Month: January 2017

Chi Squared Table (Right Tail)

chisquare-pvalue

Chi Squared Table

chi-square-table-75The chi squared table below is used in hypothesis testing. It helps you to decide whether to accept or reject the null hypothesis. The following chi squared table has the most common values for chi squared. You can find exact figures by using Excel (how to calculate a chi square p value Excel), SPSS (How to perform a chi square in SPSS) or other technology. However, in the vast majority of cases, the chi squared table will give you the value you need.

Right tail areas for the Chi-squared Distributiondownload-2

df\area .995 .990 .975 .950 .900 .750 .500 .250 .100
1 0.00004 0.00016 0.00098 0.00393 0.01579 0.10153 0.45494 1.32330 2.70554
2 0.01003 0.02010 0.05064 0.10259 0.21072 0.57536 1.38629 2.77259 4.60517
3 0.07172 0.11483 0.21580 0.35185 0.58437 1.21253 2.36597 4.10834 6.25139
4 0.20699 0.29711 0.48442 0.71072 1.06362 1.92256 3.35669 5.38527 7.77944
5 0.41174 0.55430 0.83121 1.14548 1.61031 2.67460 4.35146 6.62568 9.23636
6 0.67573 0.87209 1.23734 1.63538 2.20413 3.45460 5.34812 7.84080 10.64464
7 0.98926 1.23904 1.68987 2.16735 2.83311 4.25485 6.34581 9.03715 12.01704
8 1.34441 1.64650 2.17973 2.73264 3.48954 5.07064 7.34412 10.21885 13.36157
9 1.73493 2.08790 2.70039 3.32511 4.16816 5.89883 8.34283 11.38875 14.68366
10 2.15586 2.55821 3.24697 3.94030 4.86518 6.73720 9.34182 12.54886 15.98718
11 2.60322 3.05348 3.81575 4.57481 5.57778 7.58414 10.34100 13.70069 17.27501
12 3.07382 3.57057 4.40379 5.22603 6.30380 8.43842 11.34032 14.84540 18.54935
13 3.56503 4.10692 5.00875 5.89186 7.04150 9.29907 12.33976 15.98391 19.81193
14 4.07467 4.66043 5.62873 6.57063 7.78953 10.16531 13.33927 17.11693 21.06414
15 4.60092 5.22935 6.26214 7.26094 8.54676 11.03654 14.33886 18.24509 22.30713
16 5.14221 5.81221 6.90766 7.96165 9.31224 11.91222 15.33850 19.36886 23.54183
17 5.69722 6.40776 7.56419 8.67176 10.08519 12.79193 16.33818 20.48868 24.76904
18 6.26480 7.01491 8.23075 9.39046 10.86494 13.67529 17.33790 21.60489 25.98942
19 6.84397 7.63273 8.90652 10.11701 11.65091 14.56200 18.33765 22.71781 27.20357
20 7.43384 8.26040 9.59078 10.85081 12.44261 15.45177 19.33743 23.82769 28.41198
21 8.03365 8.89720 10.28290 11.59131 13.23960 16.34438 20.33723 24.93478 29.61509
22 8.64272 9.54249 10.98232 12.33801 14.04149 17.23962 21.33704 26.03927 30.81328
23 9.26042 10.19572 11.68855 13.09051 14.84796 18.13730 22.33688 27.14134 32.00690
24 9.88623 10.85636 12.40115 13.84843 15.65868 19.03725 23.33673 28.24115 33.19624
25 10.51965 11.52398 13.11972 14.61141 16.47341 19.93934 24.33659 29.33885 34.38159
26 11.16024 12.19815 13.84390 15.37916 17.29188 20.84343 25.33646 30.43457 35.56317
27 11.80759 12.87850 14.57338 16.15140 18.11390 21.74940 26.33634 31.52841 36.74122
28 12.46134 13.56471 15.30786 16.92788 18.93924 22.65716 27.33623 32.62049 37.91592
29 13.12115 14.25645 16.04707 17.70837 19.76774 23.56659 28.33613 33.71091 39.08747
30 13.78672 14.95346 16.79077 18.49266 20.59923 24.47761 29.33603 34.79974 40.25602

T-Distribution Table (Two Tail)

t-twotail

t-tables-75T-Distribution Table (Two Tails)download-2

For the T-Distribution Table for One Tail, click here.

df a = 0.2 0.10 0.05 0.02 0.01 0.002 0.001
ta = 1.282 1.645 1.960 2.326 2.576 3.091 3.291
1 3.078 6.314 12.706 31.821 63.656 318.289 636.578
2 1.886 2.920 4.303 6.965 9.925 22.328 31.600
3 1.638 2.353 3.182 4.541 5.841 10.214 12.924
4 1.533 2.132 2.776 3.747 4.604 7.173 8.610
5 1.476 2.015 2.571 3.365 4.032 5.894 6.869
6 1.440 1.943 2.447 3.143 3.707 5.208 5.959
7 1.415 1.895 2.365 2.998 3.499 4.785 5.408
8 1.397 1.860 2.306 2.896 3.355 4.501 5.041
9 1.383 1.833 2.262 2.821 3.250 4.297 4.781
10 1.372 1.812 2.228 2.764 3.169 4.144 4.587
11 1.363 1.796 2.201 2.718 3.106 4.025 4.437
12 1.356 1.782 2.179 2.681 3.055 3.930 4.318
13 1.350 1.771 2.160 2.650 3.012 3.852 4.221
14 1.345 1.761 2.145 2.624 2.977 3.787 4.140
15 1.341 1.753 2.131 2.602 2.947 3.733 4.073
16 1.337 1.746 2.120 2.583 2.921 3.686 4.015
17 1.333 1.740 2.110 2.567 2.898 3.646 3.965
18 1.330 1.734 2.101 2.552 2.878 3.610 3.922
19 1.328 1.729 2.093 2.539 2.861 3.579 3.883
20 1.325 1.725 2.086 2.528 2.845 3.552 3.850
21 1.323 1.721 2.080 2.518 2.831 3.527 3.819
22 1.321 1.717 2.074 2.508 2.819 3.505 3.792
23 1.319 1.714 2.069 2.500 2.807 3.485 3.768
24 1.318 1.711 2.064 2.492 2.797 3.467 3.745
25 1.316 1.708 2.060 2.485 2.787 3.450 3.725
26 1.315 1.706 2.056 2.479 2.779 3.435 3.707
27 1.314 1.703 2.052 2.473 2.771 3.421 3.689
28 1.313 1.701 2.048 2.467 2.763 3.408 3.674
29 1.311 1.699 2.045 2.462 2.756 3.396 3.660
30 1.310 1.697 2.042 2.457 2.750 3.385 3.646
60 1.296 1.671 2.000 2.390 2.660 3.232 3.460
120 1.289 1.658 1.980 2.358 2.617 3.160 3.373
1.282 1.645 1.960 2.326 2.576 3.091 3.291

