archive-edu.com » EDU » C » CSBSJU.EDU

Total: 387

Choose link from "Titles, links and description words view":

Or switch to "Titles and links view".
  • ANOVA: How many groups? Size of largest group?
    VAriance For this to make sense you should have several groups of data at least 3 maximum 26 Number of groups Each group includes a certain number of data items Often all the groups have the same number of items but that is not required What is the size i e the number of items of largest group maximum 99 Size of largest group There is no harm is over

    Original URL path: http://www.physics.csbsju.edu/stats/anova_NGROUP_NMAX_form.html (2016-02-01)
    Open archived version from archive


  • ANOVA: How many groups? Size of largest group?
    data for a ANalysis Of VAriance For this to make sense you should have several groups of data at least 3 maximum 26 Number of groups Each group includes a certain number of data items Often all the groups have the same number of items but that is not required What is the size i e the number of items of largest group maximum 99 Size of largest group There

    Original URL path: http://www.physics.csbsju.edu/stats/anova.Plot_NGROUP_NMAX_form.html (2016-02-01)
    Open archived version from archive

  • ANOVA: How many groups?
    have several groups of data at least 3 maximum 26 Number of groups Each group includes a certain number of data items minimum 3 maximum 1024 Often all the groups have the same number of items but that is not required You will be asked to enter this data into the appropriate box If you have lots of data you may want to copy the data from a different screen

    Original URL path: http://www.physics.csbsju.edu/stats/anova_pnp_NGROUP_form.html (2016-02-01)
    Open archived version from archive

  • Ordinary Least Squares: How many items?
    Least Squares How many items You are about to enter pairs of data so that a line can be fit to the data How many data pairs do you have There is no harm in over estimation blanks will be

    Original URL path: http://www.physics.csbsju.edu/stats/QF_NROW_form.html (2016-02-01)
    Open archived version from archive

  • Multiple Fit Methods: How many items?
    Fit Methods How many items You are about to enter pairs of data so that several lines can be fit to the data How many data pairs do you have There is no harm in over estimation blanks will be

    Original URL path: http://www.physics.csbsju.edu/stats/AF_NROW_form.html (2016-02-01)
    Open archived version from archive

  • Problem: Contingency Tables with Sparsely Populated Cells
    the outcomes or treatments are related to each other and can be legitimately combined to fold rarely populated cases into similar but more commonly populated cases For example the outcomes might be various Likert scale responses Strongly Agree Agree Neither Agree or Disagree if Strongly Agree is rarely selected producing many small expectation cells it may be included with the Agree thus eliminating a row of small expectation cells The size of the table is reduced with the eliminated cell results being combined with other cells In the example of the previous page a 3 3 table was reduced to 2 2 by re binning Use an exact method In the exact method we view the particular contingency table we found by assigning treatments and observing outcomes as embedded in a universe of similar tables that have the same outcome probabilities as our table i e have the same row totals and the same distribution of treatments i e have the same column totals In the re binned example from the previous page we have treatments A control no treatment and B intervention including careful removal of clearly affected branches and outcomes 1 tree death within four years 2 tree alive after four years We found this particular 2 2 table A B 1 7 12 19 2 0 5 5 7 17 24 or more simply 7 12 0 5 The universe of similar tables includes just six tables 7 12 6 13 5 14 4 15 3 16 2 17 0 5 1 4 2 3 3 2 4 1 5 0 In an exact method you calculate the probability of each table and then sum the probability of our table and every other table even more unusual than our table If the total probability of such unusual tables is small we can reject the null hypothesis that the outcome is independent of the treatment Note that the universe of a 2 2 table can be arranged in the linear form shown above so the term more unusual than is well defined without reference to any particular statistical measure of unusualness like X 2 For our particular table there is nothing to the left of it so the universe contains nothing more unusual than our table If our found table was 3 16 4 1 we would need to sum the probability of that table and all tables to the right of it Note that we only sum the probabilities for tables to one side or the other of our found table In this sense our Exact Test is one sided The universe of tables grows rapidly with the size of the contingency table For example the 3 3 discussed on the previous page 5 3 2 2 3 4 0 2 3 inhabits a universe with 756 tables The most likely p 025 table in this universe is 3 3 4 3 3 3 1 2 2 followed with six tables each with p 019 2 4

