Number of problems reported per 100 cars in 1994 versus 1995s

Source: R/BSDA-package.R

Data for Exercise 2.14, 2.17, 2.31, 2.33, and 2.40

Jdpower

Format

A data frame/tibble with 29 observations on three variables

car

a factor with levels Acura, BMW, Buick, Cadillac, Chevrolet, Dodge Eagle, Ford, Geo, Honda, Hyundai, Infiniti, Jaguar, Lexus, Lincoln, Mazda, Mercedes-Benz, Mercury, Mitsubishi, Nissan, Oldsmobile, Plymouth, Pontiac, Saab, Saturn, and Subaru, Toyota Volkswagen, Volvo

1994

number of problems per 100 cars in 1994

1995

number of problems per 100 cars in 1995

Source

USA Today, May 25, 1995.

References

Kitchens, L. J. (2003) Basic Statistics and Data Analysis. Pacific Grove, CA: Brooks/Cole, a division of Thomson Learning.

Examples


model <- lm(`1995` ~ `1994`, data = Jdpower)
summary(model)
#> 
#> Call:
#> lm(formula = `1995` ~ `1994`, data = Jdpower)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -42.142 -14.929  -0.855  17.178  37.022 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   2.2241    14.6449   0.152     0.88    
#> `1994`        0.9098     0.1213   7.501 4.55e-08 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 21.72 on 27 degrees of freedom
#> Multiple R-squared:  0.6757,	Adjusted R-squared:  0.6637 
#> F-statistic: 56.26 on 1 and 27 DF,  p-value: 4.546e-08
#> 
plot(`1995` ~ `1994`, data = Jdpower)
abline(model, col = "red")

rm(model)