T-Distribution Table (One Tail)

t-onetail-leftright

t-tables-75T-Distribution Table (One Tail)download-2

For the T-Distribution Table for Two Tails, Click Here.

df a = 0.1 0.05 0.025 0.01 0.005 0.001 0.0005
ta = 1.282 1.645 1.960 2.326 2.576 3.091 3.291
1 3.078 6.314 12.706 31.821 63.656 318.289 636.578
2 1.886 2.920 4.303 6.965 9.925 22.328 31.600
3 1.638 2.353 3.182 4.541 5.841 10.214 12.924
4 1.533 2.132 2.776 3.747 4.604 7.173 8.610
5 1.476 2.015 2.571 3.365 4.032 5.894 6.869
6 1.440 1.943 2.447 3.143 3.707 5.208 5.959
7 1.415 1.895 2.365 2.998 3.499 4.785 5.408
8 1.397 1.860 2.306 2.896 3.355 4.501 5.041
9 1.383 1.833 2.262 2.821 3.250 4.297 4.781
10 1.372 1.812 2.228 2.764 3.169 4.144 4.587
11 1.363 1.796 2.201 2.718 3.106 4.025 4.437
12 1.356 1.782 2.179 2.681 3.055 3.930 4.318
13 1.350 1.771 2.160 2.650 3.012 3.852 4.221
14 1.345 1.761 2.145 2.624 2.977 3.787 4.140
15 1.341 1.753 2.131 2.602 2.947 3.733 4.073
16 1.337 1.746 2.120 2.583 2.921 3.686 4.015
17 1.333 1.740 2.110 2.567 2.898 3.646 3.965
18 1.330 1.734 2.101 2.552 2.878 3.610 3.922
19 1.328 1.729 2.093 2.539 2.861 3.579 3.883
20 1.325 1.725 2.086 2.528 2.845 3.552 3.850
21 1.323 1.721 2.080 2.518 2.831 3.527 3.819
22 1.321 1.717 2.074 2.508 2.819 3.505 3.792
23 1.319 1.714 2.069 2.500 2.807 3.485 3.768
24 1.318 1.711 2.064 2.492 2.797 3.467 3.745
25 1.316 1.708 2.060 2.485 2.787 3.450 3.725
26 1.315 1.706 2.056 2.479 2.779 3.435 3.707
27 1.314 1.703 2.052 2.473 2.771 3.421 3.689
28 1.313 1.701 2.048 2.467 2.763 3.408 3.674
29 1.311 1.699 2.045 2.462 2.756 3.396 3.660
30 1.310 1.697 2.042 2.457 2.750 3.385 3.646
60 1.296 1.671 2.000 2.390 2.660 3.232 3.460
120 1.289 1.658 1.980 2.358 2.617 3.160 3.373
1000 1.282 1.646 1.962 2.330 2.581 3.098 3.300

Z-Table (Right of Curve)

 

 

z-right2-modified

This z-table (normal distribution table) shows the area to the right hand side of the curve. Use these values to find the area between z=0 and any positive value.download-2

z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.0 0.0000 0.0040 0.0080 0.0120 0.0160 0.0199 0.0239 0.0279 0.0319 0.0359
0.1 0.0398 0.0438 0.0478 0.0517 0.0557 0.0596 0.0636 0.0675 0.0714 0.0753
0.2 0.0793 0.0832 0.0871 0.0910 0.0948 0.0987 0.1026 0.1064 0.1103 0.1141
0.3 0.1179 0.1217 0.1255 0.1293 0.1331 0.1368 0.1406 0.1443 0.1480 0.1517
0.4 0.1554 0.1591 0.1628 0.1664 0.1700 0.1736 0.1772 0.1808 0.1844 0.1879
0.5 0.1915 0.1950 0.1985 0.2019 0.2054 0.2088 0.2123 0.2157 0.2190 0.2224
0.6 0.2257 0.2291 0.2324 0.2357 0.2389 0.2422 0.2454 0.2486 0.2517 0.2549
0.7 0.2580 0.2611 0.2642 0.2673 0.2704 0.2734 0.2764 0.2794 0.2823 0.2852
0.8 0.2881 0.2910 0.2939 0.2967 0.2995 0.3023 0.3051 0.3078 0.3106 0.3133
0.9 0.3159 0.3186 0.3212 0.3238 0.3264 0.3289 0.3315 0.3340 0.3365 0.3389
1.0 0.3413 0.3438 0.3461 0.3485 0.3508 0.3531 0.3554 0.3577 0.3599 0.3621
1.1 0.3643 0.3665 0.3686 0.3708 0.3729 0.3749 0.3770 0.3790 0.3810 0.3830
1.2 0.3849 0.3869 0.3888 0.3907 0.3925 0.3944 0.3962 0.3980 0.3997 0.4015
1.3 0.4032 0.4049 0.4066 0.4082 0.4099 0.4115 0.4131 0.4147 0.4162 0.4177
1.4 0.4192 0.4207 0.4222 0.4236 0.4251 0.4265 0.4279 0.4292 0.4306 0.4319
1.5 0.4332 0.4345 0.4357 0.4370 0.4382 0.4394 0.4406 0.4418 0.4429 0.4441
1.6 0.4452 0.4463 0.4474 0.4484 0.4495 0.4505 0.4515 0.4525 0.4535 0.4545
1.7 0.4554 0.4564 0.4573 0.4582 0.4591 0.4599 0.4608 0.4616 0.4625 0.4633
1.8 0.4641 0.4649 0.4656 0.4664 0.4671 0.4678 0.4686 0.4693 0.4699 0.4706
1.9 0.4713 0.4719 0.4726 0.4732 0.4738 0.4744 0.4750 0.4756 0.4761 0.4767
2.0 0.4772 0.4778 0.4783 0.4788 0.4793 0.4798 0.4803 0.4808 0.4812 0.4817
2.1 0.4821 0.4826 0.4830 0.4834 0.4838 0.4842 0.4846 0.4850 0.4854 0.4857
2.2 0.4861 0.4864 0.4868 0.4871 0.4875 0.4878 0.4881 0.4884 0.4887 0.4890
2.3 0.4893 0.4896 0.4898 0.4901 0.4904 0.4906 0.4909 0.4911 0.4913 0.4916
2.4 0.4918 0.4920 0.4922 0.4925 0.4927 0.4929 0.4931 0.4932 0.4934 0.4936
2.5 0.4938 0.4940 0.4941 0.4943 0.4945 0.4946 0.4948 0.4949 0.4951 0.4952
2.6 0.4953 0.4955 0.4956 0.4957 0.4959 0.4960 0.4961 0.4962 0.4963 0.4964
2.7 0.4965 0.4966 0.4967 0.4968 0.4969 0.4970 0.4971 0.4972 0.4973 0.4974
2.8 0.4974 0.4975 0.4976 0.4977 0.4977 0.4978 0.4979 0.4979 0.4980 0.4981
2.9 0.4981 0.4982 0.4982 0.4983 0.4984 0.4984 0.4985 0.4985 0.4986 0.4986
3.0 0.4987 0.4987 0.4987 0.4988 0.4988 0.4989 0.4989 0.4989 0.4990 0.4990
3.1 0.4990 0.4991 0.4991 0.4991 0.4992 0.4992 0.4992 0.4992 0.4993 0.4993
3.2 0.4993 0.4993 0.4994 0.4994 0.4994 0.4994 0.4994 0.4995 0.4995 0.4995
3.3 0.4995 0.4995 0.4995 0.4996 0.4996 0.4996 0.4996 0.4996 0.4996 0.4997
3.4 0.4997 0.4997 0.4997 0.4997 0.4997 0.4997 0.4997 0.4997 0.4997 0.4998
3.5 0.4998 0.4998 0.4998 0.4998 0.4998 0.4998 0.4998 0.4998 0.4998 0.4998
3.6 0.4998 0.4998 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999
3.7 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999
3.8 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999 0.4999

