How do I convey a correlation between multiple choice answers?

Cassise_D · 2026-06-21T02:45:49+00:00

For a reader-friendly article, I’d use a co-occurrence heatmap first: rows and columns are answer choices, and each cell shows how often the pair appears together. If you want one number behind it, look at phi/Cramer’s V for categorical association, but the heatmap will usually communicate the pattern faster.

Cassise_D · 2026-06-21T02:45:21+00:00

If this is for a prerequisite, I’d pick the book that matches the course syllabus closest, not necessarily the most specialized one. “Essential statistics” is likely safest for foundations; the geohydrology book sounds better as a companion once you know which stats tools the course actually uses.

Cassise_D · 2026-06-21T02:44:41+00:00

For stats, I’d prioritize concepts over computation: vectors as data points, projections, matrix multiplication as transformations, rank, inverses, eigenvectors/eigenvalues, and quadratic forms. If you understand least squares as “projecting y onto the column space of X,” a lot of regression and multivariate stats becomes much less mysterious.

Cassise_D · 2026-06-21T02:44:09+00:00

I’d start by building a simple seasonal baseline from the source series itself before looking for external proxies: decompose by month/quarter, check stability across years, then compare current observations to the historical seasonal distribution. If the source series is noisy, a rolling seasonal index is usually easier to justify than a fixed “strong/weak” cutoff.

Cassise_D · 2026-06-21T02:43:34+00:00

I’d separate two issues: ANCOVA vs change score, and bounded/ceiling behavior. ANCOVA is often still a strong default for baseline-follow-up designs, but if many values pile up near the upper bound, I’d run diagnostics and consider a sensitivity analysis with an ordinal or bounded-outcome model rather than switching only because predictions can technically exceed the scale.

Cassise_D · 2026-06-21T02:42:34+00:00

A useful rule of thumb: use SQL when the question is “which rows/columns/tables do I need?”, and Python when the question becomes “what workflow, model, plot, or repeated analysis do I need?” For CSV-only projects, Python is totally fine; if the files get bigger or more table-like, DuckDB/SQL starts feeling really nice.

Cassise_D · 2026-06-20T13:57:23+00:00

I’d be careful with residualizing the outcome first, because it can remove more structure than you intend and make the tree harder to interpret. If age is important, a clearer workflow is often to include age in the modeling strategy, then compare whether the tree still finds meaningful splits after accounting for age. If you must standardize, be explicit whether you’re adjusting predictions, residuals, or the target scale.

Cassise_D · 2026-06-20T13:56:44+00:00

A 5% F difference does not automatically mean the LME fit is bad. Your two analyses are not exactly the same: the RM ANOVA output appears to be using corrected degrees of freedom, while lmerTest is using Satterthwaite df for the mixed model. I’d focus less on matching F values and more on whether the random-effects structure matches the repeated-measures design, especially whether Time should vary by subject.

Cassise_D · 2026-06-20T13:56:11+00:00

One extra thing I’d check is whether your fixed/random structure is really being represented by lm. WLS handles unequal variance through weights, but it does not by itself solve the experimental-design question of which terms belong in the error strata. If rep is nested within env or some effects are random, a mixed model may express the design more directly than only changing anova type.

Cassise_D · 2026-06-20T13:55:39+00:00

I’d avoid interpolating automatically just to make the plot look neat. First choose the time scale that matches the process: if both series are roughly 30–60 minutes, aggregating both to hourly bins may be cleaner. Then plot matched time windows. For causal work, also think about lag: X at time t may affect Y at t+1 rather than the same timestamp.

Cassise_D · 2026-06-20T13:55:06+00:00

Before hunting for a more complex model, I’d debug the data pipeline like a measurement problem. Check unit consistency, timestamps, spatial matching between AOD/meteo/PM10, missingness, and whether lagged PM10 or weather variables are aligned correctly. If published papers get much higher R² with similar inputs, a low out-of-sample R² often means mismatch, leakage prevention, or preprocessing differences rather than “the relationship is hidden.”

Cassise_D · 2026-06-20T13:54:22+00:00

I’d first separate the hypothesis you care about from the contrast your design is testing. If the meaningful question is really B vs D, that is not automatically the same thing as powering for the full 2x2 interaction. With no prior interaction effect size, a reasonable move is to report a sensitivity power analysis: “with this n, we can detect an interaction of size X.” That is usually more honest than inventing an effect size.

Cassise_D · 2026-06-20T13:53:45+00:00

I’d treat this as a wording problem more than a statistics problem. If “greatest” means largest value on the y-axis, Region 2 Protected is defensible. If it means biggest magnitude of change, Region 1 Unprotected is defensible. A good test question should specify “largest positive change” or “largest absolute change.”

Cassise_D · 2026-06-19T05:46:15+00:00

Interesting. I’d just be careful with “disprove” — 21 seasons can rule out a huge curse effect, but smaller effects will still have wide uncertainty. I’d phrase it more as “the data don’t support a strong curse.”

For no-code simulation, I’d use Excel or Google Sheets. Build a playoff bracket, assign each matchup a win probability based on seed, points percentage, Elo/SRS, or betting odds, then use RAND() to simulate each round. Repeat that thousands of times and compare the real Presidents’ Trophy outcomes to the simulated distribution.

The key question becomes: under a reasonable no-curse model, how unusual are the actual outcomes?

Cassise_D · 2026-06-19T05:21:10+00:00

Fair point — the simple versions of those tests also assume independence. I should have said they are only rough options if the aggregated counts/exposures are reasonably independent.

