Merge pull request #343 from Nixon-hk/Fix_coefficient

Fix coefficient

R/linear-regression.qmd

@@ -25,13 +25,20 @@ head(htwt)
 
 ### Regression Analysis
 
-Next, we fit a regression model, representing the relationships between gender, age, height and the interaction variable created in the datastep above. We again use a where statement to restrict the analysis to those who are less than or equal to 19 years old. We use the clb option to get a 95% confidence interval for each of the parameters in the model. The model that we are fitting is ***height = b0 + b1 x female + b2 x age + b3 x fem_age + e***
+Next, we fit a regression model, representing the relationships between gender, age, height and the interaction variable created in the datastep above. We again use a where statement to restrict the analysis to those who are less than or equal to 19 years old. We use the clb option to get a 95% confidence interval for each of the parameters in the model. The model that we are fitting is $height = b_0 + b_1\times female + b_2\times age + b_3\times fem\_age + e$
 
 ```{r}
 regression<-lm(HEIGHT~female+AGE+fem_age, data=htwt, AGE<=19)
 summary(regression)
+
+b0=round(regression$coefficients[1],4)
+b1=round(regression$coefficients[2],4)
+b2=round(regression$coefficients[3],4)
+b3=round(regression$coefficients[4],4)
+
 ```
 
-From the coefficients table b0,b1,b2,b3 are estimated as b0=28.88 b1=13.61 b2=2.03 b3=-0.92942
+From the coefficients table b0,b1,b2,b3 are estimated as b0=`r b0` b1=`r b1` b2=`r b2` b3=`r b3`
+
 
-The resulting regression model for height, age and gender based on the available data is ***height=28.8828 + 13.6123 x female + 2.0313 x age -0.9294 x fem_age***
+The resulting regression model for height, age and gender based on the available data is $height= `r b0` + `r b1`\times female + `r b2`\times age `r b3`\times fem\_age$