Z-Table (Left of Curve)

untitled

Standard Normal (Z) Table

z-table-left-curve-75This table shows the area to the left of Z. In other words, the area of a left hand tail. If you want to find the value between z=0 and a positive number, use this right-hand z-table. (Hint: if you’re asked to look at the “z-table”, in most cases you’ll want to be looking at the other z-table!)download-2

Z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.0 0.5000 0.5040 0.5080 0.0120 0.0160 0.0199 0.5239 0.0279 0.0319 0.0359
0.1 0.5398 0.5438 0.5478 0.5517 0.5557 0.5596 0.5636 0.5675 0.5714 0.5753
0.2 0.5793 0.5832 0.5871 0.5910 0.5948 0.5987 0.6064 0.1064 0.6103 0.6141
0.3 0.6179 0.6217 0.6255 0.6293 0.6331 0.6368 0.6406 0.6443 0.6480 0.6517
0.4 0.6554 0.6591 0.6628 0.6664 0.6700 0.6736 0.6772 0.6808 0.6844 0.6879
0.5 0.6915 0.6950 0.6985 0.7019 0.7054 0.7088 0.7123 0.7157 0.7190 0.7224
0.6 0.7257 0.7291 0.7324 0.7357 0.7389 0.7422 0.7454 0.7486 0.7517 0.7549
0.7 0.7580 0.7611 0.7642 0.7673 0.7704 0.7734 0.7764 0.7794 0.7823 0.7852
0.8 0.7881 0.7910 0.7939 0.7967 0.7995 0.8023 0.8051 0.8078 0.8106 0.8133
0.9 0.8159 0.8186 0.8212 0.8238 0.8264 0.8289 0.8315 0.8340 0.8365 0.8389
1.0 0.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0.8554 0.8577 0.8599 0.8621
1.1 0.8643 0.8665 0.8686 0.8708 0.8729 0.8749 0.8770 0.8790 0.8810 0.8830
1.2 0.8849 0.8869 0.8888 0.8907 0.8925 0.8944 0.8962 0.8980 0.8997 0.9015
1.3 0.9032 0.9049 0.9066 0.9082 0.9099 0.9115 0.9131 0.9147 0.9162 0.9177
1.4 0.9192 0.9207 0.9222 0.9236 0.9251 0.9265 0.9279 0.9292 0.9306 0.9319
1.5 0.9332 0.9345 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9429 0.9441
1.6 0.9452 0.9463 0.9474 0.9484 0.9495 0.9505 0.9515 0.9525 0.9535 0.9545
1.7 0.9554 0.9564 0.9573 0.9582 0.9591 0.9599 0.9608 0.9616 0.9625 0.9633
1.8 0.9641 0.9649 0.9656 0.9664 0.9671 0.9678 0.9686 0.9693 0.9699 0.9706
1.9 0.9713 0.9719 0.9726 0.9732 0.9738 0.9744 0.9750 0.9756 0.9761 0.9767
2.0 0.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817
2.1 0.9821 0.9826 0.9830 0.9834 0.9838 0.9842 0.9846 0.9850 0.9854 0.9857
2.2 0.9861 0.9864 0.9868 0.9871 0.9875 0.9878 0.9881 0.9884 0.9887 0.9890
2.3 0.9893 0.9896 0.9898 0.9901 0.9904 0.9906 0.9909 0.9911 0.9913 0.9916
2.4 0.9918 0.9920 0.9922 0.9925 0.9927 0.9929 0.9931 0.9932 0.9934 0.9936
2.5 0.9938 0.9940 0.9941 0.9943 0.9945 0.9946 0.9948 0.9949 0.9951 0.9952
2.6 0.9953 0.9955 0.9956 0.9957 0.9959 0.9960 0.9961 0.9962 0.9963 0.9964
2.7 0.9965 0.9966 0.9967 0.9968 0.9969 0.9970 0.9971 0.9972 0.9973 0.9974
2.8 0.9974 0.9975 0.9976 0.9977 0.9977 0.9978 0.9979 0.9979 0.9980 0.9981
2.9 0.9981 0.9982 0.9982 0.9983 0.9984 0.9984 0.9985 0.9985 0.9986 0.9986
3.0 0.9987 0.9987 0.9987 0.9988 0.9988 0.9989 0.9989 0.9989 0.9990 0.9990

F-Table

The F Table is used to look up F Statistics in hypothesis testing. While it’s more common to use technology like Excel or SPSS to run tests, the F Table can be useful for quickly looking up several different values at once.

f-tables-75F Tables.download-2

f tableNote on reading the tables: DF2 is read DOWN the first column. DF1 is read ACROSS the top row. For example, if you wanted to find DF2 = 5, DF1 = 5 in the first table (α = .10), you would get 4.06042.

