kingers Posted Monday at 02:40 PM Report Share Posted Monday at 02:40 PM Master Statistics Using R: Coding, Concepts, Applications Published 8/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 19.12 GB | Duration: 28h 17mLearn R, data analysis, visualization, inference, and regression through real-world statistical practice. What you'll learn R Programming & Data Wrangling R programming for data analysis Writing clean reproducible R code Tidyverse data manipulation skills Data wrangling with dplyr and tidyr Visualizing data with ggplot2 Handling messy, real-world datasets Creating clear, professional plots Organizing projects for reproducibility GitHub code-along scripts included Core Statistical Concepts Understanding sampling variability Exploring statistical distributions Central limit theorem in practice Standard error and confidence intervals Logic of hypothesis testing Null vs alternative hypotheses P-values and significance testing Comparing statistical tests effectively Building analytic intuition hands-on Inferential Statistics & Modeling Conducting t-tests in R ANOVA and group comparisons Chi-square test for categorical data Linear regression modeling in R Understanding assumptions of tests Interpreting effect sizes in R Practical Data Analysis Realistic messy data scenarios Iterative analysis and refinement Making decisions with uncertainty Interpreting results like a researcher Guided exercises for practice Step-by-step code demonstrations Building confidence as a data analyst Applying statistics to real projects Requirements No knowledge or skills are required for this course Coding experience in any language is helpful but not necessary Familiarity with basic stats terms like descriptive, inferential, mean, standard deviation, but not necessary Description Unlock the power of data by learning statistics the modern way-hands-on, intuitive, and with real-world tools. This course is designed for students, researchers, and professionals who want to move beyond memorizing formulas and truly understand how to analyze data. Using R programming and the tidyverse, you'll build both the coding fluency and the statistical intuition you need to work like a real analyst.We'll start at the ground level: organizing messy datasets into tidy data, writing clean and reproducible code, and visualizing information effectively. From there, you'll gain practical experience with the logic of inference-sampling variability, distributions, confidence intervals, and hypothesis testing-through approachable, step-by-step examples. Along the way, you'll see how t-tests, chi-square, correlation, and regression all fit together under the same framework.But this isn't just another lecture-heavy course. You'll code alongside me with guided exercises, code-along scripts, and real datasets, building a skill set you can apply immediately to assignments, theses, publications, or workplace projects. You'll also explore more advanced techniques like bootstrapping, resampling, and regression modeling, reinforcing how these tools extend beyond the classroom and into research and professional practice.By the end of this course, you'll be able to:Write R code that is clean, efficient, and reproducible.Apply a broad set of inferential statistical methods to real data.Visualize results in clear and compelling ways.Develop the confidence to approach data like an experienced analyst.Whether you're new to statistics, transitioning into a data-focused role, or seeking a stronger foundation for research, this course offers a comprehensive, structured, and practical pathway to mastering statistics with R. Join today, and start building the tools to transform data into knowledge. Overview Section 1: Introduction Lecture 1 Course Overview Lecture 2 Why Use R? Lecture 3 Prerequisites and How to Rock This Course Lecture 4 The Math is Simple, the Challenge is Choice Lecture 5 Installing RStudio and Downloading Course Code Lecture 6 Policy on Sharing the Code Section 2: Overview of Part 1 Lecture 7 Overview of Part 1 Section 3: Basic R Coding Lecture 8 Markdown and Packages Lecture 9 Using R Like a Calculator Lecture 10 Variable Types Lecture 11 Variable Types Concepts Lecture 12 Assignment in R Lecture 13 Vectors Lecture 14 Arrays and Matrices Lecture 15 Advanced Indexing with Matrices Lecture 16 Lists Lecture 17 Lists, the Double Square Braket Lecture 18 File Paths Lecture 19 Data Frames and Tibbles part 1 Lecture 20 Data Frames and Tibbles part 2 Lecture 21 If Statements Lecture 22 Vectorized If Statements Lecture 23 For Loops Lecture 24 Logs and Exponents Lecture 