# AP Statistics Curriculum 2007 Contingency Fit

(Difference between revisions)
 Revision as of 02:12, 3 March 2008 (view source)IvoDinov (Talk | contribs)← Older edit Revision as of 02:13, 3 March 2008 (view source)IvoDinov (Talk | contribs) (→Calculations)Newer edit → Line 30: Line 30: * Test statistics: * Test statistics: - :$\chi_o^2 = \sum_{all-categories}{(O-E)^2/E} \sim \chi_{df=number\_of\_categories - 1}^2$ + :$\chi_o^2 = \sum_{all-categories}{(O-E)^2 \over E} \sim \chi_{(df=number\_of\_categories - 1)}^2$ * P-values and critical values for the [http://socr.stat.ucla.edu/htmls/SOCR_Distributions.html Chi-Square distribution may be easily computed using SOCR Distributions]. * P-values and critical values for the [http://socr.stat.ucla.edu/htmls/SOCR_Distributions.html Chi-Square distribution may be easily computed using SOCR Distributions]. Line 37: Line 37: For the Mendel's pea experiment, we can compute the Chi-square test statistics to be: For the Mendel's pea experiment, we can compute the Chi-square test statistics to be: : $\chi_o^2 = {(787-798)^2 \over 798} + {(277-266)^2 \over 266} = 0.152+0.455=0.607$. : $\chi_o^2 = {(787-798)^2 \over 798} + {(277-266)^2 \over 266} = 0.152+0.455=0.607$. - : p-value=$P(Chi^2 > \chi_o^2)=0.436$ + : p-value=$P(\chi^2 > \chi_o^2)=0.436$ * SOCR Chi-square Caluclations * SOCR Chi-square Caluclations

## General Advance-Placement (AP) Statistics Curriculum - Multinomial Experiments: Chi-Square Goodness-of-Fit

The chi-square test is used to test if a data sample comes from a population with a specific characteristics. The chi-square goodness-of-fit test is applied to binned data (data put into classes or categoris). In most situations, the data histogram or frequency histogram may be obtained and the chi-square test may be applied to these (frequency) values. The chi-square test requires a sufficient sample size in order for the chi-square approximation to be valid.

The Kolmogorov-Smirnov is an alternative to the Chi-square goodness-of-fit test. The chi-square goodness-of-fit test may also be applied to discrete distributions such as the binomial and the Poisson. The Kolmogorov-Smirnov test is restricted to continuous distributions.

## Motivational example

Mendel's pea experiment relates to the transmission of hereditary characteristics from parent organisms to their offspring; it underlies much of genetics. Suppose a tall offspring is the event of interest and that the true proportion of tall peas (based on a 3:1 phenotypic ratio) is 3/4 or p = 0.75. He would like to show that Mendel's data follow this 3:1 phenotypic ratio.

 Observed (O) Expected (E) Tall 787 798 Dwarf 277 266

## Calculations

Suppose there were N = 1064 data measurements with Observed(Tall) = 787 and Observed(Dwarf) = 277. These are the O’s (observed values). To calculate the E’s (expected values), we will take the hypothesized proportions under Ho and multiply them by the total sample size N. Expected(Tall) = (0.75)(1064) = 798 and Expected(Dwarf) = (0.25)(1064) = 266. Quickly check to see if the expected total = N = 1064.

• The hypotheses:
Ho:P(tall) = 0.75 (No effect, follows a 3:1phenotypic ratio)
P(dwarf) = 0.25
Ha: P(tall) ≠ 0.75
P(dwarf) ≠ 0.25
• Test statistics: $\chi_o^2 = \sum_{all-categories}{(O-E)^2 \over E} \sim \chi_{(df=number\_of\_categories - 1)}^2$
• Results:

For the Mendel's pea experiment, we can compute the Chi-square test statistics to be: $\chi_o^2 = {(787-798)^2 \over 798} + {(277-266)^2 \over 266} = 0.152+0.455=0.607$.
p-value= $P(\chi^2 > \chi_o^2)=0.436$
• SOCR Chi-square Caluclations • TBD