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