F Table for alpha=.10

DF1 (see next table below for 12 to ∞)

DF2 1 2 3 4 5 6 7 8 9 10
1 39.86346 49.50000 53.59324 55.83296 57.24008 58.20442 58.90595 59.43898 59.85759 60.19498
2 8.52632 9.00000 9.16179 9.24342 9.29263 9.32553 9.34908 9.36677 9.38054 9.39157
3 5.53832 5.46238 5.39077 5.34264 5.30916 5.28473 5.26619 5.25167 5.24000 5.23041
4 4.54477 4.32456 4.19086 4.10725 4.05058 4.00975 3.97897 3.95494 3.93567 3.91988
5 4.06042 3.77972 3.61948 3.52020 3.45298 3.40451 3.36790 3.33928 3.31628 3.29740
6 3.77595 3.46330 3.28876 3.18076 3.10751 3.05455 3.01446 2.98304 2.95774 2.93693
7 3.58943 3.25744 3.07407 2.96053 2.88334 2.82739 2.78493 2.75158 2.72468 2.70251
8 3.45792 3.11312 2.92380 2.80643 2.72645 2.66833 2.62413 2.58935 2.56124 2.53804
9 3.36030 3.00645 2.81286 2.69268 2.61061 2.55086 2.50531 2.46941 2.44034 2.41632
10 3.28502 2.92447 2.72767 2.60534 2.52164 2.46058 2.41397 2.37715 2.34731 2.32260
11 3.22520 2.85951 2.66023 2.53619 2.45118 2.38907 2.34157 2.30400 2.27350 2.24823
12 3.17655 2.80680 2.60552 2.48010 2.39402 2.33102 2.28278 2.24457 2.21352 2.18776
13 3.13621 2.76317 2.56027 2.43371 2.34672 2.28298 2.23410 2.19535 2.16382 2.13763
14 3.10221 2.72647 2.52222 2.39469 2.30694 2.24256 2.19313 2.15390 2.12195 2.09540
15 3.07319 2.69517 2.48979 2.36143 2.27302 2.20808 2.15818 2.11853 2.08621 2.05932
16 3.04811 2.66817 2.46181 2.33274 2.24376 2.17833 2.12800 2.08798 2.05533 2.02815
17 3.02623 2.64464 2.43743 2.30775 2.21825 2.15239 2.10169 2.06134 2.02839 2.00094
18 3.00698 2.62395 2.41601 2.28577 2.19583 2.12958 2.07854 2.03789 2.00467 1.97698
19 2.98990 2.60561 2.39702 2.26630 2.17596 2.10936 2.05802 2.01710 1.98364 1.95573
20 2.97465 2.58925 2.38009 2.24893 2.15823 2.09132 2.03970 1.99853 1.96485 1.93674
21 2.96096 2.57457 2.36489 2.23334 2.14231 2.07512 2.02325 1.98186 1.94797 1.91967
22 2.94858 2.56131 2.35117 2.21927 2.12794 2.06050 2.00840 1.96680 1.93273 1.90425
23 2.93736 2.54929 2.33873 2.20651 2.11491 2.04723 1.99492 1.95312 1.91888 1.89025
24 2.92712 2.53833 2.32739 2.19488 2.10303 2.03513 1.98263 1.94066 1.90625 1.87748
25 2.91774 2.52831 2.31702 2.18424 2.09216 2.02406 1.97138 1.92925 1.89469 1.86578
26 2.90913 2.51910 2.30749 2.17447 2.08218 2.01389 1.96104 1.91876 1.88407 1.85503
27 2.90119 2.51061 2.29871 2.16546 2.07298 2.00452 1.95151 1.90909 1.87427 1.84511
28 2.89385 2.50276 2.29060 2.15714 2.06447 1.99585 1.94270 1.90014 1.86520 1.83593
29 2.88703 2.49548 2.28307 2.14941 2.05658 1.98781 1.93452 1.89184 1.85679 1.82741
30 2.88069 2.48872 2.27607 2.14223 2.04925 1.98033 1.92692 1.88412 1.84896 1.81949
40 2.83535 2.44037 2.22609 2.09095 1.99682 1.92688 1.87252 1.82886 1.79290 1.76269
60 2.79107 2.39325 2.17741 2.04099 1.94571 1.87472 1.81939 1.77483 1.73802 1.70701
120 2.74781 2.34734 2.12999 1.99230 1.89587 1.82381 1.76748 1.72196 1.68425 1.65238
inf 2.70554 2.30259 2.08380 1.94486 1.84727 1.77411 1.71672 1.67020 1.63152 1.59872

DF 1

DF2 12 15 20 24 30 40 60 120 inf
1 60.70521 61.22034 61.74029 62.00205 62.26497 62.52905 62.79428 63.06064 63.32812
2 9.40813 9.42471 9.44131 9.44962 9.45793 9.46624 9.47456 9.48289 9.49122
3 5.21562 5.20031 5.18448 5.17636 5.16811 5.15972 5.15119 5.14251 5.13370
4 3.89553 3.87036 3.84434 3.83099 3.81742 3.80361 3.78957 3.77527 3.76073
5 3.26824 3.23801 3.20665 3.19052 3.17408 3.15732 3.14023 3.12279 3.10500
6 2.90472 2.87122 2.83634 2.81834 2.79996 2.78117 2.76195 2.74229 2.72216
7 2.66811 2.63223 2.59473 2.57533 2.55546 2.53510 2.51422 2.49279 2.47079
8 2.50196 2.46422 2.42464 2.40410 2.38302 2.36136 2.33910 2.31618 2.29257
9 2.37888 2.33962 2.29832 2.27683 2.25472 2.23196 2.20849 2.18427 2.15923
10 2.28405 2.24351 2.20074 2.17843 2.15543 2.13169 2.10716 2.08176 2.05542
11 2.20873 2.16709 2.12305 2.10001 2.07621 2.05161 2.02612 1.99965 1.97211
12 2.14744 2.10485 2.05968 2.03599 2.01149 1.98610 1.95973 1.93228 1.90361
13 2.09659 2.05316 2.00698 1.98272 1.95757 1.93147 1.90429 1.87591 1.84620
14 2.05371 2.00953 1.96245 1.93766 1.91193 1.88516 1.85723 1.82800 1.79728
15 2.01707 1.97222 1.92431 1.89904 1.87277 1.84539 1.81676 1.78672 1.75505
16 1.98539 1.93992 1.89127 1.86556 1.83879 1.81084 1.78156 1.75075 1.71817
17 1.95772 1.91169 1.86236 1.83624 1.80901 1.78053 1.75063 1.71909 1.68564
18 1.93334 1.88681 1.83685 1.81035 1.78269 1.75371 1.72322 1.69099 1.65671
19 1.91170 1.86471 1.81416 1.78731 1.75924 1.72979 1.69876 1.66587 1.63077
20 1.89236 1.84494 1.79384 1.76667 1.73822 1.70833 1.67678 1.64326 1.60738
21 1.87497 1.82715 1.77555 1.74807 1.71927 1.68896 1.65691 1.62278 1.58615
22 1.85925 1.81106 1.75899 1.73122 1.70208 1.67138 1.63885 1.60415 1.56678
23 1.84497 1.79643 1.74392 1.71588 1.68643 1.65535 1.62237 1.58711 1.54903
24 1.83194 1.78308 1.73015 1.70185 1.67210 1.64067 1.60726 1.57146 1.53270
25 1.82000 1.77083 1.71752 1.68898 1.65895 1.62718 1.59335 1.55703 1.51760
26 1.80902 1.75957 1.70589 1.67712 1.64682 1.61472 1.58050 1.54368 1.50360
27 1.79889 1.74917 1.69514 1.66616 1.63560 1.60320 1.56859 1.53129 1.49057
28 1.78951 1.73954 1.68519 1.65600 1.62519 1.59250 1.55753 1.51976 1.47841
29 1.78081 1.73060 1.67593 1.64655 1.61551 1.58253 1.54721 1.50899 1.46704
30 1.77270 1.72227 1.66731 1.63774 1.60648 1.57323 1.53757 1.49891 1.45636
40 1.71456 1.66241 1.60515 1.57411 1.54108 1.50562 1.46716 1.42476 1.37691
60 1.65743 1.60337 1.54349 1.51072 1.47554 1.43734 1.39520 1.34757 1.29146
120 1.60120 1.54500 1.48207 1.44723 1.40938 1.36760 1.32034 1.26457 1.19256
inf 1.54578 1.48714 1.42060 1.38318 1.34187 1.29513 1.23995 1.16860 1.00000

