### Complete in: 2-8 WEEKS

# Unit 1 - Theory of Statistics

### Module 1:1 - Introduction to Science and Data

Module overview

###### Concept Keywords

Introduction, Methods of Knowing, The Criterion of Falsifiability, The Scientific Method, Correlational Studies, Manipulation in Experimental Design, Control in Experimental Design, Correlational Methods of Research, Experimental Methods of Research, Basic Terminology, Sampling Error and Explanations in Science, Measurement Theory, Constructs and Operational Definitions, Scales of Measurement, Qualities of Measurement, The Anatomy and Interface of JMP, Saving and Sharing Work in JMP

### Module 1:2 - Visual Displays of Data

Module overview

###### Concept Keywords

Frequency Distributions, The Shape of Data in JMP, Graph Builder Basics in JMP

### Module 1:3 - Quantifying Distributions

Module overview

###### Concept Keywords

Central Tendency, Visualizing the Mean, Introduction to Variability, Deciding on an estimator for the population variance, Estimating the Population Variance, Simulating Variance Estimates

### Module 1:4 - Quantifying Locations

Module overview

###### Concept Keywords

Describing Locations of Scores in Distributions, Intro, Seeing the locations of scores in a distribution with JMP, Developing the Z-score, Using JMP to Standardize Scores, Characteristics and Uses of Z-scores

### Module 1:5 - Probability and The Normal Distribution

Module overview

###### Concept Keywords

Basics of Probability, Intro, Basic Definitions of Probability, Exploring Probability in JMP with a deck of cards, Exploring Probability in JMP with rolling dice, Exploring the probability of randomly selecting individuals in JMP, 1, Exploring the probability of randomly selecting individuals in JMP, 2, The Normal Distribution

### Module 1:6 - Sampling Distributions

Module overview

###### Concept Keywords

Sampling Distributions, Intro, Binomial Sampling Demo 1, Binomial Sampling Demo 2, Binomial Sampling Demo 3, Distribution of Sample Means, Intro 2, Sampling Distribution Definitions, and Making a Real Sampling Distribution, Sampling Distribution Simulation with a Large Population, n=2, Sampling Distribution Simulation with a Large Population, larger samples, Characteristics of Sampling Distributions, and the Standard Error, The Supreme Law of Unreason, The Central Limit Theorem

### Module 1:7 - Statistical Inference I

Module overview

###### Concept Keywords

Sampling Distributions and Statistical Inference, The Logic of Hypothesis Testing, Prediction and Standard of Evidence, Test Statistic, Evaluation, Z-test In JMP

### Module 1:8 - Statistical Inference II

Module overview

###### Concept Keywords

Hypothesis Testing Decisions, Probability of Errors, The Strength of Decisions, Never Accept The Null Hypothesis, Visualizing Statistical Power, Statistical Power, Effect size, Variability, Sample Size, Alpha Level, Directional Hypotheses

### Module 1:9 - Basic Hypothesis Testing with t

Module overview

###### Concept Keywords

Introducing the t-statistic, One-Sample t-test, Logic and JMP, Developing the dependent measures t-test, The dependent measures t-test in JMP I, The dependent measures t-test in JMP II, Developing the independent measures t-test, The independent measures t-test in JMP I, The independent measures t-test in JMP II, Advantages and Concerns with Repeated Measures Designs, Deciding on a hypothesis test tree

# Unit 2 - Linear Models of Means

### Module 2:1 - Linear Models

Module overview

###### Concept Keywords

Functional Relationships, Statistical Relationships, Components of the One Factor Linear Model, The Distribution of Error in the One Factor Linear Model, The One Factor Linear Model - Sample Form, Estimating the Mean Squared Error, Inference about treatment effect from the one factor linear model

### Module 2:2 - ANOVA and the General Linear Test

Module overview

###### Concept Keywords

The Fisher-Snedecor Distribution and the analysis of variance, The Analysis of Variance Test Statistic, Partitioning the Sums of Squares in ANOVA, The Sums of Squares Treatment in ANOVA, Degrees of Freedom in One Factor ANOVA, The General Linear Test