    Original URL path: http://www.physics.csbsju.edu/stats/contingency.problem.html (2016-02-01)
    Open archived version from archive


  • 1 p 000108 2 1 7 5 4 0 0 3 2 p 000054 2 2 6 0 6 3 5 0 0 p 000013 2 2 6 1 5 3 4 1 0 p 000377 2 2 6 1 6 2 4 0 1 p 000189 2 2 6 2 4 3 3 2 0 p 001887 2 2 6 2 5 2 3 1 1 p 002264 2 2 6 2 6 1 3 0 2 p 000377 2 2 6 3 3 3 2 3 0 p 002516 2 2 6 3 4 2 2 2 1 p 005661 2 2 6 3 5 1 2 1 2 p 002264 2 2 6 3 6 0 2 0 3 p 000126 2 2 6 4 2 3 1 4 0 p 000943 2 2 6 4 3 2 1 3 1 p 003774 2 2 6 4 4 1 1 2 2 p 002830 2 2 6 4 5 0 1 1 3 p 000377 2 2 6 5 1 3 0 5 0 p 000075 2 2 6 5 2 2 0 4 1 p 000566 2 2 6 5 3 1 0 3 2 p 000755 2 2 6 5 4 0 0 2 3 p 000189 2 3 5 0 5 4 5 0 0 p 000038 2 3 5 1 4 4 4 1 0 p 000943 2 3 5 1 5 3 4 0 1 p 000755 2 3 5 2 3 4 3 2 0 p 003774 2 3 5 2 4 3 3 1 1 p 007548 2 3 5 2 5 2 3 0 2 p 002264 2 3 5 3 2 4 2 3 0 p 003774 2 3 5 3 3 3 2 2 1 p 015095 2 3 5 3 4 2 2 1 2 p 011321 2 3 5 3 5 1 2 0 3 p 001510 2 3 5 4 1 4 1 4 0 p 000943 2 3 5 4 2 3 1 3 1 p 007548 2 3 5 4 3 2 1 2 2 p 011321 2 3 5 4 4 1 1 1 3 p 003774 2 3 5 4 5 0 1 0 4 p 000189 2 3 5 5 0 4 0 5 0 p 000038 2 3 5 5 1 3 0 4 1 p 000755 2 3 5 5 2 2 0 3 2 p 002264 2 3 5 5 3 1 0 2 3 p 001510 2 3 5 5 4 0 0 1 4 p 000189 2 4 4 0 4 5 5 0 0 p 000047 2 4 4 1 3 5 4 1 0 p 000943 2 4 4 1 4 4 4 0 1 p 001179 2 4 4 2 2 5 3 2 0 p 002830 2 4 4 2 3 4 3 1 1 p 009435 2 4 4 2 4 3 3 0 2 p 004717 2 4 4 3 1 5 2 3 0 p 001887 2 4 4 3 2 4 2 2 1 p 014152 2 4 4 3 3 3 2 1 2 p 018869 2 4 4 3 4 2 2 0 3 p 004717 2 4 4 4 0 5 1 4 0 p 000236 2 4 4 4 1 4 1 3 1 p 004717 2 4 4 4 2 3 1 2 2 p 014152 2 4 4 4 3 2 1 1 3 p 009435 2 4 4 4 4 1 1 0 4 p 001179 2 4 4 5 0 4 0 4 1 p 000236 2 4 4 5 1 3 0 3 2 p 001887 2 4 4 5 2 2 0 2 3 p 002830 2 4 4 5 3 1 0 1 4 p 000943 2 4 4 5 4 0 0 0 5 p 000047 2 5 3 0 3 6 5 0 0 p 000025 2 5 3 1 2 6 4 1 0 p 000377 2 5 3 1 3 5 4 0 1 p 000755 2 5 3 2 1 6 3 2 0 p 000755 2 5 3 2 2 5 3 1 1 p 004529 2 5 3 2 3 4 3 0 2 p 003774 2 5 3 3 0 6 2 3 0 p 000252 2 5 3 3 1 5 2 2 1 p 004529 2 5 3 3 2 4 2 1 2 p 011321 2 5 3 3 3 3 2 0 3 p 005032 2 5 3 4 0 5 1 3 1 p 000755 2 5 3 4 1 4 1 2 2 p 005661 2 5 3 4 2 3 1 1 3 p 007548 2 5 3 4 3 2 1 0 4 p 001887 2 5 3 5 0 4 0 3 2 p 000377 2 5 3 5 1 3 0 2 3 p 001510 2 5 3 5 2 2 0 1 4 p 001132 2 5 3 5 3 1 0 0 5 p 000151 2 6 2 0 2 7 5 0 0 p 000005 2 6 2 1 1 7 4 1 0 p 000054 2 6 2 1 2 6 4 0 1 p 000189 2 6 2 2 0 7 3 2 0 p 000054 2 6 2 2 1 6 3 1 1 p 000755 2 6 2 2 2 5 3 0 2 p 001132 2 6 2 3 0 6 2 2 1 p 000377 2 6 2 3 1 5 2 1 2 p 002264 2 6 2 3 2 4 2 0 3 p 001887 2 6 2 4 0 5 1 2 2 