Statistics: Chi-squared Introduction: A Chi-square test is used to compare observed data with expected data according to a hypothesis. For instance, if you were crossbreeding 2 heterozygous pea plants, you would expect to see a 3:1 phenotypic ratio in the offspring. In this case, if you were to breed 400 pea plants, you would expect to see 300 plants showing the dominant trait and 100 showing the recessive trait. But what happens if you observe only 260 plants with the dominant trait and 140 plants with the recessive trait? Does this mean something is wrong with Mendelian genetics or is this difference in expected results just due to chance (random sampling error)? These are the questions that can be answered using Chi-square statistics.
The results of this statistical test is used to either reject or accept (fail to reject) the null hypothesis. The null hypothesis states there is no significant difference between the observed results and the expected results. This means that if the null hypothesis is accepted, the difference in observed and expected results was just a matter of chance and so the observed results basically "fit" with what was expected.
Degrees of freedom (df) = number of independent outcomes (Y) being compared less 1
df = Y-1
At the 95% confidence interval we are 95% confident that there is a significant difference between the observed and expected results, therefore rejecting the null hypothesis.
Probability Value - Is the decimal value determined from the X2 table and is the probability of accepting the null hypothesis. A 0.05 probability value equates to a 95% confidence interval.

Problem:
The parent generation is yellowed podded and green podded pea plants. You cross a yellow podded pea plant with a green podded pea plant and you get 100% yellow podded plants in the 1st generation [F1 generation] (Phenotypic ratio 4 yellow : 0 green).
What will be the expected phenotypic ratio when you allow the F1 generation to reproduce? Work a Punnett square.
We observed 1150 yellow and 350 green when actually crossing F1 generation.
Would this be a consistent with what was expected?

Do your work and answer the questions on a separate paper/ document and upload it here.
Why would you run a Chi-squared test?
To determine if our data exactly matches the expected results.
To determine if our data is consistent with expected results.
To determine the expected results.
To compare the phenotypic ratios to the genotypic ratios.
Determine the degrees of Freedom of the phenotypic ratio for this genetic cross.
1
2
3
4
5
Using the data given, what is the result of your Chi-squared analysis? x2= ___.
2.22
2.71
4.36
187.78
448.27
Using the results of your Chi-squared analysis, do we fail to reject or reject the null (i. e. no connection) hypothesis?
Fail to reject the null
Reject the null
It cannot be determined from the data given
If we flipped a coin 100 times. Which of the following is the minimum number necessary to achieve significance?
55
60
63
70
75