Mastering Statistics: Fundamentals to Data Analysis

Name: Mastering Statistics: Fundamentals to Data Analysis
Rating: 3.6 (2 reviews)

Unlock the Power of Statistical Analysis to Uncover Relationships and Make Informed Decisions

Created byEnterprise DNA, Sam McKay

Last updated 1/2025

English

What you'll learn

Gain expertise in advanced statistical techniques to uncover meaningful relationships within data.
Develop the skills to make informed decisions based on robust statistical inference.
Master the application of statistical tools for analyzing categorical and quantitative variables.
Avoid common pitfalls and confidently draw accurate conclusions from complex data analysis.

Course content

6 sections • 53 lectures • 5h 20m total length

Learning Tips From Enterprise DNA1:33
Course Overview - Let's get Started!0:14
Facts and Fallacies: Outliers in Data0:31
Course Overview and Welcome9:15
Reproducing Work in R: Setting up Your Environment1:52
Understanding the Challenge of Inference14:32
Addressing Bias and Variability in Data Analysis5:37
Introduction to Confidence Intervals: Concepts and Importance12:30
Learn how to construct and interpret a 95% t confidence interval for a sample mean, including margin of error, tradeoffs between confidence level and precision, with hands-on R demonstrations.
Calculating Confidence Intervals: Step-by-Step Guide10:01
Interpreting Confidence Intervals: Practical Examples and Applications4:17
Explore how random sampling creates variability in confidence intervals by drawing 100 observations from a 10,000-observation optical dataset, generating 95% intervals that vary around the population mean.
Foundations of Statistical Analysis and Confidence Intervals - Review1:04
Your Feedback Matters!0:40
Data Analysis and Confidence Intervals Insights0:54

Quick Facts: Behind Data Statistics0:27
Introduction to Significance Testing: Fundamentals and Hypothesis Testing10:44
Apply t significance testing to assess if a target value, like zero, is plausible based on sample data; interpret p values and relate findings to confidence intervals using real data.
Common Errors in Significance Testing: Type I and Type II Errors6:21
Explore p values, null hypothesis, and the risk of type I and type II errors in significance testing through one-sample t-tests and optical sample data.
Practice with Significance Testing: Case Studies and Exercises8:34
Understanding Statistical Usage: Avoiding Misuse and Abuse9:53
Confidence Intervals for Proportion: Estimating and Interpreting Proportions9:44
Estimate and interpret population proportions from binary data using one-sample proportions test. Learn about confidence intervals, continuity correction, and practical methods like Wilson score interval and the rule of three.
Significance Testing for Proportions: Hypothesis Testing with Categorical Data4:44
Proportions Practice: Applying Proportional Analysis Techniques7:26
Significance Testing and Proportional Analysis Techniques - Review1:04
Navigating the Nuances of Significance Testing and Proportional Analysis0:46

By the Numbers: Little-Known Facts in Statistics0:23
Understanding Goodness of Fit: Assessing Model Fit and Distributional Assumption14:20
Apply chi-squared goodness-of-fit tests to assess whether categorical age distributions fit a hypothesized US distribution, interpreting p-values and omnibus results. Explore uniformity tests across categories and introductory Benford's law problems.
Goodness of Fit Practice: Analyzing and Interpreting Model Fit7:07
Analyze a towns dataset to test Benford's law using a chi-squared goodness-of-fit test, comparing observed first-digit frequencies to the Benford distribution and interpreting the p-value.
Sample Size and Power: Determining Sample Size Requirements and Power Analysis8:36
Explore statistical power and its effect on detecting true differences via confidence intervals and sample size choices. Learn when to perform power analysis and margins of error for proportions.
Fundamentals of Statistics: Key Concepts9:37
Introduction to Two-Sample Testing4:44
Explore two-sample testing with independent, not paired samples by comparing two product reviews, compute mean ratings, and assess whether observed differences reflect population effects or random sampling variability.
Confidence Intervals for Two-Sample Comparison12:50
Learn to compute a Welch two-sample confidence interval for the difference in means using an A/B testing data set of product ratings, with 15 and 12 samples.
Significance Testing for Two-Sample Comparison12:47
Goodness of Fit, Sample Size, and Two-Sample Analysis - Review1:16
The Art of Model Fitting, Sample Dynamics, and Two-Sample Analysis0:50