p 000566 2 6 2 4 1 4 1 1 3 p 001887 2 6 2 4 2 3 1 0 4 p 000943 2 6 2 5 0 4 0 2 3 p 000189 2 6 2 5 1 3 0 1 4 p 000377 2 6 2 5 2 2 0 0 5 p 000113 2 7 1 0 1 8 5 0 0 p 3 9E 07 2 7 1 1 0 8 4 1 0 p 000002 2 7 1 1 1 7 4 0 1 p 000015 2 7 1 2 0 7 3 1 1 p 000031 2 7 1 2 1 6 3 0 2 p 000108 2 7 1 3 0 6 2 1 2 p 000108 2 7 1 3 1 5 2 0 3 p 000216 2 7 1 4 0 5 1 1 3 p 000108 2 7 1 4 1 4 1 0 4 p 000135 2 7 1 5 0 4 0 1 4 p 000027 2 7 1 5 1 3 0 0 5 p 000022 2 8 0 0 0 9 5 0 0 p 5 3E 09 2 8 0 1 0 8 4 0 1 p 2 4E 07 2 8 0 2 0 7 3 0 2 p 000002 2 8 0 3 0 6 2 0 3 p 000004 2 8 0 4 0 5 1 0 4 p 000003 2 8 0 5 0 4 0 0 5 p 6 7E 07 3 0 7 0 7 2 4 1 0 p 000003 3 0 7 0 8 1 4 0 1 p 6 4E 07 3 0 7 1 6 2 3 2 0 p 000036 3 0 7 1 7 1 3 1 1 p 000021 3 0 7 1 8 0 3 0 2 p 000001 3 0 7 2 5 2 2 3 0 p 000108 3 0 7 2 6 1 2 2 1 p 000108 3 0 7 2 7 0 2 1 2 p 000015 3 0 7 3 4 2 1 4 0 p 000090 3 0 7 3 5 1 1 3 1 p 000144 3 0 7 3 6 0 1 2 2 p 000036 3 0 7 4 3 2 0 5 0 p 000018 3 0 7 4 4 1 0 4 1 p 000045 3 0 7 4 5 0 0 3 2 p 000018 3 1 6 0 6 3 4 1 0 p 000042 3 1 6 0 7 2 4 0 1 p 000018 3 1 6 1 5 3 3 2 0 p 000503 3 1 6 1 6 2 3 1 1 p 000503 3 1 6 1 7 1 3 0 2 p 000072 3 1 6 2 4 3 2 3 0 p 001258 3 1 6 2 5 2 2 2 1 p 002264 3 1 6 2 6 1 2 1 2 p 000755 3 1 6 2 7 0 2 0 3 p 000036 3 1 6 3 3 3 1 4 0 p 000839 3 1 6 3 4 2 1 3 1 p 002516 3 1 6 3 5 1 1 2 2 p 001510 3 1 6 3 6 0 1 1 3 p 000168 3 1 6 4 2 3 0 5 0 p 000126 3 1 6 4 3 2 0 4 1 p 000629 3 1 6 4 4 1 0 3 2 p 000629 3 1 6 4 5 0 0 2 3 p 000126 3 2 5 0 5 4 4 1 0 p 000189 3 2 5 0 6 3 4 0 1 p 000126 3 2 5 1 4 4 3 2 0 p 001887 3 2 5 1 5 3 3 1 1 p 003019 3 2 5 1 6 2 3 0 2 p 000755 3 2 5 2 3 4 2 3 0 p 003774 3 2 5 2 4 3 2 2 1 p 011321 3 2 5 2 5 2 2 1 2 p 006793 3 2 5 2 6 1 2 0 3 p 000755 3 2 5 3 2 4 1 4 0 p 001887 3 2 5 3 3 3 1 3 1 p 010063 3 2 5 3 4 2 1 2 2 p 011321 3 2 5 3 5 1 1 1 3 p 003019 3 2 5 3 6 0 1 0 4 p 000126 3 2 5 4 1 4 0 5 0 p 000189 3 2 5 4 2 3 0 4 1 p 001887 3 2 5 4 3 2 0 3 2 p 003774 3 2 5 4 4 1 0 2 3 p 001887 3 2 5 4 5 0 0 1 4 p 000189 3 3 4 0 4 5 4 1 0 p 000314 3 3 4 0 5 4 4 0 1 p 000314 3 3 4 1 3 5 3 2 0 p 002516 3 3 4 1 4 4 3 1 1 p 006290 3 3 4 1 5 3 3 0 2 p 002516 3 3 4 2 2 5 2 3 0 p 003774 3 3 4 2 3 4 2 2 1 p 018869 3 3 4 2 4 3 2 1 2 p 018869 3 3 4 2 5 2 2 0 3 p 003774 3 3 4 3 1 5 1 4 0 p 001258 3 3 4 3 2 4 1 3 1 p 012579 3 3 4 3 3 3 1 2 2 p 025159 3 3 4 3 4 2 1 1 3 p 012579 3 3 4 3 5 1 1 0 4 p 001258 3 3 4 4 0 5 0 5 0 p 000063 3 3 4 4 1 4 0 4 1 p 001572 3 3 4 4 2 3 0 3 2 p 006290 3 3 4 4 3 2 0 2 3 p 006290 3 3 4 4 4 1 0 1 4 p 001572 3 3 4 4 5 0 0 0 5 p 000063 