Chapter 8: Trendlines and Regression Analysis

Exercise 7: linear regression (Pg. 295)

Set up an Excel worksheet to apply formulas (8.5) and (8.6) to compute the values of b0 and b1 for the data in the Excel file Home Market Value and verify that you obtain the same values as in Examples 8.4 and 8.5.

data <- read.csv("./data/home_market_value.csv")
data
lm1 <- lm(data$Market.Value ~ data$Square.Feet - data$House.Age)
summary(lm1)
## 
## Call:
## lm(formula = data$Market.Value ~ data$Square.Feet - data$House.Age)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##  -8067  -4327  -1923   3097  32634 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      32673.220   8831.951   3.699  0.00065 ***
## data$Square.Feet    35.036      5.167   6.780  3.8e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7288 on 40 degrees of freedom
## Multiple R-squared:  0.5347, Adjusted R-squared:  0.5231 
## F-statistic: 45.97 on 1 and 40 DF,  p-value: 3.798e-08

b0 = 32673.220, b1 = 35.036, which are the same results as example.