How are degrees of freedom determined in a Chi-square test for genetics?

In summary, the degrees of freedom in a Chi-square test are calculated as the number of categories minus one. If the expected number of instances in each category is at least five, then the number of observed values (n) is equal to the number of categories. However, in more complex experimental designs, the number of degrees of freedom may be smaller.
  • #1
zmike
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In the Chi-square test, my textbook says that degrees of freedom are the number of independent variables minus one ,df = n - 1

does this mean that that n is equal to the number of observed values from the equation aka the number of times I've added or the number of terms?

sum [(O-E)^2]/E

Is there an instance where it isn't equal to the number of observed values I have?

(there's an example in my book (but no answer) with an experiment with observed values of 2 trials of genetic crosses where observed in
trial 1 was 0.5
trial 2 was 0.3
but both of these values were measuring the same variable which was heterozygosity. The expected value is 0.8. Does this mean the df = 1? or is it 0 since there is only 1 independent variable?)

thank you so much
 
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  • #2
zmike said:
In the Chi-square test, my textbook says that degrees of freedom are the number of independent variables minus one ,df = n - 1

does this mean that that n is equal to the number of observed values from the equation aka the number of times I've added or the number of terms?

The Chi-square test only deals with discrete variables. For example, if you were to sort fruit by color, you might have categories, red, orange, yellow, green, purple, pink, etc. To use the Chi-square test you want to arrange your categories so that each has an expected number of instances of at least five. If you have sixty fruit in your sample, you want each bucket to have an expectation greater than 1/12. If necessary combine categories to achieve this.

The number of degrees of freedom is the number of categories minus one. If you end up with seven colors in the fruit example, the number of degrees of freedom is six. The way to think of this is that the probabilities of the various categories (colors) has to add up to one, so there is one less d.f. than the number of categories.

If you have a more complex experimental design, the degrees of freedom can be smaller. For example, roll two differently colored dice. You have 36 different possible outcomes, counting the red die and white die results as a tuple, such as [3. 2]. If you are willing to roll the dice 180+ times, there will be 25 degrees of freedom. (A six by six grid with 36 possible outcomes, but there are 5 d.f. for the red die, and 5 d.f. for the white die. If you rolled one 36 sided die, there would be 35 d.f.)
 

Related to How are degrees of freedom determined in a Chi-square test for genetics?

1. What is a Chi Square Test in Genetics?

The Chi Square Test in Genetics is a statistical method used to determine if there is a significant association between two categorical variables in a population. It helps researchers to determine whether the observed data fits their expected theoretical ratios or if there is a difference that can be attributed to chance.

2. When is a Chi Square Test used in Genetics?

A Chi Square Test in Genetics is used when researchers want to compare observed data with expected data, such as when studying the inheritance of traits or the relationship between genetic markers and a disease. It is also used to analyze the results of genetic experiments and determine if there is a significant difference between the expected and observed outcomes.

3. How is a Chi Square Test performed in Genetics?

A Chi Square Test in Genetics involves calculating the Chi Square statistic by comparing the observed data with the expected data. This is done by using a formula that takes into account the number of observed and expected data points, as well as the degrees of freedom. The resulting Chi Square value is then compared to a critical value from a Chi Square table to determine if there is a significant difference between the observed and expected data.

4. What are the assumptions of a Chi Square Test in Genetics?

There are three main assumptions for a Chi Square Test in Genetics: 1) the data must be categorical, with each observation belonging to one of two or more categories, 2) the observations must be independent of each other, and 3) the expected values must be greater than 5 for each category. Violating these assumptions can lead to inaccurate results.

5. What are the limitations of a Chi Square Test in Genetics?

One limitation of the Chi Square Test in Genetics is that it can only determine if there is a significant difference between observed and expected data, but it cannot determine the direction or strength of the relationship. Additionally, it assumes that the sample data is representative of the entire population, and it may not be accurate for small sample sizes. It is also not suitable for analyzing continuous data, as it is designed for categorical data.

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