#### Section 1 : Introduction

 Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 2 The big picture 2:11

#### Section 2 : Excel Statistics Fundamentals

 Lecture 3 Using Excel functions 6:12 Lecture 4 Understanding Excel statistics functions 5:55 Lecture 5 Working with Excel graphics 4:23 Lecture 6 Installing the Excel Analysis Toolpak

#### Section 3 : Types of Data

 Lecture 7 Differentiating data types 4:20 Lecture 8 Independent and dependent variables 1:0

#### Section 4 : Probability

 Lecture 9 Defining probability 1:55 Lecture 10 Calculating probability 6:14 Lecture 11 Understanding conditional probability 2:6

#### Section 5 : Central Tendency

 Lecture 12 The mean and its properties 2:16 Lecture 13 Working with the median 2:23 Lecture 14 Working with the mode 1:53

#### Section 6 : Variability

 Lecture 15 Understanding variance 4:30 Lecture 16 Understanding standard deviation 2:48 Lecture 17 Z-scores 3:2

#### Section 7 : Distributions

 Lecture 18 Organizing and graphing a distribution 3:58 Lecture 19 Graphing frequency polygons 2:5 Lecture 20 Properties of distributions 3:5 Lecture 21 Probability distributions 4:10

#### Section 8 : Normal Distributions

 Lecture 22 The standard normal distribution Lecture 23 Meeting the normal distribution family 1:32 Lecture 24 Standard normal distribution probability 4:10 Lecture 25 Visualizing normal distributions 1:37

#### Section 9 : Sampling Distributions

 Lecture 26 Introducing sampling distributions 3:45 Lecture 27 Understanding the central limit theorem 3:53 Lecture 28 Meeting the t-distribution 2:24

#### Section 10 : Estimation

 Lecture 29 Confidence in estimation 4:45 Lecture 30 Calculating confidence intervals 5:16

#### Section 11 : Hypothesis Testing

 Lecture 31 The logic of hypothesis testing Lecture 32 Type I errors and Type II errors 3:22

#### Section 12 : Testing Hypotheses about a Mean

 Lecture 33 Applying the central limit theorem 4:17 Lecture 34 The z-test and the t-test 8:2

#### Section 13 : Testing Hypotheses about a Variance

 Lecture 35 The chi-squared distribution 3:37

#### Section 14 : Independent Samples Hypothesis Testing

 Lecture 36 Understanding independent samples 2:54 Lecture 37 Distributions for independent samples 3:48 Lecture 38 The z-test for independent samples 2:27 Lecture 39 The t-test for independent samples 7:0

#### Section 15 : Matched Samples Hypothesis Testing

 Lecture 40 Understanding matched samples Lecture 41 Distributions for matched samples 2:4 Lecture 42 The t-test for matched samples 4:34

#### Section 16 : Testing Hypotheses about Two Variances

 Lecture 43 Working with the F-test 3:0

#### Section 17 : The Analysis of Variance

 Lecture 44 Testing more than two parameters 3:22 Lecture 45 Introducing ANOVA 6:22 Lecture 46 Applying ANOVA 1:41

#### Section 18 : After the Analysis of Variance

 Lecture 47 Types of post-ANOVA testing 2:5 Lecture 48 Post-ANOVA planned comparisons 3:7

#### Section 19 : Repeated Measures Analysis

 Lecture 49 What is repeated measures Lecture 50 Applying repeated measures ANOVA 2:36

#### Section 20 : Hypothesis Testing with Two Factors

 Lecture 51 Statistical interactions 5:4 Lecture 52 Two-factor ANOVA 5:21 Lecture 53 Performing two-factor ANOVA 2:5

#### Section 21 : Regression

 Lecture 54 Understanding the regression line 5:46 Lecture 55 Variation around the regression line 3:11 Lecture 56 Analysis of variance for regression 5:16 Lecture 57 Multiple regression analysis 3:16

#### Section 22 : Correlation

 Lecture 58 Hypothesis testing with correlation 2:26 Lecture 59 Understanding correlation 2:39 Lecture 60 The correlation coefficient 3:0 Lecture 61 Correlation and regression 2:0