25 Functions part 1 Lecture 26 Functions part 2 Lecture 27 Helper Script Code Organization Lecture 28 Getting Help From ChatGPT Section 4: The Tidyverse and Data Import Lecture 29 What is Tidying Data? Lecture 30 Import Data From the Internet Lecture 31 Renaming Variables part 1 Lecture 32 Renaming Variables part 2 Lecture 33 Group and Summarize Data Lecture 34 Filtering to Get Rid of NA Rows Lecture 35 Filtering for Subsetting Lecture 36 Selecting Lecture 37 Combining Data From Multiple Sources Lecture 38 Using Across With Summarize Lecture 39 Pivoting Data Frames Lecture 40 Importing Text and CSV Files Lecture 41 Example Cardiovascular Health Data part 1 Lecture 42 Example Cardiovascular Health Data part 2 Section 5: GGPlot and Creating Great Graphs Lecture 43 GGplot and the Grammar of Graphics Lecture 44 Lines and Scatter Plots part 1 Lecture 45 Lines and Scatter Plot part 2 Lecture 46 Bar Plots Lecture 47 Histograms Lecture 48 Aesthetic Customization Section 6: Part 2 Overview Lecture 49 Part 2 Overview Section 7: What Are (is?) Data? Lecture 50 Are Data Plural? Lecture 51 What to Measure? Lecture 52 Accuracy and Precision Lecture 53 Types of Data Lecture 54 Samples V. Populations Lecture 55 Case Studies and Anecdotes Lecture 56 Faking Data Section 8: Simulating Data From Different Distributions Lecture 57 Project Descriptions and Goals Lecture 58 Simulate Random Data From Several Distributions Lecture 59 Central Tendency Concepts Lecture 60 Central Tendency Calculations part 1 Lecture 61 Central Tendency Calculations part 2 Lecture 62 Parametric Variability Concepts Lecture 63 Parametric Variability Calculations Lecture 64 Non-Parametric Variability Concepts Lecture 65 Non-Parametric Variability Calculations Lecture 66 Plotting Error Bars to Show Variability Lecture 67 Conclusions for Descriptive Stats with Simulated Data Section 9: Determining Which Distribution Data Come From Lecture 68 Introduction: Can Descriptive Stats Tell Us About Distributions? Lecture 69 Describing Real Data Lecture 70 Comparing Empirical Data to Analytic Distributions part 1 Lecture 71 Comparing Empirical Data to Analytic Distributions part 2 Lecture 72 Q-Q Plots Concepts Lecture 73 Q-Q Plots Calculations part 1 Lecture 74 Q-Q Plots Calculations part 2 Lecture 75 What Measures Would You Choose to Describe These Data? Section 10: Transforming Data Lecture 76 How Transforming Data Makes It Interpretable Lecture 77 Log Transformation for Normalization Conceptual Lecture 78 Log Transformation for Normalization Calculations Lecture 79 Constant Value Transformations Lecture 80 Properties of the Normal Distribution Lecture 81 Z-score Conceptual Lecture 82 Z-score Calculations part 1 Lecture 83 Z-score Calculations part 2 Lecture 84 Model-Based vs. Empirical Probabilities Lecture 85 Log and z-Score Transformations Combined Lecture 86 Min-Max Scaling Conceptual Lecture 87 Min-Max Scaling Calculations Lecture 88 Reviewing Transformations Section 11: Identify and Remove Outliers Lecture 89 How Can We Decide Which Data Are Valid? Lecture 90 Garbage In Garbage Out Lecture 91 Z-scores for Outlier Detection Concepts Lecture 92 Z-scores for Outlier Detection part 1 Lecture 93 Z-scores for Outlier Detection part 2 Lecture 94 Modified Z-scores Concepts Lecture 95 Modified Z-scores Calculations part 1 Lecture 96 Modified Z-scores Calculations part 2 Lecture 97 Super Extreme Values Lecture 98 Transform and Z-score Combined Lecture 99 Dealing With Outliers Lecture 100 Importance of Domain Knowledge Lecture 101 Reviewing Outlier Concepts Section 12: Probability Lecture 102 Probability Basic Concepts Lecture 103 Probability Versus Proportion Lecture 104 Data Types for Probability Lecture 105 Calculating Probability Lecture 106 Upper and Lower Tails Lecture 107 Doing Math with Probability Lecture 108 Probability with Flipping Coins Lecture 109 Rarity of Multiple Events Section 13: Overview of Part 3 Lecture 110 Overview of Part 3 Section 14: Z-test Lecture 111 Using the Z-test to make Inferences Lecture 112 Probability of a Sample Lecture 113 Sampling Distribution of the Mean Lecture 114 Null Hypothesis Testing Lecture 115 Performing a Z-test Lecture 116 Outcomes of a Z-test Lecture 117 Sample Size and Error part 1 Lecture 118 Sample Size and Error part 2 Lecture 119 Central Limit Theorem Lecture 120 Z-test Review Section 15: T-tests Lecture 121 T-tests: a More Flexible Mean Comparison Lecture 122 Degrees of Freedom Lecture 123 One-Sample t-test Lecture 124 Confidence Intervals Lecture 125 Paired-Sample T-test Lecture 126 Independent Samples T-test part 1 Lecture 127 