Learn applied statistics — distributions, hypothesis tests, regression: a study plan
Quizlar drafted this outline for "Learn applied statistics — distributions, hypothesis tests, regression" — 5 domains in the order to tackle them, weighted by importance. Approve it and a voice tutor turns each topic into daily 20-minute sessions, scheduled by FSRS spaced repetition.
Study plan: Learn applied statistics — distributions, hypothesis tests, regression
Suggested pace: 20 minutes a day
As you study, a readiness score climbs toward your goal — you'll know when you've got it, not just when you've finished.
This outline is a draft. Check the topics, weights and items, then approve. No cards are written until you do.
Probability Foundations 20%
Define probability, sample spaces, and events; compute basic probabilities
Work with discrete distributions (Binomial, Poisson) and continuous distributions (Normal, Exponential)
Calculate expectation, variance, and higher moments; apply linearity of expectation
Use the Central Limit Theorem to approximate sampling distributions for sums/means
Apply transformation techniques to derive distributions of functions of random variables
Descriptive Statistics & Data Exploration 15%
Summarize data with measures of central tendency and dispersion (mean, median, mode, variance, IQR)
Create and interpret visualizations: histograms, boxplots, scatterplots, QQ-plots
Assess data quality: detect missing values, outliers, and measurement errors
Compute and interpret correlation coefficients (Pearson, Spearman)
Perform basic data transformations (log, sqrt, standardization) to meet analysis assumptions
Inferential Statistics – Hypothesis Testing 25%
Formulate null and alternative hypotheses for one‑sample and two‑sample problems
Select appropriate test statistics (z, t, chi‑square, F) based on data type and sample size
Compute p‑values and construct confidence intervals; interpret both in context
Conduct power analysis and determine required sample sizes for desired effect sizes
Apply multiple‑comparison corrections (Bonferroni, Holm) when testing several hypotheses
Perform non‑parametric tests (Wilcoxon, Mann‑Whitney, Kruskal‑Wallis) when assumptions fail
Regression Analysis 25%
Fit simple linear regression; interpret slope, intercept, R², and residuals
Diagnose regression assumptions using residual plots, tests for heteroscedasticity, and normality
Extend to multiple linear regression; assess multicollinearity (VIF) and variable selection
Implement regularization techniques (Ridge, Lasso) for high‑dimensional data
Fit generalized linear models (logistic, Poisson) for binary and count outcomes
Evaluate model performance with cross‑validation, AIC/BIC, and out‑of‑sample prediction error
Applied Statistical Modeling Projects 15%
Define a real‑world research question and identify appropriate statistical methods
Collect, clean, and preprocess a dataset; document data‑wrangling steps
Perform exploratory data analysis and summarize key findings
Build and validate a predictive or inferential model using techniques from earlier domains
Communicate results through a written report and visual presentation, emphasizing interpretation and limitations
How to Study Applied Statistics, From Distributions to Regression
This plan is for anyone who needs statistics to work in practice: analysts moving beyond spreadsheets, students heading into a methods-heavy course, researchers who want to trust their own results, or developers drifting toward data science. If you have run a t-test or fit a trend line without being sure why it worked, this plan helps you build that understanding.
Quizlar's draft outline has five domains. It starts with Probability Foundations (20%), where you work with distributions, expectation and variance, and the Central Limit Theorem. Next comes Descriptive Statistics & Data Exploration (15%), which covers summaries, plots, data quality, correlation and transformations. The two heaviest domains, at 25% each, come after that. Inferential Statistics covers hypotheses, test selection, p-values, confidence intervals, power, multiple-comparison corrections and non-parametric tests. Regression Analysis runs from simple linear regression through diagnostics, multiple regression, Ridge and Lasso, GLMs and cross-validation. The order follows how the ideas depend on each other. You cannot interpret a p-value without sampling distributions, and you cannot diagnose a regression without knowing what residuals should look like. The plan ends with Applied Statistical Modeling Projects (15%), where you take a real question from data cleaning to a written report.
Quizlar turns each outline item into quiz cards and tutors you through them by voice in short daily sessions. When you get stuck on choosing between a t-test and a Mann-Whitney test, it asks guiding questions instead of giving you the answer. It follows up when your reasoning is shaky and explains concepts like heteroscedasticity in plain language. FSRSspaced repetition then schedules reviews so ideas you learned early, like linearity of expectation, are still there when regression needs them.Start this plan — free
FAQ
How long does it take to learn applied statistics — distributions, hypothesis tests, regression?
The outline has 27 learning objectives across five domains, and hypothesis testing and regression together make up half of the weight. With steady daily sessions of 20–40 minutes, many learners can work through the core material in roughly two to four months. Your pace will depend on your math background and how much time you give the final project domain.
Where should I start?
Start with Probability Foundations, which covers sample spaces, Binomial, Poisson, Normal and Exponential distributions, expectation and variance, and the Central Limit Theorem. Everything in hypothesis testing and regression builds on these ideas. If you are already comfortable with them, Quizlar's quizzes will show it quickly and you can move on to Descriptive Statistics & Data Exploration.
Can I edit this study plan?
Yes. The outline is Quizlar's draft, and you can review and change it before you approve it. You might adjust domain weights, remove items you have already mastered (such as transformation techniques), or add emphasis where your coursework or job needs it.
How is this different from flashcards or Anki?
Flashcards test whether you can recall an answer. Quizlar talks you through the reasoning, which matters in statistics, where the hard part is knowing which test fits or why a residual plot signals trouble. It gives Socratic hints, asks follow-up questions and explains concepts by voice. It also uses the FSRS spaced repetition algorithm to schedule reviews, so you get the retention benefits of Anki without flipping static cards.
Is Quizlar free?
Quizlar has a free tier, so you can draft this applied statistics plan and start voice tutoring sessions without paying. Paid plans are available if you want more usage. Check the pricing page for current limits.