RPG 131 APPLIED QUANTITATIVE METHODCHI SQUARE TEST

PREPARED BY:NUR AMIRAH MOHAMAD (124108)NUR AMIRAH BINTI ROSLI (124109)SHARMIN BINTI ZOLKIFLI (126899)NURUL HANISAH BINTI RAMLI (124132)NOR ADILAH BINTI ROSLI (124095)SITI NAJAA BINTI JONIT (124163)

PREPARED FOR:DR. NORMAH ABDUL LATIP

CONTENTI. ACKNOWLEDGEMENT............................................................................................................21.0 INTRODUCTION3-42.0 OBJECTIVE............................................................................................................................43.0 RESEARCH HYPOTHESIS...................................................................................................54.0 LITERATURE REVIEW...................................................................................................5-145.0 EXAMPLE ANALYSIS...................................................................................................15-316.0 RESULT/ CONCLUSIOIN...................................................................................................327.0 REFERENCE.........................................................................................................................33

ACKNOWLEDGEMENTIn performing our assignment, we had to take the help and guideline of some respected persons, who deserve our greatest gratitude. The completion of this assignment gives us much pleasure. We would like to show our gratitude to Dr. Normah Abdul Latip for giving us a good guideline for assignment throughout numerous consultations. We would also like to expand our deepest gratitude to all those who have directly and indirectly guided us in writing this assignment.Many people, especially our classmates and team members itself, have made valuable comment suggestions on this proposal which gave us an inspiration to improve our assignment. We thank all the people for their help directly and indirectly to complete our assignment.

1.0 INTRODUCTIONThe topic of gene interaction includes a sometimes bewildering array of different phenotypic ratios. Although these ratios are easily demonstrated in established systems such as the ones illustrated in this chapter, in an experimental setting a researcher may observe an array of different progeny phenotypes and not initially know the meaning of this ratio. At this stage, a hypothesis is devised to explain the observed ratio. The next step is to determine whether the observed data are compatible with the expectations of the hypothesis.In research generally, it is often necessary to compare experimentally observed numbers of items in several different categories with numbers that are predicted on the basis of some hypothesis. For example, you might want to determine whether the sex ratio in some specific population of insects is 1:1 as expected. If there is a close match, then the hypothesis is upheld, whereas, if there is a poor match, then the hypothesis is rejected. As part of this process, a judgment has to be made about whether the observed numbers are a close enough match to those expected. Very close matches and blatant mismatches generally present no problem in judgment, but inevitably there are gray areas in which the match is not obvious. Genetic analysis often requires the interpretation of numbers in various phenotypic classes. In such cases, a statistical procedure called the 2 (chi-square) test is used to help in making the decision to hold onto or reject the hypothesis.The 2 test is simply a way of quantifying the various deviations expected by chance if a hypothesis is true. For example, consider a simple hypothesis that a certain plant is a heterozygote (monohybrid) of genotype A/a. To test this hypothesis, we would make a testcross to a/a and predict a 1:1 ratio of A/a and a/a in the progeny. Even if the hypothesis is true, we do not always expect an exact 1:1 ratio. We can model this experiment with a barrel full of equal numbers of red and blue marbles. If we blindly removed samples of 100 marbles, on the basis of chance we would expect samples to show small deviations such as 52 red: 48 blue quite commonly and larger deviations such as 60 red:40 blue less commonly. The 2 test allows us to calculate the probability of such chance deviations from expectations if the hypothesis is true. But, if all levels of deviation are expected with different probabilities even if the hypothesis is true, how can we ever reject a hypothesis? It has become a general scientific convention that a probability value of less than 5 percent is to be taken as the criterion for rejecting the hypothesis. The hypothesis might still be true, but we have to make a decision somewhere, and the 5 percent level is the conventional decision line. The logic is that, although results this far from expectations are expected 5 percent of the time even when the hypothesis is true, we will mistakenly reject the hypothesis in only 5% of cases and we are willing to take this chance of error.2.0 OBJECTIVEAfter completing this module, the student will be able to:1.Perform chi-square tests2.Appropriately interpret results of chi-square tests3.Identify the appropriate hypothesis testing procedure based on type of outcome variable and number of samples

3.0 RESEARCH HYPOTHESIS1. Null Hypothesis: There is no difference between the numbers of road cases in two years.2. Research Hypothesis: There is a difference between the numbers of road cases in two years.4.0 LITERATURE REVIEW

Chi square test is non-parametric test and widely used in social science research, particularly in studies that used a nominal scale for measurement. The purpose of this test is to compare the observed frequency in the sample with the expected frequency should exist in theory. For chi-square, the data are frequencies rather than numerical scores.

Achi-square test, also referred to astest(orchi-squared test), is anystatisticalhypothesis testin which thesampling distributionof the test statistic is achi-square distributionwhen thenull hypothesisis true. Also considered a chi-square test is a test in which this isasymptoticallytrue, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by making the sample size large enough.

TYPE OF CHI SQUARE TEST

1. Chi Square Goodness of Fit (One Sample Test)This test allows us to compae a collection of categorical data with some theoretical expected distribution. This test is often used in genetics to compare the results of a cross with the theoretical distribution based on genetic theory. Suppose you preformed a simpe monohybrid cross between two individuals that were heterozygous for the trait of interest.Aa x Aa

The results of your cross are shown in Table 4.Table 4. Results of a monohybrid coss between two heterozygotes for the 'a' gene.AaTotals

A104252

a331548

Totals4357100

Table 4The penotypic ratio 85 of the A type and 15 of the a-type (homozygous recessive). In a monohybrid cross between two heterozygotes, however, we would have predicted a 3:1 ratio of phenotypes. In other words, we would have expected to get 75 A-type and 25 a-type. Are or results different?

Calculate the chi square statistic x2by completing the following steps:1. For eachobservednumber in the table subtract the correspondingexpectednumber (O E).2. Square the difference [ (O E)2].3. Divide the squares obtained for each cell in the table by theexpectednumber for that cell [ (O - E)2/ E ].4. Sum all the values for (O - E)2/ E. This is the chi square statistic.5. For our example, the calculation would be:ObservedExpected(O E)(O E)2(O E)2/ E

A-type8575101001.33

a-type1525101004.0

Total1001005.33

x2= 5.33We now have our chi square statistic (x2= 5.33), our predetermined alpha level of significalnce (0.05), and our degrees of freedom (df =1). Entering the Chi square distribution table with 1 degree of freedom and reading along the row we find our value of x25.33) lies between 3.841 and 5.412. The corresponding probability is 0.05