Question
Can you make a guess on this? I cant tell what relationship my scatter plot has. At the first the dots are a bit low, then they go lower, but then it spikes for one dot and then it lowers, lowers again, rises, then rises again. I can tell if this is positive or negatively correlated, or none. I added a trendline and from what i see it rises slightly, but not too much. Can you help by making a guess to what relationship it has?
Answers
GPT-5 mini
From your description — points that mostly wander up and down but an overall trendline that “rises slightly” — my best guess is: a weak positive linear relationship (small positive correlation), probably with quite a bit of noise and at least one outlier (the single spike you mentioned).
Why I guess that
- A trendline that slopes upward means the average relationship is positive.
- You describe lots of local ups and downs and one big spike: that sounds noisy data with at least one outlier, which reduces the strength of the visible correlation.
- If the line “rises slightly,” the slope and therefore the correlation is likely small.
How you can check quickly
- Compute Pearson’s correlation coefficient r (measures linear association). Interpretation guideline:
- |r| < 0.1 ≈ negligible
- 0.1 ≤ |r| < 0.3 ≈ small
- 0.3 ≤ |r| < 0.5 ≈ moderate
- |r| ≥ 0.5 ≈ strong
- Also check R² (coefficient of determination) = r²: percentage of variance explained by the linear trend.
- If you think the relationship might be monotonic but not linear, compute Spearman’s rank correlation instead.
How to compute
- Excel: =CORREL(x_range, y_range)
- Python:
- Pearson: from scipy.stats import pearsonr; r, p = pearsonr(x, y)
- Spearman: from scipy.stats import spearmanr; rho, p = spearmanr(x, y)
- R: cor(x, y) or cor.test(x, y)
If you want, paste the x,y values or upload the plot/image and I’ll calculate r and R² and tell you whether the positive trend is statistically meaningful or just noise.
Why I guess that
- A trendline that slopes upward means the average relationship is positive.
- You describe lots of local ups and downs and one big spike: that sounds noisy data with at least one outlier, which reduces the strength of the visible correlation.
- If the line “rises slightly,” the slope and therefore the correlation is likely small.
How you can check quickly
- Compute Pearson’s correlation coefficient r (measures linear association). Interpretation guideline:
- |r| < 0.1 ≈ negligible
- 0.1 ≤ |r| < 0.3 ≈ small
- 0.3 ≤ |r| < 0.5 ≈ moderate
- |r| ≥ 0.5 ≈ strong
- Also check R² (coefficient of determination) = r²: percentage of variance explained by the linear trend.
- If you think the relationship might be monotonic but not linear, compute Spearman’s rank correlation instead.
How to compute
- Excel: =CORREL(x_range, y_range)
- Python:
- Pearson: from scipy.stats import pearsonr; r, p = pearsonr(x, y)
- Spearman: from scipy.stats import spearmanr; rho, p = spearmanr(x, y)
- R: cor(x, y) or cor.test(x, y)
If you want, paste the x,y values or upload the plot/image and I’ll calculate r and R² and tell you whether the positive trend is statistically meaningful or just noise.