3 4 3 0 3 6 4 1 0 p 000210 3 4 3 0 4 5 4 0 1 p 000314 3 4 3 1 2 6 3 2 0 p 001258 3 4 3 1 3 5 3 1 1 p 005032 3 4 3 1 4 4 3 0 2 p 003145 3 4 3 2 1 6 2 3 0 p 001258 3 4 3 2 2 5 2 2 1 p 011321 3 4 3 2 3 4 2 1 2 p 018869 3 4 3 2 4 3 2 0 3 p 006290 3 4 3 3 0 6 1 4 0 p 000210 3 4 3 3 1 5 1 3 1 p 005032 3 4 3 3 2 4 1 2 2 p 018869 3 4 3 3 3 3 1 1 3 p 016772 3 4 3 3 4 2 1 0 4 p 003145 3 4 3 4 0 5 0 4 1 p 000314 3 4 3 4 1 4 0 3 2 p 003145 3 4 3 4 2 3 0 2 3 p 006290 3 4 3 4 3 2 0 1 4 p 003145 3 4 3 4 4 1 0 0 5 p 000314 3 5 2 0 2 7 4 1 0 p 000054 3 5 2 0 3 6 4 0 1 p 000126 3 5 2 1 1 7 3 2 0 p 000216 3 5 2 1 2 6 3 1 1 p 001510 3 5 2 1 3 5 3 0 2 p 001510 3 5 2 2 0 7 2 3 0 p 000108 3 5 2 2 1 6 2 2 1 p 002264 3 5 2 2 2 5 2 1 2 p 006793 3 5 2 2 3 4 2 0 3 p 003774 3 5 2 3 0 6 1 3 1 p 000503 3 5 2 3 1 5 1 2 2 p 004529 3 5 2 3 2 4 1 1 3 p 007548 3 5 2 3 3 3 1 0 4 p 002516 3 5 2 4 0 5 0 3 2 p 000377 3 5 2 4 1 4 0 2 3 p 001887 3 5 2 4 2 3 0 1 4 p 001887 3 5 2 4 3 2 0 0 5 p 000377 3 6 1 0 1 8 4 1 0 p 000004 3 6 1 0 2 7 4 0 1 p 000018 3 6 1 1 0 8 3 2 0 p 000009 3 6 1 1 1 7 3 1 1 p 000144 3 6 1 1 2 6 3 0 2 p 000252 3 6 1 2 0 7 2 2 1 p 000108 3 6 1 2 1 6 2 1 2 p 000755 3 6 1 2 2 5 2 0 3 p 000755 3 6 1 3 0 6 1 2 2 p 000252 3 6 1 3 1 5 1 1 3 p 001006 3 6 1 3 2 4 1 0 4 p 000629 3 6 1 4 0 5 0 2 3 p 000126 3 6 1 4 1 4 0 1 4 p 000314 3 6 1 4 2 3 0 0 5 p 000126 3 7 0 0 0 9 4 1 0 p 7 1E 08 3 7 0 0 1 8 4 0 1 p 6 4E 07 3 7 0 1 0 8 3 1 1 p 000003 3 7 0 1 1 7 3 0 2 p 000010 3 7 0 2 0 7 2 1 2 p 000015 3 7 0 2 1 6 2 0 3 p 000036 3 7 0 3 0 6 1 1 3 p 000024 3 7 0 3 1 5 1 0 4 p 000036 3 7 0 4 0 5 0 1 4 p 000009 3 7 0 4 1 4 0 0 5 p 000009 4 0 6 0 6 3 3 2 0 p 000021 4 0 6 0 7 2 3 1 1 p 000018 4 0 6 0 8 1 3 0 2 p 000002 4 0 6 1 5 3 2 3 0 p 000126 4 0 6 1 6 2 2 2 1 p 000189 4 0 6 1 7 1 2 1 2 p 000054 4 0 6 1 8 0 2 0 3 p 000002 4 0 6 2 4 3 1 4 0 p 000157 4 0 6 2 5 2 1 3 1 p 000377 4 0 6 2 6 1 1 2 2 p 000189 4 0 6 2 7 0 1 1 3 p 000018 4 0 6 3 3 3 0 5 0 p 000042 4 0 6 3 4 2 0 4 1 p 000157 4 0 6 3 5 1 0 3 2 p 000126 4 0 6 3 6 0 0 2 3 p 000021 4 1 5 0 5 4 3 2 0 p 000189 4 1 5 0 6 3 3 1 1 p 000252 4 1 5 0 7 2 3 0 2 p 000054 4 1 5 1 4 4 2 3 0 p 000943 4 1 5 1 5 3 2 2 1 p 002264 4 1 5 1 6 2 2 1 2 p 001132 4 1 5 1 7 1 2 0 3 p 000108 4 1 5 2 3 4 1 4 0 p 000943 4 1 5 2 4 3 1 3 1 p 003774 4 1 5 2 5 2 1 2 2 p 003396 4 1 5 2 6 1 1 1 3 p 000755 4 1 5 2 7 0 1 0 4 p 000027 4 1 5 3 2 4 0 5 0 p 000189 4 1 5 3 3 3 0 4 1 p 001258 4 1 5 3 4 2 0 3 2 p 001887 4 1 5 3 5 1 0 2 3 p 000755 4 1 5 3 6 0 0 1 4 p 000063 4 2