Fact Trek: Navigating Data Statistics0:25
Practice: Performing the Welch Test7:53
Apply Welch two-sample tests to construct a 95% confidence interval and test differences in mean monthly income and rate between departments using t.test in the attrition dataset.
Analyzing Two-Sample Binary Data14:46
Compare attrition rates across departments and travel groups using two-sample binary data. Estimate the difference in proportions with confidence intervals and significance tests in R.
Pitfalls to Avoid: The Dangers of Data Dredging6:15
Analyzing Two-Sample Categorical Data6:56
Practice: Analyzing Categorical Data4:57
Two-Sample Analysis: Binary and Categorical Data - Review1:13
Decoding Binary and Categorical Data: Two-Sample Analyses Unraveled0:53

Data Dispatch: Good to Know Quick Facts0:20
Introduction to Sample Correlation11:47
Hypothesis Testing and Significance of Correlation11:41
Learn to assess correlation with scatter plots and a line of best fit, perform a correlation test, and interpret p-values, confidence intervals, effect size, and significance while considering sample size.
Linear Regression: Modeling Relationships14:36
Fit and interpret a regression line with a linear model, understanding the slope and intercept. Learn to predict within the data range (interpolation) and avoid extrapolation.
Practice: Correlation and Regression Analysis5:36
Explore the strong linear relationship between city and highway mileage in the mpg 2008 data, use a regression line and correlation test, and predict highway mileage for city 24.
Introduction to ANOVA (Analysis of Variance)11:13
Learn how ANOVA tests whether a categorical work life balance relates to a quantitative monthly rate, using box plots, p-values, and assumptions checks.
Advanced ANOVA Techniques10:01
Explore advanced ANOVA techniques using the MPG 2008 data to compare highway mileage across drive types, interpret p-values, visualize data, and apply Tukey HSD post-hoc tests.
Independence Testing of Categorical Variables7:20
Practice: Analyzing Categorical Variables6:31
Perform a chi-squared test on a contingency table to assess independence. For intervention patients, run anova with Tukey post-hoc to compare opioid and alcohol groups.
Statistical Relationships and Analysis - Review0:59
Navigating Statistical Relationships: Correlation, Regression, and Beyond0:49

Requirements

Basic understanding of variable types, descriptive statistics, and inferential statistics (recommended)
Familiarity with statistical software such as R or Python (beneficial but not required)
Open to beginners and individuals with prior data analysis experience
No specific prerequisites or prior knowledge necessary

Description

Are you ready to elevate your data analysis skills and unveil the untold stories within your datasets? Dive into the captivating universe of "Mastering Statistics: Fundamentals to Data Analysis." This course isn't just about crunching numbers; it's your portal to deciphering the language of statistical inference and relationships.

From deciphering samples and appraising relationships to grasping confidence intervals and significance testing, you'll build a robust toolkit for dynamic data analysis. Explore strategies for handling binary and categorical data, dive into correlation and regression analysis, and command ANOVA for advanced inference.

By steering clear of pitfalls and comprehending the risks of data manipulation, you'll emerge armed with the precision to draw sound conclusions and fuel data-driven choices. Whether your playground is business, research, or any data-centric realm, this course empowers you to extract the insights that transform success.

By the course's end, you'll wield advanced statistical techniques that metamorphose your approach to data analysis. Uncover concealed relationships, drive data-fueled decisions, and unlock pathways to unparalleled growth.

Ready to embark on the journey to mastery? Enroll now and harness the formidable prowess of statistical inference and relationships, guiding your stride towards informed decision-making. Your voyage to mastery commences here; seize the moment and transform your career!

Who this course is for:

Data analysts seeking to enhance their statistical analysis skills
Researchers looking to deepen their understanding of statistical inference and relationships
Professionals working with data who want to make data-driven decisions based on solid evidence
Individuals interested in unlocking insights and drawing conclusions from complex datasets
Anyone eager to apply advanced statistical techniques for practical data analysis
Beginners and experienced learners alike, as the course accommodates a wide range of skill levels

Mastering Statistics: Fundamentals to Data Analysis

What you'll learn

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Course content

Foundations of Statistical Analysis and Confidence Intervals13 lectures • 1hr 3min

Significance Testing and Proportional Analysis Techniques10 lectures • 1hr

Goodness of Fit, Sample Size, and Two-Sample Analysis10 lectures • 1hr 13min

Two-Sample Analysis: Binary and Categorical Data8 lectures • 43min

Statistical Relationships and Analysis11 lectures • 1hr 21min

Additional Resources - Complimentary guides for your data career1 lecture • 1min

Requirements

Description

Who this course is for: