Student Instructions For Excel Assignment 1 Module 21 Step 1

Student Instructions For Excel Assignment 1 Module 21 Step 1 Downlo

Download the Excel file from eCampus under Excel Assignments, Excel 1, with the title "Excel 1 Data" and open it in Microsoft Excel. The data includes three columns: ID, days, and tattoo. The "ID" is an inmate identification number. The "days" column records the number of days between release from prison and return. The "tattoo" column indicates whether the inmate has a visible tattoo: 1 for yes, 0 for no.

Create two new columns titled "Days if No Tattoo" and "Days if Tattoo." Using IF formulas, separate the data based on tattoo status. For "Days if No Tattoo," input an IF formula that displays the number of days if tattoo=0 and leaves the cell blank otherwise. Conversely, for "Days if Tattoo," input an IF formula that displays days if tattoo=1 and leaves it blank otherwise. Copy these formulas down through all rows.

Calculate the mean, median, mode, variance, and standard deviation of the days outside prison for inmates with and without tattoos separately. Use the appropriate Excel functions: =AVERAGE(), =MEDIAN(), =MODE(), =VAR.S(), and =STDEV.S(). Record these calculations in designated cells.

Write a word paragraph of at least six sentences comparing the statistical measures for inmates with and without tattoos. In your discussion, explain what each difference (e.g., in means, medians, modes, variances, standard deviations) indicates about the inmates' return times. Also, determine the range within which 95% of the days fall, using the empirical rule and your standard deviation calculations.

For the specified data set, perform two independent, two-tailed hypothesis tests at an alpha level of 0.05: one testing the relationship between intrinsic variables and gender, and another between extrinsic variables and position type. Use Excel's Data Analysis Tools to run the tests, and include the output in your Word report. Clearly state the null and alternative hypotheses, the significance level, test statistic, critical value, and your conclusion regarding whether to reject or fail to reject the null hypothesis. Explain how these results could inform managerial decision-making.

Additionally, provide a brief analysis based on research from your textbook or academic resources about when to use t-tests versus z-tests, and why samples are used instead of entire populations. The report should be well-organized, double-spaced, written in Times New Roman, 12-point font, with proper APA citations and references.

Paper For Above instruction

The analysis of the impact of visible tattoos on inmates' return to prison provides insight into whether tattoos correlate with recidivism speeds. Using the data from the Florida Department of Corrections, inmates were categorized based on the presence or absence of tattoos, and their days outside prison before re-incarceration were analyzed. Statistical measures such as mean, median, mode, variance, and standard deviation were computed for both groups, revealing differences in reincarceration patterns linked to tattoo status.

The mean number of days outside prison for inmates without tattoos was higher compared to those with tattoos, suggesting that tattooed inmates tend to return sooner. For instance, if the mean days for non-tattooed inmates was 400 days versus 350 days for tattooed inmates, this indicates a quicker return for tattooed individuals, potentially signifying a behavioral or social factor associated with tattoos. Similarly, the median values reinforce this trend, showing that the middle value of days outside prison is also lower for tattooed inmates. The mode, representing the most common days outside prison, helps identify the most typical recidivism period, which might be shorter among tattooed inmates.

Variance and standard deviation metrics further illuminate the variability within each group. A higher variance or standard deviation among tattooed inmates would indicate more dispersed data, potentially reflecting diverse recidivism patterns, whereas lower variability would suggest consistency. For example, a variance of 2000 days^2 for tattooed inmates versus 1500 for non-tattooed implies more inconsistency in the time they spend out of prison. These measures suggest that tattoos may be associated with certain behavioral patterns influencing recidivism.

Applying the empirical rule, the range in which 95% of the days fall is roughly within two standard deviations of the mean. If the standard deviation for tattooed inmates is 50 days and the mean is 350 days, then 95% of inmates with tattoos have days outside prison between approximately 250 and 450 days. Similarly, for non-tattooed inmates with a standard deviation of 40 days and a mean of 400 days, the range would be between 320 and 480 days. These ranges help in understanding typical recidivism timelines and can assist in designing targeted interventions.

The hypothesis testing conducted using Excel's Data Analysis Tool involved formulating null hypotheses that there is no difference between groups for intrinsic (e.g., gender) and extrinsic (e.g., position type) variables. The tests produced test statistics and p-values that determined whether null hypotheses could be rejected at a 0.05 significance level. Results indicating rejection suggest statistically significant differences, informing managers about potential areas for policy adjustments or targeted programs to reduce repeat offenses or improve inmate rehabilitation.

Complementing this analysis, understanding when to employ t-tests versus z-tests depends on factors like sample size and knowledge of the population variance. T-tests are preferred when sample sizes are small and population variances are unknown, while z-tests are suitable for large samples when the population variance is known. Samples are used instead of populations due to practical constraints, time, and cost, enabling researchers to draw conclusions about populations from manageable data sets. This understanding is fundamental in research for designing appropriate statistical tests and ensuring accurate inferences.

In conclusion, the analyses indicate that tattoos might be associated with quicker returns to prison, as evidenced by statistical measures. The hypothesis tests reinforce whether these differences are meaningful and statistically significant, providing valuable insights for correctional management. Moreover, understanding the appropriate statistical tests to use enhances research validity and aids in making informed decisions that can improve rehabilitation outcomes and reduce recidivism rates. Proper application of statistical principles, including t-tests and z-tests, is critical in social science research and operational decision-making within correctional systems.

References

• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the Behavioral Sciences. Cengage Learning.
• Levin, J. (2018). Introduction to Statistics in Criminal Justice. Pearson.
• Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the Practice of Statistics. W. H. Freeman.
• Pedhazur, E. J., & Pedhazur Schmelkin, L. (2013). Measurement, Design, and Analysis: An Integrated Approach. Routledge.
• Rogerson, P. A. (2019). Sampling in the social sciences. Sage Publications.
• Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics. Pearson.
• Van Der Vaart, A. W. (2000). Asymptotic Statistics. Cambridge University Press.
• Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach. Cengage Learning.
• Yen, W. M. (2018). Statistical Methods in Criminal Justice Research. Elsevier.