Hi! I need your help in forming a meaningful conclusion (at least 2 parags) summarizing the key findings of the study and describing potential areas for further research. You may provide recommendations as well such as more variables for the dataset or highlight factors that affect the suicide rate and or explore the correlation between suicide rate and economic development.

Attached below are screenshots for guide. I have also placed relevant links for more references. Thanks a lot!

Orig Dataset from Kaggle: https://www.kaggle.com/datasets/sandragracenelson/suicide-rate-of-countries-per-every-year?select=suicideratemale.csv

Link to Gsheets: https://docs.google.com/spreadsheets/d/1I4xXaHS7Hm4W0K-nzXd9i6MiHduAhX6LzSafhPtgJHI/edit?usp=sharing

Sample Paper: https://www.dlsu.edu.ph/wp-content/uploads/2019/10/CorrelatesofPoverty_EvidencefromtheCommunity-BasedMonitoringSystemCBMSData.pdf

Univariate Tests of Significance for Suicide Rate (Suicide Rate) Sigma-restricted parameterization Effective hypothesis decomposition Effect SS Degr. of MS F P Country 998371.9 171 5838.432 11945.64 0.000000 Year 7387.5 18 410.415 839.72 0.000000 Gender 0 Country*Year 94605.1 3447 27.446 56.15 0.000000 Country*Gender 305345.3 348 877.429 1795.25 0.000000 Year* Gender 1150.3 36 31.953 65.38 0.000000 Country*Year* Gender 27004.0 6900 3.914 8.01 0.000010 Error 7.8 16 0.4891. INTRODUCTION phenomenon across countries and the different risk factors that may be involved in suicide. It also provides Suicide is a significant public health problem valuable insight into how suicide rates vary between countries, with some high-income nations having low worldwide that is influenced by several factors, suicide rates compared to other countries with similar including genetic predisposition, increase in income levels. Moreover, the data for the report comes unemployment, and environmental factors such as from various sources, including national health agencies depression or traumatic events that can affect anyone at and universities, as well as non-governmental any age. With the annual comprehensive suicide reports, organizations (NGOs)-the WHO compiles these figures authorities from every country can administer into one comprehensive list each year. prevention efforts directed at children, adolescents, and Further, risk factors associated with suicide rates young adults through community programs that offer must reflect to assess and develop projects carefully. support services such as counseling, education, and Although estimates of gender-specific deaths are then intervention training At large, mindful execution of adjusted for under-reporting or over-reporting, inclusion strategies focusing on these high-risk groups would of relevant factors that significantly affect the numbers benefit more communities. generated by WHO should be utilized. Similarly, the gender gap in suicide attempts has also risen sharply over the past two decades, with men committing suicide at a rate nearly double that of Objectives of the Study women. This trend is most pronounced among young males and those from economically disadvantaged The principal objective of this research was to populations. provide insights into the effect sizes and post hoc tests The World Health Organization (WHO) is one of the of suicide rates of countries per year using the World most definitive sources of information worldwide that Health Organization (WHO) data. Specifically, this study provides a valuable tool for researchers and aimed to (1) determine if there is no significant policymakers to understand the magnitude of thisdifference between the suicide rates in different To test for the main effects for each variable, countries; and (2) to find out whether the suicide rate in the treatment sum of squares was used. Listed below are low-income countries has high significance level than in the formulas for testing the main effects of a single variable other countries. Several factors could influence suicide rates, such (Eq. 1) as economic conditions, family structure, social Country Main Effect = > (v, - F) isolation and stress due to unemployment, etc. However, these factors may be more influential in some countries (Eq. 2) than others. As such, a statistical test called ANOVA will Year Main Effect = E (F - F) be utilized. (Eq. 3) In addition, the post hoc analysis is where each data Gender Main Effect = > (v, - F) point will be observed to confirm whether they follow or where: deviate from the mean by more than one standard Y = Mean of Country (1) deviation from its mean value, indicating a significant difference and an outlier data point. The findings of this V = Mean of Year () study can be used as an indicator for the future of a country's economy and as a guide to other countries Y. = Mean of Gender (k) trying to improve their economic development strategies. V = Overall Sample Mean Generated results will also highlight certain groups that are particularly vulnerable to suicide, thus providing them with better assistance and effective intervention programs. To test for the interaction effects, the following formulas were used. 2. METHODOLOGY All Interaction Effects: (Eq. 4) To accomplish the goals of the study, the group utilised data from 183 countries gathered by the WHO. = [ Y - Y ,) The source's variables measured suicide rates based on categorical characteristics of country, year, and gender. Based on these factors, the research group identified a Country and Year Interaction (Eq. 5) factorial ANOVA analysis to be the most appropriate test to gather conclusions from the data. Effects: = > (F, - F) The data was screened on the homogeneity of Country and Gender Interaction (Eq. 6) variances and the normality of distribution before Effects: conducting analysis. The tables and test statistics shown were gathered using statistical software. The = [( F - V) homogeneity of variances for suicide rate was conducted using Levene's test for homogeneity of variances. The p - value is 0.00. We conclude that the Year and Gender Interaction Effects: (Eq. 7) variances are not homogenous. For the normality of distribution assumption, a Sharpio-Wilk analysis was = [ Y - F) conducted. The result is a p - value of 0.00. We can conclude that the distribution of observations for where: suicide rate is not normally distributed. The results from both assumption tests conclude that this analysis is not = Overall Sample Mean entirely accurate to the portrayal of suicide rates describing the country, annual data, and gender Suicide rate (1) observations in Country (f) and variables. Year (f) and Gender (k)Y = Mean of {3131111113 {E}, Year {j} and Calendar {k} if]: g 2 Sample Mean {301111113 1:!) and Year u} and Gland-Er {k} if = Mean of {3131111113 {E}, Year {j} and GenEr {It} (fit "1