F Table for alpha=.05

DF1 (see next table below for 12 to ∞)

df2 1 2 3 4 5 6 7 8 9 10
1 161.4476 199.5000 215.7073 224.5832 230.1619 233.9860 236.7684 238.8827 240.5433 241.8817
2 18.5128 19.0000 19.1643 19.2468 19.2964 19.3295 19.3532 19.3710 19.3848 19.3959
3 10.1280 9.5521 9.2766 9.1172 9.0135 8.9406 8.8867 8.8452 8.8123 8.7855
4 7.7086 6.9443 6.5914 6.3882 6.2561 6.1631 6.0942 6.0410 5.9988 5.9644
5 6.6079 5.7861 5.4095 5.1922 5.0503 4.9503 4.8759 4.8183 4.7725 4.7351
6 5.9874 5.1433 4.7571 4.5337 4.3874 4.2839 4.2067 4.1468 4.0990 4.0600
7 5.5914 4.7374 4.3468 4.1203 3.9715 3.8660 3.7870 3.7257 3.6767 3.6365
8 5.3177 4.4590 4.0662 3.8379 3.6875 3.5806 3.5005 3.4381 3.3881 3.3472
9 5.1174 4.2565 3.8625 3.6331 3.4817 3.3738 3.2927 3.2296 3.1789 3.1373
10 4.9646 4.1028 3.7083 3.4780 3.3258 3.2172 3.1355 3.0717 3.0204 2.9782
11 4.8443 3.9823 3.5874 3.3567 3.2039 3.0946 3.0123 2.9480 2.8962 2.8536
12 4.7472 3.8853 3.4903 3.2592 3.1059 2.9961 2.9134 2.8486 2.7964 2.7534
13 4.6672 3.8056 3.4105 3.1791 3.0254 2.9153 2.8321 2.7669 2.7144 2.6710
14 4.6001 3.7389 3.3439 3.1122 2.9582 2.8477 2.7642 2.6987 2.6458 2.6022
15 4.5431 3.6823 3.2874 3.0556 2.9013 2.7905 2.7066 2.6408 2.5876 2.5437
16 4.4940 3.6337 3.2389 3.0069 2.8524 2.7413 2.6572 2.5911 2.5377 2.4935
17 4.4513 3.5915 3.1968 2.9647 2.8100 2.6987 2.6143 2.5480 2.4943 2.4499
18 4.4139 3.5546 3.1599 2.9277 2.7729 2.6613 2.5767 2.5102 2.4563 2.4117
19 4.3807 3.5219 3.1274 2.8951 2.7401 2.6283 2.5435 2.4768 2.4227 2.3779
20 4.3512 3.4928 3.0984 2.8661 2.7109 2.5990 2.5140 2.4471 2.3928 2.3479
21 4.3248 3.4668 3.0725 2.8401 2.6848 2.5727 2.4876 2.4205 2.3660 2.3210
22 4.3009 3.4434 3.0491 2.8167 2.6613 2.5491 2.4638 2.3965 2.3419 2.2967
23 4.2793 3.4221 3.0280 2.7955 2.6400 2.5277 2.4422 2.3748 2.3201 2.2747
24 4.2597 3.4028 3.0088 2.7763 2.6207 2.5082 2.4226 2.3551 2.3002 2.2547
25 4.2417 3.3852 2.9912 2.7587 2.6030 2.4904 2.4047 2.3371 2.2821 2.2365
26 4.2252 3.3690 2.9752 2.7426 2.5868 2.4741 2.3883 2.3205 2.2655 2.2197
27 4.2100 3.3541 2.9604 2.7278 2.5719 2.4591 2.3732 2.3053 1.7306 1.6717
28 4.1960 3.3404 2.9467 2.7141 2.5581 2.4453 2.3593 2.2913 2.2360 2.1900
29 4.1830 3.3277 2.9340 2.7014 2.5454 2.4324 2.3463 2.2783 2.2229 2.1768
30 4.1709 3.3158 2.9223 2.6896 2.5336 2.4205 2.3343 2.2662 2.2107 2.1646
40 4.0847 3.2317 2.8387 2.6060 2.4495 2.3359 2.2490 2.1802 2.1240 2.0772
60 4.0012 3.1504 2.7581 2.5252 2.3683 2.2541 2.1665 2.0970 2.0401 1.9926
120 3.9201 3.0718 2.6802 2.4472 2.2899 2.1750 2.0868 2.0164 1.9588 1.9105
inf 3.8415 2.9957 2.6049 2.3719 2.2141 2.0986 2.0096 1.9384 1.8799 1.8307
df2 12 15 20 24 30 40 60 120 inf
1 243.9060 245.9499 248.0131 249.0518 250.0951 251.1432 252.1957 253.2529 254.3144
2 19.4125 19.4291 19.4458 19.4541 19.4624 19.4707 19.4791 19.4874 19.4957
3 8.7446 8.7029 8.6602 8.6385 8.6166 8.5944 8.5720 8.5494 8.5264
4 5.9117 5.8578 5.8025 5.7744 5.7459 5.7170 5.6877 5.6581 5.6281
5 4.6777 4.6188 4.5581 4.5272 4.4957 4.4638 4.4314 4.3985 4.3650
6 3.9999 3.9381 3.8742 3.8415 3.8082 3.7743 3.7398 3.7047 3.6689
7 3.5747 3.5107 3.4445 3.4105 3.3758 3.3404 3.3043 3.2674 3.2298
8 3.2839 3.2184 3.1503 3.1152 3.0794 3.0428 3.0053 2.9669 2.9276
9 3.0729 3.0061 2.9365 2.9005 2.8637 2.8259 2.7872 2.7475 2.7067
10 2.9130 2.8450 2.7740 2.7372 2.6996 2.6609 2.6211 2.5801 2.5379
11 2.7876 2.7186 2.6464 2.6090 2.5705 2.5309 2.4901 2.4480 2.4045
12 2.6866 2.6169 2.5436 2.5055 2.4663 2.4259 2.3842 2.3410 2.2962
13 2.6037 2.5331 2.4589 2.4202 2.3803 2.3392 2.2966 2.2524 2.2064
14 2.5342 2.4630 2.3879 2.3487 2.3082 2.2664 2.2229 2.1778 2.1307
15 2.4753 2.4034 2.3275 2.2878 2.2468 2.2043 2.1601 2.1141 2.0658
16 2.4247 2.3522 2.2756 2.2354 2.1938 2.1507 2.1058 2.0589 2.0096
17 2.3807 2.3077 2.2304 2.1898 2.1477 2.1040 2.0584 2.0107 1.9604
18 2.3421 2.2686 2.1906 2.1497 2.1071 2.0629 2.0166 1.9681 1.9168
19 2.3080 2.2341 2.1555 2.1141 2.0712 2.0264 1.9795 1.9302 1.8780
20 2.2776 2.2033 2.1242 2.0825 2.0391 1.9938 1.9464 1.8963 1.8432
21 2.2504 2.1757 2.0960 2.0540 2.0102 1.9645 1.9165 1.8657 1.8117
22 2.2258 2.1508 2.0707 2.0283 1.9842 1.9380 1.8894 1.8380 1.7831
23 2.2036 2.1282 2.0476 2.0050 1.9605 1.9139 1.8648 1.8128 1.7570
24 2.1834 2.1077 2.0267 1.9838 1.9390 1.8920 1.8424 1.7896 1.7330
25 2.1649 2.0889 2.0075 1.9643 1.9192 1.8718 1.8217 1.7684 1.7110
26 2.1479 2.0716 1.9898 1.9464 1.9010 1.8533 1.8027 1.7488 1.6906
27 2.1323 2.0558 1.9736 1.9299 1.8842 1.8361 1.7851 1.7306 1.6717
28 2.1179 2.0411 1.9586 1.9147 1.8687 1.8203 1.7689 1.7138 1.6541
29 2.1045 2.0275 1.9446 1.9005 1.8543 1.8055 1.7537 1.6981 1.6376
30 2.0921 2.0148 1.9317 1.8874 1.8409 1.7918 1.7396 1.6835 1.6223
40 2.0035 1.9245 1.8389 1.7929 1.7444 1.6928 1.6373 1.5766 1.5089
60 1.9174 1.8364 1.7480 1.7001 1.6491 1.5943 1.5343 1.4673 1.3893
120 1.8337 1.7505 1.6587 1.6084 1.5543 1.4952 1.4290 1.3519 1.2539
inf 1.7522 1.6664 1.5705 1.5173 1.4591 1.3940 1.3180 1.2214 1.0000

F Table for alpha=.025

DF1 (see next table below for 12 to ∞)

df2 1 2 3 4 5 6 7 8 9 10
1 647.7890 799.5000 864.1630 899.5833 921.8479 937.1111 948.2169 956.6562 963.2846 968.6274
2 38.5063 39.0000 39.1655 39.2484 39.2982 39.3315 39.3552 39.3730 39.3869 39.3980
3 17.4434 16.0441 15.4392 15.1010 14.8848 14.7347 14.6244 14.5399 14.4731 14.4189
4 12.2179 10.6491 9.9792 9.6045 9.3645 9.1973 9.0741 8.9796 8.9047 8.8439
5 10.0070 8.4336 7.7636 7.3879 7.1464 6.9777 6.8531 6.7572 6.6811 6.6192
6 8.8131 7.2599 6.5988 6.2272 5.9876 5.8198 5.6955 5.5996 5.5234 5.4613
7 8.0727 6.5415 5.8898 5.5226 5.2852 5.1186 4.9949 4.8993 4.8232 4.7611
8 7.5709 6.0595 5.4160 5.0526 4.8173 4.6517 4.5286 4.4333 4.3572 4.2951
9 7.2093 5.7147 5.0781 4.7181 4.4844 4.3197 4.1970 4.1020 4.0260 3.9639
10 6.9367 5.4564 4.8256 4.4683 4.2361 4.0721 3.9498 3.8549 3.7790 3.7168
11 6.7241 5.2559 4.6300 4.2751 4.0440 3.8807 3.7586 3.6638 3.5879 3.5257
12 6.5538 5.0959 4.4742 4.1212 3.8911 3.7283 3.6065 3.5118 3.4358 3.3736
13 6.4143 4.9653 4.3472 3.9959 3.7667 3.6043 3.4827 3.3880 3.3120 3.2497
14 6.2979 4.8567 4.2417 3.8919 3.6634 3.5014 3.3799 3.2853 3.2093 3.1469
15 6.1995 4.7650 4.1528 3.8043 3.5764 3.4147 3.2934 3.1987 3.1227 3.0602
16 6.1151 4.6867 4.0768 3.7294 3.5021 3.3406 3.2194 3.1248 3.0488 2.9862
17 6.0420 4.6189 4.0112 3.6648 3.4379 3.2767 3.1556 3.0610 2.9849 2.9222
18 5.9781 4.5597 3.9539 3.6083 3.3820 3.2209 3.0999 3.0053 2.9291 2.8664
19 5.9216 4.5075 3.9034 3.5587 3.3327 3.1718 3.0509 2.9563 2.8801 2.8172
20 5.8715 4.4613 3.8587 3.5147 3.2891 3.1283 3.0074 2.9128 2.8365 2.7737
21 5.8266 4.4199 3.8188 3.4754 3.2501 3.0895 2.9686 2.8740 2.7977 2.7348
22 5.7863 4.3828 3.7829 3.4401 3.2151 3.0546 2.9338 2.8392 2.7628 2.6998
23 5.7498 4.3492 3.7505 3.4083 3.1835 3.0232 2.9023 2.8077 2.7313 2.6682
24 5.7166 4.3187 3.7211 3.3794 3.1548 2.9946 2.8738 2.7791 2.7027 2.6396
25 5.6864 4.2909 3.6943 3.3530 3.1287 2.9685 2.8478 2.7531 2.6766 2.6135
26 5.6586 4.2655 3.6697 3.3289 3.1048 2.9447 2.8240 2.7293 2.6528 1.878
27 5.6331 4.2421 3.6472 3.3067 3.0828 2.9228 2.8021 2.7074 2.6309 2.5676
28 5.6096 4.2205 3.6264 3.2863 3.0626 2.9027 2.7820 2.6872 2.6106 2.5473
29 5.5878 4.2006 3.6072 3.2674 3.0438 2.8840 2.7633 2.6686 2.5919 2.5286
30 5.5675 4.1821 3.5894 3.2499 3.0265 2.8667 2.7460 2.6513 2.5746 2.5112
40 5.4239 4.0510 3.4633 3.1261 2.9037 2.7444 2.6238 2.5289 2.4519 2.3882
60 5.2856 3.9253 3.3425 3.0077 2.7863 2.6274 2.5068 2.4117 2.3344 2.2702
120 5.1523 3.8046 3.2269 2.8943 2.6740 2.5154 2.3948 2.2994 2.2217 2.1570
inf 5.0239 3.6889 3.1161 2.7858 2.5665 2.4082 2.2875 2.1918 2.1136 2.0483
df2 12 15 20 24 30 40 60 120 inf
1 976.7079 984.8668 993.1028 997.2492 1001.414 1005.598 1009.800 1014.020 1018.258
2 39.4146 39.4313 39.4479 39.4562 39.465 39.473 39.481 39.490 39.498
3 14.3366 14.2527 14.1674 14.1241 14.081 14.037 13.992 13.947 13.902
4 8.7512 8.6565 8.5599 8.5109 8.461 8.411 8.360 8.309 8.257
5 6.5245 6.4277 6.3286 6.2780 6.227 6.175 6.123 6.069 6.015
6 5.3662 5.2687 5.1684 5.1172 5.065 5.012 4.959 4.904 4.849
7 4.6658 4.5678 4.4667 4.4150 4.362 4.309 4.254 4.199 4.142
8 4.1997 4.1012 3.9995 3.9472 3.894 3.840 3.784 3.728 3.670
9 3.8682 3.7694 3.6669 3.6142 3.560 3.505 3.449 3.392 3.333
10 3.6209 3.5217 3.4185 3.3654 3.311 3.255 3.198 3.140 3.080
11 3.4296 3.3299 3.2261 3.1725 3.118 3.061 3.004 2.944 2.883
12 3.2773 3.1772 3.0728 3.0187 2.963 2.906 2.848 2.787 2.725
13 3.1532 3.0527 2.9477 2.8932 2.837 2.780 2.720 2.659 2.595
14 3.0502 2.9493 2.8437 2.7888 2.732 2.674 2.614 2.552 2.487
15 2.9633 2.8621 2.7559 2.7006 2.644 2.585 2.524 2.461 2.395
16 2.8890 2.7875 2.6808 2.6252 2.568 2.509 2.447 2.383 2.316
17 2.8249 2.7230 2.6158 2.5598 2.502 2.442 2.380 2.315 2.247
18 2.7689 2.6667 2.5590 2.5027 2.445 2.384 2.321 2.256 2.187
19 2.7196 2.6171 2.5089 2.4523 2.394 2.333 2.270 2.203 2.133
20 2.6758 2.5731 2.4645 2.4076 2.349 2.287 2.223 2.156 2.085
21 2.6368 2.5338 2.4247 2.3675 2.308 2.246 2.182 2.114 2.042
22 2.6017 2.4984 2.3890 2.3315 2.272 2.210 2.145 2.076 2.003
23 2.5699 2.4665 2.3567 2.2989 2.239 2.176 2.111 2.041 1.968
24 2.5411 2.4374 2.3273 2.2693 2.209 2.146 2.080 2.010 1.935
25 2.5149 2.4110 2.3005 2.2422 2.182 2.118 2.052 1.981 1.906
26 2.4908 2.3867 2.2759 2.2174 2.157 2.093 2.026 1.954 1.878
27 2.4688 2.3644 2.2533 2.1946 2.133 2.069 2.002 1.930 1.853
28 2.4484 2.3438 2.2324 2.1735 2.112 2.048 1.980 1.907 1.829
29 2.4295 2.3248 2.2131 2.1540 2.092 2.028 1.959 1.886 1.807
30 2.4120 2.3072 2.1952 2.1359 2.074 2.009 1.940 1.866 1.787
40 2.2882 2.1819 2.0677 2.0069 1.943 1.875 1.803 1.724 1.637
60 2.1692 2.0613 1.9445 1.8817 1.815 1.744 1.667 1.581 1.482
120 2.0548 1.9450 1.8249 1.7597 1.690 1.614 1.530 1.433 1.310
inf 1.9447 1.8326 1.7085 1.6402 1.566 1.484 1.388 1.268 1.000

