A model with zero intercept was fitted. The model is given by yi=1xi+i,i=1,2,,n, where the slope
1 is an unknown constant and 1 is normally distributed random errors with mean zero and
variance 2. The residual sum of squares for this model is SSmin=i=1n(yi^1xi)2. where ^1 is the
least squares estimator of 1. (i) Show that ^1=i=1nxi2i=1nxiyi Construct the analysis of variance
(ANOVA) table for this model.
A test is run on a given process to determine the relationship between boiling water temperature,
X (Celcius) and pressure, Y(kPa). The following are the summary statistics for 19 samples:
i=119xiyi=475.30;i=119xi2=530.78;i=119yi2=427.76 Find the fitted regression equation. Using
the ANOVA table in part (ii), test for significance of regression model at 0.05 level of
significance..
A model with zero intercept was fitted. The model is given by yi=1xi+.pdf
1. A model with zero intercept was fitted. The model is given by yi=1xi+i,i=1,2,,n, where the slope
1 is an unknown constant and 1 is normally distributed random errors with mean zero and
variance 2. The residual sum of squares for this model is SSmin=i=1n(yi^1xi)2. where ^1 is the
least squares estimator of 1. (i) Show that ^1=i=1nxi2i=1nxiyi Construct the analysis of variance
(ANOVA) table for this model.
A test is run on a given process to determine the relationship between boiling water temperature,
X (Celcius) and pressure, Y(kPa). The following are the summary statistics for 19 samples:
i=119xiyi=475.30;i=119xi2=530.78;i=119yi2=427.76 Find the fitted regression equation. Using
the ANOVA table in part (ii), test for significance of regression model at 0.05 level of
significance.