### Module 2:3 - ANOVA and pairwise comparisons in JMP

Module overview

###### Concept Keywords

One-Factor ANOVA in JMP with Fit Y by X, One-Factor ANOVA in JMP with Fit Model, Pairwise Comparisons in JMP with Fit Y by X, Pairwise Comparisons in JMP with Fit Model, The Problem of Multiplicity and Alpha Escalation, Controlling Alpha for Planned Comparisons, Controlling Alpha for Unplanned Comparisons, Multiple Comparison Summary

### Module 2:4 - Factorial ANOVA

Module overview

###### Concept Keywords

Introduction to Factorial Designs, The Three Possible Tests in a Two-Way Factorial Design, The Two-Factor Linear Model, Modeling the Cell Means in the Two Factor Linear Model, Estimating the Main Effects in the Two Factor Linear Model, Estimating the Interaction in the Two Factor Linear Model, The Structure of the Interaction Offsets in the Two Factor Linear Model, The Cell Means as a Function of the Row, Column, and Interaction Effects, Residuals and Test of Effects in the Two Factor Linear Model

### Module 2:5 - Factorial ANOVA in JMP

Module overview

###### Concept Keywords

Factorial Analysis Introduction, Defining the Model in Fit Model, Fit Model Output, Using Fit Model to Understand Effects, Prediction Profiler, Pairwise Comparisons in Fit Model, Pairwise Comparisons in Fit Model - Contrasts, Factorial ANOVA Larger than 2x2, Factorial ANOVA, Testing Slices in Factorial Designs

### Module 2:6 - General Linear Model Assumptions

Module overview

###### Concept Keywords

Assumptions of the General Linear Model, Introduction, Homogeneity of Error for One Factor Models, Homogeneity of Error for Two Factor Models, Testing Homogeneity of Variance with Fit Y by X in JMP, Testing Homogeneity of Variance with Variability Gauge Chart

### Module 2:7 - Multifactor ANOVA

Module overview

###### Concept Keywords

Three Factor ANOVA, Data Quality Check before 3-Factor ANOVA, Using Fit Model in JMP to set up a Three-Factor ANOVA, Interpreting the Three-Factor ANOVA in JMP

### Module 2:8 - One Factor Repeated Measures

Module overview

###### Concept Keywords

Introduction to Repeated Measures Designs and the One Factor Repeated Measures Model, Fixed and Random Factors, Visualizing the effect of modeling individual subject offsets, The Subject x Treatment Interaction Source as Error, Data Arrangements, Stacked and Split Data, Stacking and Splitting Data in JMP, Data Quality Check before Repeated Measures Analysis, Using Fit Model in JMP to set up a One factor Repeated Measures Analysis, Interpreting the Fit Model output in JMP for a One Factor Repeated Measures Analysis, Using the Repeated Measures Add-in for JMP, One Factor

### Module 2:9 - Factorial Repeated Measures

Module overview

###### Concept Keywords

Factorial Repeated Measures Model, Using the JMP Repeated Measures Add-In for Factorial Repeated Measures, Interpreting the Factorial Repeated Measures Output in JMP, Introduction to Repeated Measures with Between Subjects Factors, Using the JMP Repeated Measures Add-In for Mixed-Factor Repeated Measures, Interpreting the Mixed Factor Repeated Measures Output in JMP

# Unit 3 - Linear Regression Models

### Module 3:1 - Simple Linear Regression Models

Module overview

###### Concept Keywords

Introduction to Regression Models, The One Predictor Linear Regression Model, Least Squares, The Conditional Mean for Y given X

### Module 3:2 - Simple Linear Regression Tests

Module overview

###### Concept Keywords

Error and Tests of Effect in Regression Models Module, T-Test for the SImple Regression Slope, T-Test for the Simple Regression Y-Intercept, Analysis of Variance Test of the Simple Regression Slope

### Module 3:3 - Simple Linear Regression in JMP

Module overview

###### Concept Keywords

Simple Linear Regression in JMP with Fit Y by X, Simple Linear Regression in JMP with Fit Model