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DS 3100: FUNDAMENTALS OF DATA SCIENCE
Lab 8: Identifying Data for Classification
Selecting variables for logistic regression
DR. SIMON LILBURN · SPRING 2026 · WEEK 8
IMPORTANT — Academic Integrity
The learning objectives for this course require you to personally engage with this material through careful reading, critical thinking, and reflection. Using AI tools to summarize, analyze, or answer questions about this content violates the academic integrity expectations for assignments related to it and defeats the educational purpose.
Please read this document yourself—your understanding of its content is important for DS 3100.
5 min read, 110 min activity
Data for Logistic Regression Analysis Report
For this lab, you’ll pick your dataset for the third analysis report. You can stick with the dataset you used for Reports 1 or 2 or switch to something new.
As before, you are strongly encouraged to explore beyond the datasets listed below—find data that genuinely interest you.
The datasets below are starting points, not a definitive menu. If something outside this list catches your eye, go for it.
A few ground rules for choosing a dataset:
1. The data must not come from another course you are taking.
2. The data should not be a commonly used teaching dataset (e.g., any of the datasets found here).
3. If you want to use a dataset not listed below and not already approved, email [email protected] to ask for permission. If the profes sor has already okay’ed your dataset, you are good to go and do not need to email.
The following datasets have been vetted for their applicability to this course. You can use one single dataset for all of your analysis reports or use a different one for each report.
General
• NFL Statistics
• Complete Pokemon Dataset
• University Test Scores
• Los Angeles Airbnb Listings
• Dog Breeds
• YouTube Performance Analytics
• Laptop Prices
• Air Quality and Public Health
Sports
• FIFA 23 Complete Player Dataset — 19,000+ players, 110+ attributes
• NBA Players Stats — 3,000+ players from 1950 to present
• Baseball Databank (Lahman) — MLB statistics 1871–2015
• Formula 1 World Championship — races, drivers, constructors 1950–2024
• ATP Tennis — 60,000+ matches from 2000–2025, daily updates
• Esports Earnings — prize money by game/tournament 1998–2023
Finance and Economy
• FRED (Federal Reserve Economic Data) — GDP, unemployment, CPI, interest rates
• World Bank Open Data — 1,000+ global development indicators
• Bureau of Labor Statistics (BLS) — employment, wages, CPI, productivity data
• S&P 500 Stock Data — 5 years of historical stock prices
• Bitcoin Historical Data — minute-level prices from 2012 to present
Video Games
• Steam Store Games — 27,000 games with genres, owners, ratings
• Video Game Sales — 16,000+ games with regional sales data
• IGN Games — 18,625 games with scores and platforms (20 years)
Movies and Entertainment
• IMDB Movies (up to May 2024) — 120,000+ movies from 1911–2024
• Netflix Movies and TV Shows — 8,000+ titles with cast, ratings, duration
Music
• Spotify Tracks Dataset — 125 genres with audio features (danceability, energy, tempo)
• Billboard “The Hot 100” Songs — chart history from 1958 to present
Health and Medicine
• CDC Open Data Portal — mortality, disease surveillance, vaccination, NHANES surveys
• COVID-19 Dataset — global cases, deaths, recoveries from Johns Hopkins
Environment and Climate
• Climate Change: Earth Surface Temperature — 1.6 billion temperature records from 1750–present
• World Bank Climate Change Data — global climate indicators
Transportation
• Flight Delay and Cancellation Data 2024 — 1M+ US flights with weather delays
• 2015 Flight Delays and Cancellations — US DOT official data
Food and Nutrition
• Fastfood Nutrition — calories, fat, protein from fast food chains
• Nutritional Values for Common Foods — 8,800 food items
Real Estate
• Zillow House Price Data — housing and rental prices by city and bed rooms
Education
• Students Performance Dataset — 2,392 students with GPA, study habits, parental support
Crime and Safety
• U.S. Crime Dataset (2020–2024) — violent and property crimes with geographic data
E-commerce
• Amazon US Customer Reviews — 100M+ reviews since 1995 with ratings and text
• Consumer Reviews of Amazon Products — product reviews from Datafiniti
Space and Astronomy
• NASA’s Confirmed Exoplanets — 5,788 exoplanets, updated regularly
• Exoplanet Hunting in Deep Space — Kepler mission flux data for planet detection
Census and Demographics
• U.S. Census Bureau (data.census.gov) — decennial census, ACS, population estimates
• IPUMS USA — harmonized U.S. census microdata 1850–present, ACS
Social Science Surveys
• General Social Survey (GSS)
— attitudes, behaviors, demographics since 1972
• World Values Survey — beliefs, values across 90+ countries since 1981
• Pew Research Center — public opinion on politics, media, religion, technology
• ICPSR (Inter-university Consortium for Political and Social Research) — 500,000+ social science data files
NOTE
You may have to look at multiple datasets before you find one that has a natural binary outcome. Some datasets have obvious binary variables (e.g., survived/died, won/lost, yes/no). Others require you to create a binary outcome from a continuous variable (e.g., above/below the median).
