Week 5 Discussion Post Responses Instructions: Review your classmates' posts and respond to at least three of them. Compare the real-world examples identified by two of your classmates with the one you selected and respond substantively. Do you agree or disagree with their analyses of when to use one test over the other? My Discussion Post: Use for comparison The Chi-sq integrity of-fit test measures whether the watched information is supporting the guaranteed populace or not. It tests for the noteworthy contrast between the asserted populace and watched information so see whether we can reason that the example information is essentially unique in relation to the guaranteed populace. It is altogether unique in relation to the t-test and the chi-square test of freedom. The autonomous t-test tests for the mean between two populaces and the chi-square test of autonomy tests for the freedom between two clear cut variable and subsequently the chi-square decency of-fit test is entirely unexpected from the above two. We realize that we need to utilize chi-square decency of-fit test when we have given some data about the populace and we need to test whether the information backings are it or not. Take into consideration the following real world situation: A specialist arranges a study in which a pivotal stride is putting forth members a sustenance reward. It is critical that the three nourishment prizes be equivalent in offer. Consequently, a prestudy was outlined in which members were solicited from which prizes they favored. Of the 60 members, 16 favored cupcakes, 26 favored pieces of candy, and 18 favored dried apricots. Do these scores propose that the diverse nourishment are deferentially favored by individuals all in all? For this situation we need to test whether the extent of favored sustenance for various individuals are equivalent or not consequently a chi-square integrity of-fit test would be generally suitable. Student 1 (Jessica) - Reply to this student The chi-square goodness-of-it test, "asks whether an outcome is different enough from an initial hypothesis that research should conclude that the difference is not likely to have occurred by chance" (Tanner, 2016). It is important to research if the outcome of a certain study just "happened" or if it is truly accurate. The degrees of freedom, which is the total number of the values in that final calculation that have the freedom to very, is computed. "The degrees of freedom for the good-of-fit problem are the number of categories in the problem, minus one" (Tanner, 2016). The anticipated frequency counts is then calculated and based on the chi-square stat as well as the degree of freedom, the P-value is then figured. When using the t-test, the means of two independent groups are compared in order to figure out if there is statistical proof that the means are significantly different. Two unrelated groups are examined and compared to means between two totally unrelated groups that share a continuous variable that is dependent. The chi-square test is looking or "denoting" the statistical method being used and is looking at the goodness-of-fit between the examined values and also those that are assumed or expected. Both tests have two variables an one dependent variable. For example, my son love gummies. He eats them by his least favorite color first and saves his favorite for last. They go in this order from least favorite to favorite; green, yellow, orange, red. Often times, there are more of one color than another. In order to figure out the distribution. I would need to how man of each color is in every individual bag that we get in the box and see if its at random or they all have more of one color. Student 2 (Najette) - Reply to this student The chi-square goodness-of-fit test is a test that is used when you have a categorical variable from a single population. One can see if the sample comes from the population with the claimed distribution. Another way of looking at that is to ask if the frequency distribution fits a specific pattern. This test is used to determine whether sample data are consistent with a hypothesized distribution. The chi-square goodness -of- fit test is applicable when: the sampling method is random sampling, the variable under test is categorical, and the predictable value of the number of sample interpretations in each level of the variable is more than 5. A real world example would be: If one wanted to see what was the distribution in the colors of a skittles candy bag. A standard package of skillets consists of 6 different flavors \"colors\". Suppose that we are curious about the distribution of these colors and ask, do all six colors occur in equal proportion? This is the type of question that can be answered with a goodness of fit test. We could gather random sample skillets from different bags and use a chi-square goodness of fit test to see whether our sample distribution differed significantly from the distribution claimed by the company. Student 3 (Shakiba) - Reply to this student The goodness-of-fit chi square test, also called the 1 x k, similar to one-way ANOVA, accommodates one variable but that variable can have numerous of categories that is greater than one. The test of independence also is a form of goodness of fit chi square test ( r x k) The test asks if an outcome is varied enough from an initial hypothesis that research should conclude that by chance it is unlikely to occur (Tanner 2016). This test measures very small data sets, with their inherent risks to normality and are accepted more in chi square test than t test. This test represents a nominal data equivalent to the one way ANOVA and the factual ANOVA test used for interval and ratio data. It involves just one independent variable, however can be distributed onto any number of categories. The chi-square test of independence requires two independent variables, which is the limit. For example political party affiliation and gender (Tanner, 2016). The difference is the two test is whether the count or frequency with which subjects occur in each category significantly strays from a predetermined hypothesis (Tanner 2016). Trying to determine which test to use would depend on the data set that is included. Each test requires different information on how to analyze for that particular test using various methods. Each test has its own statistical way of analysis. A real life example would be one of my two year olds loves skittles and always says I can taste the rainbow. He loves to sort the skittles according to color/flavor. I ask him how many of each color do you think will be in the pack? There are red, orange, yellow, green, and purple as the colors and flavors. The independent t -test will evaluate if there is more of one color than the others meaning is there a dependent variable. The chi-square test would evaluate which color/flavor is being preferred over the others