Sales receipts versus shoplifting losses for a department store

Data for Exercise 9.58

Shoplift

Format

A data frame/tibble with eight observations on two variables

sales

sales (in 1000 dollars)

loss

loss (in 100 dollars)

References

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

Examples

plot(loss ~ sales, data = Shoplift)

model <- lm(loss ~ sales, data = Shoplift)
summary(model)
#> 
#> Call:
#> lm(formula = loss ~ sales, data = Shoplift)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -2.6829 -1.2232 -0.7567  1.3339  4.0696 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)  
#> (Intercept)   6.1050     4.0496   1.508   0.1824  
#> sales         1.0350     0.4513   2.293   0.0617 .
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 2.316 on 6 degrees of freedom
#> Multiple R-squared:  0.4671,	Adjusted R-squared:  0.3783 
#> F-statistic: 5.259 on 1 and 6 DF,  p-value: 0.06167
#> 
rm(model)