The students who produce the best reports are the ones who explored several options first.
TASK — Choose Your Dataset
Figure out which dataset you will use for your third analysis report.
Identify an Outcome
In Lab 6, you identified a continuous outcome for linear regression. Now you need a binary outcome for logistic regression. The logic is different: instead of asking “is it continuous and normally distributed?” you are asking “does it have exactly two categories, and are those categories well-behaved enough to model?”
There are six things to check.
1. Outcome is binary (or can be made binary)
Logistic regression models the probability of belonging to one of two classes, so your outcome must have exactly two categories, typically coded as 0 and 1.
Things to check: How many unique values does the variable have? Is it already binary, or does it need recoding? Potential actions:
Recall from Unit 5.1 that logistic regression uses glm(…, family = binomial) . The outcome must be a 0/1 numeric vector or a two-level factor. If you pass a factor, R predicts the probability of the second level.
-
If a variable is already binary (e.g., yes/no, win/lose, survived/died), recode it to 0/1.
-
If you have a continuous variable with a meaningful threshold, you can create a binary outcome via a median split or a domain-informed cutpoint. For example, splitting test scores into pass/fail at the passing grade, or splitting income at the median.
-
If a categorical variable has more than two levels, you can either combine levels into two groups or consider multinomial logistic regression (which is beyond the scope of this course).
WARNING
Median splits discard information—a score of 89 and a score of 51 both become “below median” even though they are very different. Use a domain-informed cutpoint when one exists (e.g., a clinical threshold, a passing grade). Reserve median splits for when no natural boundary exists and you need a binary outcome for the assignment.
2. Outcome is correctly coded
Once you have a binary variable, make sure you know exactly how it is coded and which level R will treat as the “success” class (the event you are predicting).
This is a common source of confusion. A student fits a model predicting “survived” but R is actually modeling P(died = 1) —every coefficient interpretation is backwards.
Things to check:
• If the outcome is numeric (0/1), glm() models the probability that Y = 1.
Make sure the 1 corresponds to the event you care about.
• If the outcome is a factor,
glm()
models the probability of the second level
(alphabetically, unless you set the levels explicitly). Run
levels()
to verify.
Potential actions:
• Recode with
ifelse()
so that 1 is the event of interest.
• Or use
factor()
with an explicit
levels
argument so that the reference
category (0, “no”, “control”) comes first:
factor(x, levels = c(”no”, ”yes”)).
NOTE
Get the coding right before you start modeling. Fixing it after the fact means re-interpreting every coefficient, which is error-prone.
3. Sufficient observations in each class
Logistic regression needs enough observations in both categories to estimate reliable coefficients. A common rule of thumb is 10–20 observations per predictor in the
class. Severe class imbalance (e.g., 95%/5%
can cause the model to predict the majority class for every observation and still achieve high accuracy. This is why accu racy alone is a poor metric for imbalanced data—you’ll learn about better metrics (precision, recall, F1 later.
Things to check:
Use table() to count observations in each category. Compute the proportion in each class.
Potential actions:
• If the split is reasonably balanced (e.g., 60/40 or better), proceed without adjustment.
• If moderately imbalanced (e.g., 70/30 to 80/20), note it as a limitation and consider adjusting the decision threshold later (e.g., using 0.30 instead of 0.50).