F Table for alpha=.01

DF1 (see table below for 12 to ∞)

df2/df1 1 2 3 4 5 6 7 8 9 10
1 4052.181 4999.500 5403.352 5624.583 5763.650 5858.986 5928.356 5981.070 6022.473 6055.847
2 98.503 99.000 99.166 99.249 99.299 99.333 99.356 99.374 99.388 99.399
3 34.116 30.817 29.457 28.710 28.237 27.911 27.672 27.489 27.345 27.229
4 21.198 18.000 16.694 15.977 15.522 15.207 14.976 14.799 14.659 14.546
5 16.258 13.274 12.060 11.392 10.967 10.672 10.456 10.289 10.158 10.051
6 13.745 10.925 9.780 9.148 8.746 8.466 8.260 8.102 7.976 7.874
7 12.246 9.547 8.451 7.847 7.460 7.191 6.993 6.840 6.719 6.620
8 11.259 8.649 7.591 7.006 6.632 6.371 6.178 6.029 5.911 5.814
9 10.561 8.022 6.992 6.422 6.057 5.802 5.613 5.467 5.351 5.257
10 10.044 7.559 6.552 5.994 5.636 5.386 5.200 5.057 4.942 4.849
11 9.646 7.206 6.217 5.668 5.316 5.069 4.886 4.744 4.632 4.539
12 9.330 6.927 5.953 5.412 5.064 4.821 4.640 4.499 4.388 4.296
13 9.074 6.701 5.739 5.205 4.862 4.620 4.441 4.302 4.191 4.100
14 8.862 6.515 5.564 5.035 4.695 4.456 4.278 4.140 4.030 3.939
15 8.683 6.359 5.417 4.893 4.556 4.318 4.142 4.004 3.895 3.805
16 8.531 6.226 5.292 4.773 4.437 4.202 4.026 3.890 3.780 3.691
17 8.400 6.112 5.185 4.669 4.336 4.102 3.927 3.791 3.682 3.593
18 8.285 6.013 5.092 4.579 4.248 4.015 3.841 3.705 3.597 3.508
19 8.185 5.926 5.010 4.500 4.171 3.939 3.765 3.631 3.523 3.434
20 8.096 5.849 4.938 4.431 4.103 3.871 3.699 3.564 3.457 3.368
21 8.017 5.780 4.874 4.369 4.042 3.812 3.640 3.506 3.398 3.310
22 7.945 5.719 4.817 4.313 3.988 3.758 3.587 3.453 3.346 3.258
23 7.881 5.664 4.765 4.264 3.939 3.710 3.539 3.406 3.299 3.211
24 7.823 5.614 4.718 4.218 3.895 3.667 3.496 3.363 3.256 3.168
25 7.770 5.568 4.675 4.177 3.855 3.627 3.457 3.324 3.217 3.129
26 7.721 5.526 4.637 4.140 3.818 3.591 3.421 3.288 3.182 3.094
27 7.677 5.488 4.601 4.106 3.785 3.558 3.388 3.256 3.149 3.062
28 7.636 5.453 4.568 4.074 3.754 3.528 3.358 3.226 3.120 3.032
29 7.598 5.420 4.538 4.045 3.725 3.499 3.330 3.198 3.092 3.005
30 7.562 5.390 4.510 4.018 3.699 3.473 3.304 3.173 3.067 2.979
40 7.314 5.179 4.313 3.828 3.514 3.291 3.124 2.993 2.888 2.801
60 7.077 4.977 4.126 3.649 3.339 3.119 2.953 2.823 2.718 2.632
120 6.851 4.787 3.949 3.480 3.174 2.956 2.792 2.663 2.559 2.472
inf 6.635 4.605 3.782 3.319 3.017 2.802 2.639 2.511 2.407 2.321
df2 12 15 20 24 30 40 60 120 inf
1 6106.321 6157.285 6208.730 6234.631 6260.649 6286.782 6313.030 6339.391 6365.864
2 99.416 99.433 99.449 99.458 99.466 99.474 99.482 99.491 99.499
3 27.052 26.872 26.690 26.598 26.505 26.411 26.316 26.221 26.125
4 14.374 14.198 14.020 13.929 13.838 13.745 13.652 13.558 13.463
5 9.888 9.722 9.553 9.466 9.379 9.291 9.202 9.112 9.020
6 7.718 7.559 7.396 7.313 7.229 7.143 7.057 6.969 6.880
7 6.469 6.314 6.155 6.074 5.992 5.908 5.824 5.737 5.650
8 5.667 5.515 5.359 5.279 5.198 5.116 5.032 4.946 4.859
9 5.111 4.962 4.808 4.729 4.649 4.567 4.483 4.398 4.311
10 4.706 4.558 4.405 4.327 4.247 4.165 4.082 3.996 3.909
11 4.397 4.251 4.099 4.021 3.941 3.860 3.776 3.690 3.602
12 4.155 4.010 3.858 3.780 3.701 3.619 3.535 3.449 3.361
13 3.815 3.665 3.587 3.507 3.425 3.341 3.255 3.165
14 3.800 3.656 3.505 3.427 3.348 3.266 3.181 3.094 3.004
15 3.666 3.522 3.372 3.294 3.214 3.132 3.047 2.959 2.868
16 3.553 3.409 3.259 3.181 3.101 3.018 2.933 2.845 2.753
17 3.455 3.312 3.162 3.084 3.003 2.920 2.835 2.746 2.653
18 3.371 3.227 3.077 2.999 2.919 2.835 2.749 2.660 2.566
19 3.297 3.153 3.003 2.925 2.844 2.761 2.674 2.584 2.489
20 3.231 3.088 2.938 2.859 2.778 2.695 2.608 2.517 2.421
21 3.173 3.030 2.880 2.801 2.720 2.636 2.548 2.457 2.360
22 3.121 2.978 2.827 2.749 2.667 2.583 2.495 2.403 2.305
23 3.074 2.931 2.781 2.702 2.620 2.535 2.447 2.354 2.256
24 3.032 2.889 2.738 2.659 2.577 2.492 2.403 2.310 2.211
25 2.993 2.850 2.699 2.620 2.538 2.453 2.364 2.270 2.169
26 2.958 2.815 2.664 2.585 2.503 2.417 2.327 2.233 2.131
27 2.926 2.783 2.632 2.552 2.470 2.384 2.294 2.198 2.097
28 2.896 2.753 2.602 2.522 2.440 2.354 2.263 2.167 2.064
29 2.868 2.726 2.574 2.495 2.412 2.325 2.234 2.138 2.034
