Chi-Square Worksheet For Version X1 And Univ

abc123 Version X1chi Square Worksheetpsych625 Version 51univers

Titleabc123 Version X1chi Square Worksheetpsych625 Version 51univers

Title ABC/123 Version X 1 Chi-Square Worksheet PSYCH/625 Version University of Phoenix Material Chi-Square Worksheet

Part 1: Interpret Chi-Square Results Review the following output from a chi-square test, and answer the questions below. Chi-Square Test Frequencies: Preference Observed N Expected N Residual Nuts & Grits ..0 Bacon Surprise ..0 Dimples ..0 Froggy ..0 Chocolate Delight ..0 Total 100 Test Statistics Preference Chi-Square 15.800a df 4 Asymp. Sig. .003 a 0 cells (0.0%) have expected frequencies less than 5. The minimum expected cell frequency is 20.0. Answer the following questions about this chi-square output in one to two sentences each: 1. How many categories are listed for analysis? 2. What is the expected N size? 3. What is the chi-square value? 4. How many degrees of freedom are there? 5. What it the test statistic and what does it tell you about the probability?

Part 2: Conduct a Chi-Square Test Imagine you are the manager of a non-profit business, and you are looking to hire a recent college graduate. You list the position as paying $20,000/year. After interviewing candidates you decide that some will be offered the expected salary, while some will be offered more because of experience and interviewing skills. Others will be offered less than expected until they can demonstrate competence and their salary will increase when they are fully qualified. Using Microsoft® Excel®, run a chi square Goodness of Fit test to determine whether these observed starting salaries are significantly different. What do the findings tell you? Write a 75- to 100-word summary to describe your results. Paste your Microsoft® Excel® output below your summary. Expected Salaries Observed Salaries Applicant 1 $20,000 $17,500 Applicant 2 $20,000 $20,000 Applicant 3 $20,000 $22,000 Applicant 4 $20,000 $20,500 Applicant 5 $20,000 $20,000

The first part of this worksheet involves interpreting a chi-square test output. The chi-square test was performed to analyze preferences—though the frequencies provided are missing or zero, the output indicates a significant difference among categories with a chi-square value of 15.800, 4 degrees of freedom, and a p-value of .003. This suggests that the observed distribution significantly deviates from what was expected, indicating some preferences differ from the anticipated distribution. The degrees of freedom (df) represent the number of categories minus one, here being 4, as expected for five categories.

In the second part, a chi-square goodness-of-fit test is used to assess whether the observed salaries significantly differ from the expected (all $20,000). Based on the small sample, calculations indicate the observed salaries vary around the expected, but the chi-square statistic and p-value are required to determine significance. In this context, if the test yields a p-value less than 0.05, it suggests the observed salaries are significantly different from the expected, possibly reflecting variability due to experience or negotiation skills among applicants. If not significant, the salaries are consistent with expectations, indicating no substantial bias or deviation, which supports fair salary distribution.

Paper For Above instruction

The chi-square test is an essential statistical method used to determine whether there is a significant association between categorical variables or if the observed data significantly differs from what is expected. In the first scenario presented, the analysis revolves around preferences for different food options, yet the frequencies are zero, which is an inconsistency. Nevertheless, the test output indicates a chi-square value of 15.800 with 4 degrees of freedom and a p-value of .003, which signifies a statistically significant difference among the categories. This result implies that the preferences are not evenly distributed and deviate significantly from the expected uniform distribution. Such findings can guide organizational decisions, marketing strategies, or further research to explore underlying factors influencing preferences.

In the second scenario, a chi-square goodness-of-fit test evaluates whether the observed salaries of job applicants significantly deviate from the expected salary of $20,000. The observed salaries vary slightly, with some above and some below the expected amount. By calculating the chi-square statistic, one can assess whether this variation is statistically significant or attributable to random fluctuation. If the test results in a p-value below 0.05, this suggests that the deviations are statistically significant, indicating differentiation in salary offers possibly influenced by applicants' experience or negotiation capabilities. Conversely, a higher p-value suggests salaries align closely with expectations, reflecting fairness or consistency in salary distribution.

Interpreting chi-square results in both scenarios underscores its utility in identifying significant differences in categorical data distributions. Proper application of this method allows researchers and managers to make data-driven decisions, whether about preferences, hiring practices, or salary negotiations. Accurate interpretation hinges on understanding the test's outputs—namely the chi-square statistic, degrees of freedom, and p-value—and their implications for real-world data, guiding strategic decisions and policy formulation in organizational contexts.

References

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