DSST Principles of Statistics study plan
DSST Principles of Statistics · Give each week a clear focus.
8 weeks · About 7 hours per week
Principles and concepts underlying higher-order statistics & Continuous and discrete distributions
Distinguish population parameters from sample statistics and classify variables by measurement scale (nominal, ordinal, interval, ratio)
Compute and interpret mean, median, mode, variance, standard deviation, and standard error by hand for a small dataset
Explain the central limit theorem in your own words and state how sample size affects the sampling distribution of the mean
Build a one-page formula sheet covering descriptive statistics and notation (mu, sigma, x-bar, s, n, N)
Your checkpoint
A worked solutions packet of 15 descriptive-statistics problems with hand calculations, plus the completed formula sheet
Continuous and discrete distributions
Derive binomial probabilities using the counting formula and identify when the binomial model applies
Compute Poisson probabilities for rare-event counts and state the mean-variance relationship
Calculate normal-curve areas and inverse-normal cutoffs using z-scores without software
Contrast discrete and continuous distributions and select the appropriate model for a described scenario
Your checkpoint
A distribution decision table mapping 12 scenario descriptions to the correct model with justification and one computed probability each
Continuous and discrete distributions & Principles and concepts underlying higher-order statistics
Apply the normal approximation to the binomial and state the continuity correction rule
Compute expected value and variance for discrete random variables from a probability table
Explain the law of large numbers and the difference between a statistic's expected value and a single observed value
Interpret standard scores and percentiles within a normal distribution
Your checkpoint
A timed 25-question self-quiz on distributions and probability scored with a written error log identifying each missed concept
Principles and concepts underlying higher-order statistics & Confidence intervals
State the logic of hypothesis testing: null and alternative hypotheses, Type I and Type II error, power, and p-value interpretation
Construct a confidence interval for a mean with known and unknown sigma, choosing between z and t
Explain how confidence level, sample size, and variability each change interval width
Distinguish statistical significance from practical significance in written scenarios
Your checkpoint
A half-length practice test (about 50 questions) covering Domains 1, 2, and 4, with a scored breakdown by domain
Confidence intervals
Compute confidence intervals for proportions and interpret them in context
Determine required sample size for a target margin of error at a given confidence level
Interpret a confidence interval correctly and refute common misinterpretations
Compare paired and independent designs and match each to its interval procedure
Your checkpoint
A one-page interpretation guide with 10 correct-versus-incorrect interval statements and the reasoning for each verdict
Use of predictive statistics using the linear model
Interpret slope, intercept, and coefficient of determination in a simple linear regression
Compute predicted values and residuals and explain the least-squares criterion
State the assumptions of linear regression and describe how residual plots reveal violations
Distinguish correlation from causation and interpret the correlation coefficient's sign and magnitude
Your checkpoint
A complete regression analysis write-up for a small dataset you generate or collect, including scatterplot description, fitted equation, r-squared, and assumption check
Use of predictive statistics using the linear model & Confidence intervals
Test the significance of a regression slope and connect the slope test to the correlation test
Construct and interpret a confidence interval for a predicted mean response
Explain multiple regression coefficient interpretation and the idea of adjusted r-squared
Integrate interval reasoning with regression output to answer applied questions
Your checkpoint
A worked set of 10 applied regression-and-interval problems with full solutions and a list of the three concepts you found hardest
Principles and concepts underlying higher-order statistics & Continuous and discrete distributions & Use of predictive statistics using the linear model & Confidence intervals
Complete two full-length timed practice exams under test-day conditions
Review every missed item and classify the error as conceptual, computational, or misreading
Re-drill the 20 weakest concepts identified across all four domains
Finalize a condensed review sheet limited to formulas and decision rules
Your checkpoint
A final one-page formula and decision-rule sheet plus a scored log showing improvement between the two practice exams
Milestones
Week 2: Solve binomial, Poisson, and normal problems cold, without notes, at 80 percent accuracy or better
Week 4: Score at least 70 percent on the half-length practice test, with no domain below 60 percent
Week 6: Produce a correct regression write-up with slope, intercept, r-squared, and residual interpretation all accurate
Week 8: Score at least 80 percent on the second full-length practice exam and show a higher score than the first