I was trying to understand qqPlot and found that for finding Normal Distribution of data , qqPlot is a good tool.
Then I ran 2 set of examples and got the following output where in both the case, all the points lies close to the identity line but both the graph gave a different look-
model2 <- lm(Withdrawal ~ Deposit +Balance,data = cdata)
#to find the normal distribution of data
qqPlot(model2,id.n=3)
Here all the points are close to X-axis , so what should I derive here , is it normal distribution.
Lets consider the other example-
row.names education income women prestige census type
1 gov.administrators 13.11 12351 11.16 68.8 1113 prof
2 general.managers 12.26 25879 4.02 69.1 1130 prof
3 accountants 12.77 9271 15.70 63.4 1171 prof
4 purchasing.officers 11.42 8865 9.11 56.8 1175 prof
5 chemists 14.62 8403 11.68 73.5 2111 prof
6 physicists 15.64 11030 5.13 77.6 2113 prof
7 biologists 15.09 8258 25.65 72.6 2133 prof
8 architects 15.44 14163 2.69 78.1 2141 prof
9 civil.engineers 14.52 11377 1.03 73.1 2143 prof
10 mining.engineers 14.64 11023 0.94 68.8 2153 prof
11 surveyors 12.39 5902 1.91 62.0 2161 prof
12 draughtsmen 12.30 7059 7.83 60.0 2163 prof
13 computer.programers 13.83 8425 15.33 53.8 2183 prof
14 economists 14.44 8049 57.31 62.2 2311 prof
data(Prestige)
model2 <- lm(prestige ~ education*type +log2(income)*type, data = Prestige)
qqPlot(model2$res,id.n=3)
Here the line crosses almost 45 degree and the plotted points fall closely onto the identity line, so the data do not seem to come from the normal distribution So exactly what is the difference between these 2 plots.