    Original URL path: http://www.physics.csbsju.edu/stats/3x3_count_tables.w.p.txt (2016-02-01)
    Open archived version from archive

  • Exact Test: Some Details
    by c in our example c 5 The X 2 test but not the exact test makes use of an expected contingency table Whereas the actual contingency table cells must be integers the expected contingency table cells are real numbers e ij r i N c j where r i is the total of the i th row c j is the total of the j th column and N is the grand total of the table For example r 2 x 21 x 22 x 23 x 2c where c N B c without a subscript is the total number of columns In our example r 2 210 Similarly we can define column totals c 4 x 14 x 24 x 34 x r4 In our example c 4 15 and the sum includes just two terms since r 2 In our example the expect table is A B C D E 1 16 6 9 5 152 7 1 4 8 2 18 4 10 5 168 7 9 5 8 X 2 is then defined by X 2 x ij e ij 2 e ij As described on another page if any e ij are small say less than 5 we have problems and another approach may be needed In this example we have one expected cell smaller than 5 However by the Cochran conditions this table can still be analyzed with X 2 One option is the exact method In the exact method we view the particular contingency table x ij as embedded in a universe of similar tables that have the same outcome probabilities as our table i e have the same row totals and the same distribution of treatments i e have the same column totals The probability of each table in this universe

    Original URL path: http://www.physics.csbsju.edu/stats/exact.details.html (2016-02-01)
    Open archived version from archive



  •