-
Parameterize a Histogram analysis as follows:
- Histogram Style — Basic
-
Columns to
Analyze
- twm_customer.age
- twm_customer.income
- Overlay Columns — gender
- Statistics Columns — nbr_children
-
Run the analysis in the same manner as described above.
This time, the following Results should be generated. Again, the SQL is not shown.
Histogram Analysis Example #2 Table xtbl xcol xbin xbeg xend xcnt xpct ovly_gen xocnt xopbct xopct twm_c... age 1 13 20.6 140 18.7416332 F 78 55.7142857 10.4417671 twm_c... age 1 13 20.6 140 18.7416332 M 62 44.2857143 8.2998661 twm_c... age 2 20.6 28.2 56 7.4966533 F 33 58.9285714 4.4176707 twm_c... age 2 20.6 28.2 56 7.4966533 M 23 41.0714286 3.0789826 twm_c... age 3 28.2 35.8 92 12.3159304 F 49 53.2608696 6.5595716 twm_c... age 3 28.2 35.8 92 12.3159304 M 43 46.7391304 5.7563588 twm_c... age 4 35.8 43.4 107 14.3239625 F 63 58.8785047 8.4337349 twm_c... age 4 35.8 43.4 107 14.3239625 M 44 41.1214953 5.8902276 twm_c... age 5 43.4 51 88 11.7804552 F 52 59.0909091 6.961178 twm_c... age 5 43.4 51 88 11.7804552 M 36 40.9090909 4.8192771 twm_c... age 6 51 58.6 110 14.7255689 F 58 52.7272727 7.7643909 twm_c... age 6 51 58.6 110 14.7255689 M 52 47.2727273 6.961178 twm_c... age 7 58.6 66.2 71 9.5046854 F 41 57.7464789 5.4886212 twm_c... age 7 58.6 66.2 71 9.5046854 M 30 42.2535211 4.0160643 twm_c... age 8 66.2 73.8 35 4.6854083 F 17 48.5714286 2.2757697 twm_c... age 8 66.2 73.8 35 4.6854083 M 18 51.4285714 2.4096386 twm_c... age 9 73.8 81.4 28 3.7483266 F 18 64.2857143 2.4096386 twm_c... age 9 73.8 81.4 28 3.7483266 M 10 35.7142857 1.3386881 twm_c... age 10 81.4 89 20 2.6773762 F 9 45 1.2048193 twm_c... age 10 81.4 89 20 2.6773762 M 11 55 1.4725569 twm_c... income 1 0 14415.7 332 44.4444444 F 200 60.2409639 26.7737617 twm_c... income 1 0 14415.7 332 44.4444444 M 132 39.7590361 17.6706827 twm_c... income 2 14415.7 28831.4 191 25.5689424 F 117 61.2565445 15.6626506 twm_c... income 2 14415.7 28831.4 191 25.5689424 M 74 38.7434555 9.9062918 twm_c... income 3 28831.4 43247.1 108 14.4578313 F 50 46.2962963 6.6934404 twm_c... income 3 28831.4 43247.1 108 14.4578313 M 58 53.7037037 7.7643909 twm_c... income 4 43247.1 57662.8 63 8.4337349 F 30 47.6190476 4.0160643 twm_c... income 4 43247.1 57662.8 63 8.4337349 M 33 52.3809524 4.4176707 twm_c... income 5 57662.8 72078.5 20 2.6773762 F 12 60 1.6064257 twm_c... income 5 57662.8 72078.5 20 2.6773762 M 8 40 1.0709505 twm_c... income 6 72078.5 86494.2 19 2.5435074 F 6 31.5789474 .8032129 twm_c... income 6 72078.5 86494.2 19 2.5435074 M 13 68.4210526 1.7402945 twm_c... income 7 86494.2 100909.9 7 .9370817 F 1 14.2857143 .1338688 twm_c... income 7 86494.2 100909.9 7 .9370817 M 6 85.7142857 .8032129 twm_c... income 8 100909.9 115325.6 3 .4016064 F 2 66.6666667 .2677376 twm_c... income 8 100909.9 115325.6 3 .4016064 M 1 33.3333333 .1338688 twm_c... income 9 115325.6 129741.3 2 .2677376 M 2 100 .2677376 twm_c... income 10 129741.3 144157 2 .2677376z M 2 100 .2677376 Histogram Analysis Example #2 Data xtbl xmin_nbr... xman_nbr... xmean_nbr... xstd_nbr... twm_c... 0 0 0 0 twm_c... 0 0 0 0 twm_c... 0 2 .7878788 .7690047 twm_c... 0 2 .5217391 .650723 twm_c... 0 3 1.6326531 1.1192905 twm_c... 0 3 1.5813953 1.1858185 twm_c... 0 5 1.3333333 1.5013222 twm_c... 0 5 1.5227273 1.3566107 twm_c... 0 5 .8653846 1.2093998 twm_c... 0 5 1.2222222 1.7497795 twm_c... 0 2 .9655172 .6939521 twm_c... 0 2 .8461538 .7174907 twm_c... 0 2 9.7560976E-02 .3698964 twm_c... 0 2 .1333333 .4268749 twm_c... 0 0 0 0 twm_c... 0 0 0 0 twm_c... 0 0 0 0 twm_c... 0 0 0 0 twm_c... 0 0 0 0 twm_c... 0 0 0 0 twm_c... 0 3 .315 .7181748 twm_c... 0 3 .25 .6077155 twm_c... 0 5 1 1.132277 twm_c... 0 4 .7297297 1.0691914 twm_c... 0 5 .82 1.1779643 twm_c... 0 5 1.2413793 1.3684921 twm_c... 0 5 1.4666667 1.2578642 twm_c... 0 5 1.6666667 1.6080605 twm_c... 0 4 1.75 1.4215602 twm_c... 0 2 1 .8660254 twm_c... 0 4 1 1.4142136 twm_c... 0 3 .8461538 .9483714 twm_c... 1 1 1 0 twm_c... 0 2 .5 .7637626 twm_c... 0 2 1 1 twm_c... 0 0 0 0 twm_c... 1 2 1.0 .5 twm_c... 0 0 0 0 By default, the same two-dimensional graph shown in Tutorial #1 appears.
- Go to the Graph Options tab.
-
Select the Show Overlay
Counts and 3D Graph
radio buttons.
This graph is shown below.
Histogram Analysis Example #2 Graph: Three Dimensional View
Note the same ranges for “age” column as before (“13” to “89”). Now, however, each bin has been overlaid with the distinct values of “gender” (“M” and “F”). The counts for each overlay are represented by height. Note that, for the first bin range of “age” (“18 to 20.6”), there are approximately 75 females (where “gender” = “F”) and 60 males (where “gender” = “M”).
This image can be rotated either by double-clicking anywhere on the graph (automatic), or by the vertical and/or horizontal scroll-bars.
When rotating, the number from 0-359 in the uppermost left-hand corner of the graph is the degrees of rotation about the z-axis. This value changes as the horizontal scroll-bar is adjusted.
- Click on the Graph Options tab as described above to change the data being graphed from “age” to “income.”
-
Select the Show Bin
Stats radio button.
Note that this disables the Show Overlay Counts option as well as 3D Graph.
This graph is shown here.
Histogram Analysis Example #2 Graph
Note that the first bin range of the “income” variable is 0-14415.7. This is broken down into the two pieces, one for each overlay value (“M” and “F”). So, within this first bin of income, we have 200 females (where “gender” = “F”) and 132 males (where “gender” = “M”). Further, the statistics for “nbr_children” are shown. Note that the minimum value of nbr_children is 0, the maximum is 3, the mean is .315 and the standard deviation is .7181748. This is illustrated graphically by the orange square (mean), the wide blue bar (+/- one standard deviation), and the upper and lower blue line (minimum and maximum). Note that, since minus one standard deviation encompasses the minimum value, no lower blue line is shown.