Question
Im having trouble with excel. 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?
Answers
GPT-5 mini
Yes — I can help. From your description it sounds like your points are noisy and there may be an outlier or a non‑linear wiggle. A picture can be misleading, so the best way is to quantify the relationship with a correlation and/or a fitted model. Here’s a short, practical checklist and the exact Excel formulas and steps to use.
1) Quick numeric check of linear correlation
- Use CORREL or PEARSON to get r (ranges in the same order):
=CORREL(Xrange, Yrange)
or
=PEARSON(Xrange, Yrange)
- Interpretation (rules of thumb):
- r near 0 → no linear relationship
- r > 0 → positive linear tendency, r < 0 → negative
- |r| ≈ 0.0–0.2 none/very weak, 0.2–0.4 weak, 0.4–0.7 moderate, >0.7 strong
- To get R² for the linear fit:
=RSQ(Yrange, Xrange) (this is r²; proportion of variance explained)
2) Get the fitted line equation and slope
- Slope: =SLOPE(Yrange, Xrange)
- Intercept: =INTERCEPT(Yrange, Xrange)
If the slope is positive the best linear fit rises; if it’s very small and RSQ is small, the rise is negligible.
3) Test significance (optional)
- n = COUNT(Xrange)
- t-statistic: =r*SQRT((n-2)/(1-r^2)) where r is CORREL result
- two‑tailed p-value (Excel 2010+): =2*(1 - T.DIST(ABS(t), n-2, TRUE))
If p < 0.05 the correlation is statistically significant.
4) Add or inspect trendline on the chart (visual)
- Right-click a point → Add Trendline → choose Linear (or Polynomial degree 2 or 3 if it looks like it goes up/down).
- Check “Display Equation on chart” and “Display R‑squared value on chart” to see slope and R².
5) Consider non‑linear behavior and outliers
- If your points go down then up (or spike), a linear trend may not capture the pattern. Try a Polynomial trendline (degree 2 or 3) and compare R².
- Outliers can strongly affect slope and R². Identify any single spike; compute correlation excluding that row and see how the numbers change.
6) Practical interpretation for your description
- If the trendline “rises slightly” but R² is small (for example R² < 0.2), there’s only a very weak positive linear relationship — essentially no reliable linear trend for prediction.
- If R² is moderate/large and slope positive, there is a meaningful positive relation.
- If the scatter clearly changes direction (down → up → down) consider polynomial or transforming variables (log) or there may be no simple relationship.
If you want, paste your X and Y values here (or a small screenshot/table) and I’ll compute r, slope, R², p‑value, and suggest whether a linear model is appropriate or whether a polynomial/outlier removal is better.
1) Quick numeric check of linear correlation
- Use CORREL or PEARSON to get r (ranges in the same order):
=CORREL(Xrange, Yrange)
or
=PEARSON(Xrange, Yrange)
- Interpretation (rules of thumb):
- r near 0 → no linear relationship
- r > 0 → positive linear tendency, r < 0 → negative
- |r| ≈ 0.0–0.2 none/very weak, 0.2–0.4 weak, 0.4–0.7 moderate, >0.7 strong
- To get R² for the linear fit:
=RSQ(Yrange, Xrange) (this is r²; proportion of variance explained)
2) Get the fitted line equation and slope
- Slope: =SLOPE(Yrange, Xrange)
- Intercept: =INTERCEPT(Yrange, Xrange)
If the slope is positive the best linear fit rises; if it’s very small and RSQ is small, the rise is negligible.
3) Test significance (optional)
- n = COUNT(Xrange)
- t-statistic: =r*SQRT((n-2)/(1-r^2)) where r is CORREL result
- two‑tailed p-value (Excel 2010+): =2*(1 - T.DIST(ABS(t), n-2, TRUE))
If p < 0.05 the correlation is statistically significant.
4) Add or inspect trendline on the chart (visual)
- Right-click a point → Add Trendline → choose Linear (or Polynomial degree 2 or 3 if it looks like it goes up/down).
- Check “Display Equation on chart” and “Display R‑squared value on chart” to see slope and R².
5) Consider non‑linear behavior and outliers
- If your points go down then up (or spike), a linear trend may not capture the pattern. Try a Polynomial trendline (degree 2 or 3) and compare R².
- Outliers can strongly affect slope and R². Identify any single spike; compute correlation excluding that row and see how the numbers change.
6) Practical interpretation for your description
- If the trendline “rises slightly” but R² is small (for example R² < 0.2), there’s only a very weak positive linear relationship — essentially no reliable linear trend for prediction.
- If R² is moderate/large and slope positive, there is a meaningful positive relation.
- If the scatter clearly changes direction (down → up → down) consider polynomial or transforming variables (log) or there may be no simple relationship.
If you want, paste your X and Y values here (or a small screenshot/table) and I’ll compute r, slope, R², p‑value, and suggest whether a linear model is appropriate or whether a polynomial/outlier removal is better.