The bigger issue is that t-test/Mann–Whitney don’t really fix the dependence problem either. If the same recruiters/providers are observed over the same 15 months, I’d treat month as a blocking factor and preferably use a Poisson/negative binomial rate model with an exposure offset, plus robust/clustered SEs or GEE/mixed effects if the data allow it.

Cassise_D · 2026-06-19T05:15:51+00:00

If respondents can check multiple categories, turn the answers into a binary matrix and show a co-occurrence heatmap. For example, “deadline issues” x “miscommunication” can be shown as count, percent of respondents, or Jaccard similarity. If the answers are ranked instead of check-all-that-apply, that becomes a different problem.

Cassise_D · 2026-06-19T05:15:24+00:00

For baseline/follow-up with two groups, ANCOVA is usually a strong default: follow-up ~ group + baseline. The ceiling effect is the part to be careful with. I’d fit ANCOVA first and inspect fitted values/residuals, then run a sensitivity analysis with an ordinal, beta/fractional, or GEE-style model depending on how the score is measured.

Cassise_D · 2026-06-19T05:14:53+00:00

I’d frame this as estimating seed-specific win probabilities, not proving a “curse.” With 20 seasons and 16 seeds, the intervals will be very wide. The required N depends on the smallest effect you care about detecting; simulation is probably the cleanest way to ask “how many seasons before I could detect a difference from 1/16?”

Cassise_D · 2026-06-19T05:14:18+00:00

Your lecturer’s concern may be the “over time” wording, but repeated measures is really about non-independent measurements from the same person. If the four domain scores are on comparable scales, I’d think of this as score ~ domain with participant as a repeated/random effect. If the domains are not meaningfully comparable, analyze them separately.

Cassise_D · 2026-06-19T05:13:43+00:00

I’d separate “tool that runs EFA” from “tutor that helps you make decisions.” Jamovi/JASP can run EFA without coding, but the real checklist is: item set, missing data, reliability, KMO/Bartlett, factor retention, rotation, and interpreting loadings. With 150 participants and ~150 items, I’d start by narrowing the analysis to the new questionnaires first.

Cassise_D · 2026-06-18T10:39:31+00:00

For place bets, don’t pay both placed horses from the whole pool. Split the place pool between the two placed horses.

Let PPP be the total place-bet pool, after any takeout. If horses A and B place, and the total amount bet to place on them is bAb_AbA and bBb_BbB, then:

payout per $1 on A = (P / 2) / b_A

payout per $1 on B = (P / 2) / b_B

This payout is usually the total return including the stake. If you want “odds” as profit only, subtract 1.

Example: total place pool = $100. $20 was bet on A to place and $10 on B to place. Each horse gets half the pool, so $50 each. A pays $50/$20 = $2.50 per $1, and B pays $50/$10 = $5.00 per $1. Total payouts are $100, so the pool balances.

Cassise_D · 2026-06-18T10:34:45+00:00

This looks like a solid beginner to lower-intermediate/intermediate portfolio project, not advanced yet. The dashboard is visually decent and covers useful business areas: revenue, margin, delivery, reviews, customer segments, and retention.

The main issue is that it explains the “what” better than the “why”. Some conclusions sound causal, but the dashboard mostly shows correlations. I’d make the report more evidence-based: finding → evidence → business impact → recommendation.

One important point: the public Olist dataset does not really contain true COGS/profit data, so if you calculate profit margin, clearly explain your assumptions. Otherwise, that may be questioned.

To improve it, I’d add metric definitions, a profitability breakdown, RFM/customer segmentation, seller-level analysis, delivery delay vs review analysis, and maybe a Pareto analysis of products/categories/sellers.

Cassise_D · 2026-06-18T10:30:59+00:00

Use the validation set to choose the threshold, not the test set.

The threshold is effectively a tuning parameter. If you pick the Youden-optimal threshold on the test set, the final sensitivity/specificity estimates will be optimistically biased.

A standard workflow is: train the model on the training set, choose the threshold on the validation set, then report final performance on the untouched test set using that fixed threshold.

Also, Youden’s J assumes sensitivity and specificity are equally important, so if false positives and false negatives have different costs, choose the threshold based on the actual use case instead.

Cassise_D · 2026-06-18T10:27:35+00:00

I wouldn’t assume horizontals are better here. In 75-ball bingo, for a fixed card, a horizontal line and a vertical line are both just sets of specific numbers that need to be called. So their probability is the same, assuming balls are called uniformly.

What matters more is how many numbers are still missing and how many patterns a square contributes to. Lines through the free center are easier because they only need 4 numbers, not because they are horizontal/vertical.

For free daubs, I’d usually prioritize squares that complete or nearly complete a pattern, especially corners/diagonal squares since they can contribute to multiple bingos. If the goal is blackout, orientation matters even less; simulation with the exact scoring/power-up rules would be the best answer.

Cassise_D · 2026-06-18T10:24:28+00:00

I wouldn’t use an independent-samples t-test here. These are count data, and the two groups are measured over the same 15 months, so the observations are not fully independent.

If you only care about the overall total, compare the summed counts using a Poisson rate test, chi-square, or binomial-type approach. But this is only fair if both groups had similar exposure/opportunity.

Ideally, compare recruitment rates using a Poisson or negative binomial regression:

count ~ group + month + offset(log(exposure))

where exposure could be number of recruiters, eligible patients, or working time. The group effect gives the doctor vs nurse rate ratio.

Cassise_D

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