30 2.843 2.700 2.549 2.469 2.386 2.299 2.208 2.111 2.006
40 2.665 2.522 2.369 2.288 2.203 2.114 2.019 1.917 1.805
60 2.496 2.352 2.198 2.115 2.028 1.936 1.836 1.726 1.601
120 2.336 2.192 2.035 1.950 1.860 1.763 1.656 1.533 1.381
inf 2.185 2.039 1.878 1.791 1.696 1.592 1.473 1.325 1.000

Webinar: Descriptive, Prescriptive, and Predictive Analytics

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  • Descriptive, Prescriptive, and Predictive Analytics
  • DATE: March 4, 2017
  • TIME: 2 PM Eastern / 11 AM Pacific
  • PRICE: Free to all attendees

About the Webinar

Click to Register

Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets.

This webinar will compare and contrast these different data analysis activities and cover:

  • Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis
  • Descriptive Data Analytics – finding patterns
  • Predictive Analytics – creating models of behavior
  • Prescriptive Analytics – acting on insight
  • How the analytic environment differs for each

About the Speakers

Kelle O’Neal

Founder & CEO, First San Francisco Partners

kelleONeal142x142.jpgKelle O’Neal is Founder and CEO of First San Francisco Partners, an Enterprise information Management (EIM) consulting firm. A veteran leader and accomplished advisor in the information management sector, as well as a speaker, author, and trainer, Kelle is passionate about helping organizations apply data intelligence to gain a true competitive advantage.

Kelle’s strong background in customer relationship management, enterprise software, and systems integration uniquely positions her to excel in helping organizations of all sizes and complexities successfully execute on Data Governance, Organizational Change Management, Master Data Management, Data Insights and Analytics, and other EIM initiatives.

Kelle developed her ability to work through organizational complexity, build consensus, and drive results in senior roles at companies that include US-based firms GoldenGate Software, Siebel Systems, and Oracle. She also worked at the executive level in Europe and Asia. Under her leadership of First San Francisco Partners, the firm’s client list has grown significantly over the years, as has its consulting and support teams – and FSFP will celebrate its 10th anniversary in April 2017.

Kelle’s thought leadership and training has been featured on CIO.com, Data Informed, B-Eye Network, Data Management Body of Knowledge, and DATAVERSITY. She received her MBA from the University of Chicago Booth School of Business, and she holds a BA degree from Duke University.

John Ladley

President and Chief Delivery Officer, First San Francisco Partners

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John Ladley is a business technology thought leader and recognized authority in all aspects of Enterprise Information Management (EIM) with 30 years’ experience in planning, project management, improving IT organizations, and successful implementation of information systems. Prior to joining First San Francisco Partners in 2015, John was CEO and President of IMCue Solutions, a consultancy he founded in 2007. At IMCue, John led a team focused on improving a client’s business results through business intelligence, information management, and data governance. John is widely published, co-authoring a well-known data warehouse methodology and a trademarked process for data strategy planning. His books: Making EIM work for Business; A Guide to Understanding Information as an Asset and Data Governance; and How to Design, Deploy, and Sustain an Effective Data Governance Program; are recognized as authoritative sources in the EIM field. John frequently writes and speaks on a variety of technology and EIM topics. His information management experience is balanced between strategic technology planning, project management, and practical application of technology to business problems.

This webinar is brought to you in partnership with:
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