Independent Samples T-test part2 Lecture 128 Comparing Paired v. Independent Sample Tests Lecture 129 T-test v. z-test Lecture 130 Reporting T-test Results Section 16: Multiple Comparisons Lecture 131 Why are Multiple Comparisons a Problem? Lecture 132 Simulating Multiple Tests Lecture 133 Multiple Comparison Wrap Up Section 17: A/B Testing Lecture 134 Using T-tests To Make Decisions Lecture 135 Marketing Data Description Lecture 136 Effect Size Lecture 137 Calculating Effect Size Lecture 138 Conclusions on the Marketing Data Lecture 139 Splitting Continuous Data Section 18: Power (Effect Size Impacts Choice of Sample Size) Lecture 140 What Sample Size to Choose Lecture 141 Power When n=1 Lecture 142 Power Increases With Sample Size Lecture 143 Power When N Varies Lecture 144 Further Considerations of Power Lecture 145 A Useful Tool for Calculating Power Section 19: Wilcoxon Rank Test Lecture 146 Wilcoxon Rank Tests Are Non-Parametric Lecture 147 Wilcoxon Rank Sum Test Calculations Lecture 148 Random Sample Example Lecture 149 The W Statistic Lecture 150 Signed Rank Test For One Sample Concept Lecture 151 Signed Rank Test For One Sample Calculation Lecture 152 Signed Rank Test For Paired Samples Lecture 153 When To Use the Wilcoxon Tests Section 20: Part 4 Overview Lecture 154 Part 4 Overview Section 21: ANOVAs Lecture 155 One-Way ANOVA Concepts Lecture 156 One-Way ANOVA Calculations Lecture 157 One-Way ANOVA Relation to T-test Lecture 158 F to t equivalence calculations Lecture 159 Assumptions of the ANOVA Lecture 160 Checking the Residuals Lecture 161 Post Hoc Testing Lecture 162 Using the Tukey Test Lecture 163 Two-Way ANOVA Conceptual Lecture 164 Math of a Two-Way ANOVA Lecture 165 Two-Way ANOVA Calculations part 1 Lecture 166 Two-Way ANOVA Calculations part 2 Lecture 167 Types of Sums of Squares Lecture 168 Tukey Test with Two-Way ANOVA Lecture 169 Drawing Conclusions from ANOVA Section 22: Correlation Lecture 170 Correlation For Continuous Relationships Lecture 171 Pearson Correlation Lecture 172 Correlation is not Causation Lecture 173 Null Hypothesis Testing for Correlation Lecture 174 Confidence Intervals for Correlation Lecture 175 Pearson Correlation is Linear Only Lecture 176 Handling non-Linearity Lecture 177 Spearman Correlation Concept Lecture 178 Spearman Correlation Calculations Lecture 179 Correlation Matrices Lecture 180 Correlation Conclusions Section 23: Single Predictor Linear Regression Lecture 181 Making Predictions With Regression Lecture 182 Single Predictor Regression Lecture 183 Interpolation versus Extrapolation Lecture 184 R Squared Concept Lecture 185 R Squared Calculations Lecture 186 Regression Significance Testing Concept Lecture 187 Regression Significance Testing Calculations Lecture 188 Examining Residuals Lecture 189 Regression With vs. Without an Intercept Lecture 190 Single Predictor Regression Wrap Up Section 24: T-test vs. Regression Comparison Project Lecture 191 Where Does Plastic Waste Come From? Lecture 192 Median-Split Test Lecture 193 Linear Regression Lecture 194 Which Test Was Better? Section 25: Chi-Squared Test Lecture 195 Goodness of Fit Test Lecture 196 Why is it Called Goodness of Fit? Lecture 197 Introducing Brain Data Example Lecture 198 Goodness of Fit Test Calculations Lecture 199 Two Variable Chi Squared Test For Independence Concept Lecture 200 Two Variable Chi Squared Test for Independence Calculations Lecture 201 Interpreting the Test for Independence Lecture 202 Chi Squared Conclusions Section 26: Congratulations! Lecture 203 Course is Over! Students & Early-Career Researchers,Psychology students learning statistics,Biology and neuroscience majors using R,Public health data analysis beginners,Social science undergraduates in research methods,Graduate students writing theses with data,Early-career researchers preparing publications,Students needing reproducible R workflows,Professionals Transitioning to Data Roles,Healthcare professionals learning R statistics,Education researchers analyzing student data,Nonprofit staff working with survey data,Policy analysts learning statistical tools,Professionals moving into data science careers,People with stats background new to R,Learners seeking modern tidyverse methods,Self-Taught & Lifelong Learners,Beginners wanting a guided R path,Self-taught coders needing structured learning,Lifelong learners exploring data science,Hobbyists wanting real-world data analysis,Learners preferring clear step-by-step examples,People seeking intuition, not black-box methods,Independent learners 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