On the F-statistic, referring to Wikipedia here (see below).
#> F-statistic: 3.142 on 1 and 6 DF, p-value: 0.1267Ĭreated on by the reprex package (v0.2.1) #> Residual standard error: 481.5 on 6 degrees of freedom #> Residual standard error: 593.4 on 6 degrees of freedom #> lm(formula = sale ~ work_days, data = data) Ggplot(data, aes(work_days, sale)) + geom_point() + geom_smooth(method = "lm") Neither data set shows a convincing trend, especially the first one. Plotting the data can help you understand what you are dealing with. Multiple R-squared: 0.3437,Ědjusted R-squared: 0.2343į-statistic: 3.142 on 1 and 6 DF, p-value: 0.1267Įvery workday increases the sales by 387 ? Residual standard error: 481.5 on 6 degrees of freedom Multiple R-squared: 0.00333,Ědjusted R-squared: -0.1628į-statistic: 0.02005 on 1 and 6 DF, p-value: 0.892Ĭould you please help me understand if the coefficientsĮvery work day decreases the sale by 38.05 ? Residual standard error: 593.4 on 6 degrees of freedom
Lm(formula = sale ~ work_days, data = data) We are trying to understand the impact of number of workdays on sales.
0 kommentar(er)