• If severely imbalanced (e.g., 90/10 or worse), consider whether this out come is viable. You may need to choose a different variable or combine categories.
4. Enough events per variable
In linear regression, the rule of thumb for sample size is roughly 10–15 observations per predictor. In logistic regression the constraint is tighter:
what matters is the number of observations in the smaller class (the “events”), not the total sample size.
EPV = events per variable. With 200 observations, a 70/30 split, and 5 predictors, you have 60 events and EPV = 60/5 = 12.
That’s workable but tight. With 10 predictors, EPV = 6—too low for stable estimates.
The standard guideline is EPV ≥ 10: at least 10 observations in the minority class for every predictor you plan to include. Below this threshold, coefficient estimates become unstable and confidence intervals unreliable.
Things to check: Count the observations in your smaller class. Divide by the number of predictors you plan to use. Is EPV at least 10?
Potential actions:
• If EPV is low, reduce the number of predictors. Focus on the ones with the strongest theoretical or empirical justification.
• If your minority class is very small, consider whether the outcome is viable or whether a different binary split would give you more events.
5. No complete or quasi-complete separation
Separation occurs when a predictor perfectly (or nearly perfectly) divides the two outcome classes. When this happens, logistic regression cannot find a finite maximum-likelihood solution and the coefficient estimates blow up to infinity.
You’ll know you have separation if R gives a warning like fitted probabilities numerically 0 or 1 occurred or if a coefficient estimate is absurdly large (e.g., b = 23.7 with SE = 1,847).
Things to check: Create cross-tabulations (
table()
) between your outcome and each categorical predictor. Look for cells with zero counts—these indicate separation.
Potential actions:
• Remove the separating predictor from the model.
• Combine sparse categories to eliminate zero cells.
• Use penalized regression (ridge or lasso), which can handle separation by regularizing the coefficients.
NOTE
Separation is not always obvious during exploratory analysis. You may only discover it when you fit the model. That’s fine—note it, address it, and document the fix.
6. Outcome is related to other variables Just as with linear regression, your outcome needs to be related to at leastsome of your predictors. Classification will fail if your variables lack predic tive power.
From Unit 3.1 point-biserial correlation is the appropriate measure for a continuous predictor and a binary outcome. It is mathematically equivalent to Pearson’s r when one variable is coded 0/1. Use
cor.test(continuous, binary_01)
or let EGAnet^:auto.correlate()
handle it automatically.
The correlation toolkit from Unit 3.1 still applies, but the pairings change because your outcome is now binary:
• Continuous predictor × binary outcome: point-biserial correlation
(
cor.test(continuous_var, binary_outcome)
)
• Binary predictor × binary outcome: phi coefficient or tetrachoric correlation
(
psych^:tetrachoric()
)
• Mixed types automatically:
EGAnet^:auto.correlate()
detects variable types and selects the appropriate correlation for each pair
In addition to correlations, visualizations are especially useful for binary outcomes:
• Boxplots (continuous predictor × binary outcome): Do the distributions differ between the two groups?
• Stacked or grouped bar charts (categorical predictor × binary outcome):
Are the proportions different across categories?
Things to check: correlations between your outcome and variables of interest using the appropriate correlation type; visualizations that reveal group
differences
Potential actions: compute a correlation matrix (with appropriate correlations), create boxplots and bar charts, look for moderate or larger effects
(|r| > 0.30)
⋄ HINT
You already know a function that can handle all of these checks at once— the same one you used in Lab 6.
Finalize Your Outcome
The primary goal for this lab is for you to work through your exploratory data analysis for your third analysis report. This process is structured to guide you through what you should be checking for a logistic regression outcome.
By the end, you should have identified variables that can be used in logistic regression. Ideally, you’ll have identified and finalized exactly which variabl you’ll use as your outcome.
NOTE
Nothing you choose today is set in stone. The dataset, the outcome variable, and the predictors you explore in this lab are a starting point for your third analysis report—not a commitment. If you discover a better outcome, a more interesting dataset, or a different angle as you continue your analysis, you are free to change direction. The purpose of this lab is to practice the process of evaluating variables for classification, not to lock you into a final plan.
TASK 1 — Work Through the Rmarkdown
Work through the Rmarkdown associated with this lab.
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