Covers the Data → Data Analysis → Regression workflow and how to read Regression Statistics, ANOVA, and Coefficients.
1) In simple linear regression, Excel’s “Multiple R” equals the absolute value of the correlation \( r \) between X and Y.
2) “Standard Error” in the Regression Statistics box equals \( \sqrt{\text{MSE}} \) from the ANOVA table (a typical vertical miss in Y-units).
3) “Significance F” in the ANOVA table tests \( H_0:\; \beta_1=0 \) against “the line explains Y.”
4) When your first row contains headers, you should not check “Labels” in the Regression dialog.
5) With one X, the model F-statistic equals the square of the slope’s t-statistic: \( F = t(\beta_1)^2 \).
6) “Input Y Range” should be set to the X column and “Input X Range” to the Y column.
7) In simple linear regression, the Residual (Error) degrees of freedom equal \( n-2 \).
8) Adjusted \( R^2 \) is always larger than \( R^2 \).
9) In Excel’s ANOVA, “Regression SS” is the same as SSE (error sum of squares).
10) Choosing “New Worksheet Ply” puts the Summary Output on a new sheet in the workbook.