Drosophila is a model organism for genetics experiments because it reproduces quickly and when it reproduces it reproduces in large quantities. Also it is a model organism because so much is already known about it. The Drosophila egg is about half a millimeter long. One day after fertilization the embryo develops and hatches worm like larvae. The larva continuously eats and grows, and moults four times. The fourth time it moults it forms an immobile pupa and turns into the winged form. It hatches in about 4 days and is fertile in 12 hours.

Statistical analysis can be used to determine if there is a significant difference between two of group data sets. One way to do this is to use a Chi square. The Chi square test produces a number which you compare to a statistical Chi square number. Each of these statistical numbers has a significance level. Significance levels show you how likely a result is due to chance. The most common level, which is also used in this lab, is .95 which makes something good enough to be believed. This means that 95% of the time the findings will be true, and 5% of the time they will not. If the Chi square produces a number which is less then the statistical value, you accept your null hypothesis, meaning that there is no significant difference between the data. If the Chi square test produces a number higher than the statistical value then you must refute your null hypothesis, meaning that there is a significant difference in your data. The null hypothesis used is that the pattern for inheritance is autosomal. The expected phenotype ratios for mutant to wild type flies were 1:4. The expected genotype ratios were 1:4 homozygous dominant (ant+/ant+), 2:4 heterozygous (ant+/ant), and 1:4 homozygous recessive (ant/ant). The null hypothesis is that there is no significant difference between this expected data and the data we collected.
II. Results

...Chisquare test for independence of two attributes. Suppose N observations are considered and classified according two characteristics say A and B. We may be interested to test whether the two characteristics are independent. In such a case, we can use Chisquare test for independence of two attributes.
The example considered above testing for independence of success in the English test vis a vis immigrant status is a case fit for...

...2.3. The Chi-Square Distribution
One of the most important special cases of the gamma distribution is the chi-square distribution because the sum of the squares of independent normal random variables with mean zero and standard deviation one has a chi-square distribution. This section collects some basic properties of chi-square random variables, all of which...

...CHI-SQUARE TEST (χ²):
Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. For example, if, according to Mendel's laws, you expected 10 of 20 offspring from a cross to be male and the actual observed number was 8 males, then you might want to know about the "goodness to fit" between the observed and expected. Were the deviations...

...A chi-squared test, also referred to as chi-square test or χw² test, is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. Also considered a chi-squared test is a test in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a...

...Chi-square requires that you use numerical values, not percentages or ratios.
Then calculate 2 using this formula, as shown in Table B.1. Note that we get a value of 2.668 for 2. But what does this number mean? Here's how to interpret the 2 value:
1. Determine degrees of freedom (df). Degrees of freedom can be calculated as the number of categories in the problem minus 1. In our example, there are two categories (green and yellow); therefore, there is I degree...

...Testing statisticalsignificance is an excellent way to identify probably relevance between a total data set mean/sigma and a smaller sample data set mean/sigma, otherwise known as a population mean/sigma and sample data set mean/sigma. This classification of testing is also very useful in proving probable relevance between data samples. Although testing statisticalsignificance is not a 100% fool proof, if testing to the 95%...

...
Copyright 1983 by the American Psychological Association, Inc.
StatisticalSignificance, Power, and Effect Size: A Response to the Reexamination of Reviewer Bias
Bruce E. Wampold
Department of Educational Psychology University of Utah
Michael J. Furlong and Donald R. Atkinson
Graduate School of Education University of California, Santa Barbara
In responding to our study of the influence that statisticalsignificance has on...

...A Study on Gugo and Okra as Homemade Shampoo
A Research Done by:
Francine Faye A. Jumaquio
Majaline Faye A. Tolentino
Romer T. Nepumoceno
Talavera National High School
Talavera Nueva Ecija
A Study on Gugo and Okra as a Homemade Shampoo
Claudine M. Lajara
I-Rosal
Introduction
This study was conducted to determine the effectiveness of a homemade shampoo out of the native Gugo, scientific name Entada phaseuoliodes and Okra, scientific name Abelomoschus Esculentus L....

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