Chi-Square Measurement and Misuses

Fundamental to all these considerations are measurement issues. Financial measures, in particular, cost measures, are needed to evaluate alternate strategies on whether to introduce a new product or service line, to determine the appropriate sale price and the consequent market position for the firm’s product.

 Inferential statistics helps us to analyze predictions, inferences, or samples about a specific population from the observations that they make. “With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone” (Trochim, 2006). The goal for this type of data is to review the sample data to be able to infer what the test group may think. It does this by making judgment of the chance that a difference that is observed between the groups is indeed one that can be counted on that could have otherwise happened by coincidence. In order to help solve the issue of generalization, tests of significance are used. For example, a chi-square test or T-test provides a person with the probability that the analysis’ sample results may or may not represent the respective population. In other words, the tests of significance provides us the likelihood of how the analysis results might have happened by chance in a scenario that a relationship may not exist between the variables in regards to the population that is being studied.

Just like in baseball there are large and small businesses. Businesses have to make decisions, decisions that will help the business in the long run. By using analytics business can measure their performance to know where they stand financially and economically. Numbers are very important in a

However, we must perform some analysis on this data to confirm that the results for our class really are significantly different from the average. To do this, we performed a chi-square analysis on our data. Our chi-square value is 14.733, and the degrees of freedom are 6. The resulting p-value falls between 0.05 and 0.02. Therefore, we conclude that the data from our class indeed does not fit the average.

Businesses give an analysis of the profit and sales also other business data to find a trend of popular sales and what is making the most money for the company. An example is if an item is usually sold out in days and making lots of profit and if an item is barely sales businesses would buy more of the popular product and less of the unwanted product.

One application of a Chi-square test is a test for independence. In this case, the null hypothesis is that the occurrence of the outcomes for the two groups is equal. A Fishers exact test is used when you have a small sample Reference Cozby, P. C. (2009). Methods in Behavioral Research (10th ed.). Retrieved from The University of Phoenix eBook Collection database. Week Seven Homework Exercise PSYCH/610 Version 1 PAGE MERGEFORMAT 3 Y, dXiJ(x(I_TS1EZBmU/xYy5g/GMGeD3Vqq8K)fw9

• Provide at least two examples or problem situations in which statistics was used or could be used.

Testing allows the p-value that represents the probability showing that results are unlikely to occur by chance. A p-value of 5% or lower is statistically significant. The p value helps in minimizing Type I or Type II errors in the dataset that can often occur when the p value is more than the significance level. The p value can help in stopping the positive and negative correlation between the dataset to reject the null hypothesis and to determine if there is statistical significance in the hypothesis. Understanding the p value is very important in helping researchers to determine the significance of the effect of their experiment and variables for other researchers

It helps clarify relationsips between variables that cannot be examined by other methods and allows prediction

Researchers routinely choose an ◊-level of 0.05 for testing their hypotheses. What are some experiments for which you might want a lower ◊-level (e.g., 0.01)? What are some situations in which you might accept a higher level (e.g., 0.1)?

While reading through the study it was mentioned that the chi-square goodness-of-fit statistic was used for a dichotomized variable of white race versus non-white race, this was not listed under statistical analysis used for the study. It could have been mentioned that chi-square was used in this scenario to test whether proportions in levels of one variable were significantly different from proportions of a second variable, opioid treatment in neonatal outcome in the white race vs. the non-white race (Gray, Grove, & Sutherland, 2017).

Statistics uses math to determine whether or not an experiment happened by chance. In other words, it determines the probability of your results being by chance or if it is factual data. The mathematical ways of determining probably include looking at mean, standard deviation, mode, and median. This experiment will use statistics to test the probability.

It considers how we can measure the economy and the ways to do so. When evaluating our problem, we must take in mind the measure of what is at stake. We conduct measurements of the economy and all that we need to be able to get the full picture and get the absolute best information to make the best and most accurate decision. From that step, it will give us a clearer picture of whether we should take the ‘wait and see’ step or the ‘at your discretion’ step.

When thinking about the variables the agency is measuring, a chi-square statistic would help the agency measure the association and agreement for nominal and ordinal data in the intervention, which in this case is employment level and treatment condition. If the treatment condition (intervention) is reliable and effective and shows this was what caused the employment level (outcome) of the participants. Therefore, the agency chose the chi-square statistic to show if there was a relationship between the two variables (independent and dependent). The variables have to be categorical to show by attending the treatment the employment level increased for

The instrument used is questionnaire and chi-square is used to test the relationship between the variables, which has proven that there is a