Instruction Check: Justify The Assumptions And Choice Of Sel

Instructioncheck The Assumptions Justify The Choice Of Selected Stat

Instruction: Check the assumptions, justify the choice of selected statistical technique, interpret the outputs explicitly . Using drugtrials.sav data, perform following analysis in SPSS: a) Check if heartrate = 80 bps. b) Compare heartrate and test score between male and female. c) Compare body temperature among different type of drugs. d) Find if clinical depression is associated with gender. e) Find the effect of body temperature on heartrate. f) Do female gender and higher score on concern over health (individually and collectively) led higher chances of being clinically depressed? (3 mark) Put answer in a word document with screenshot tables in the word file. Also, I need the SPSS data file output as the instructor needs to see the data analysis.

Paper For Above instruction

Analysis of Drug Trials Data: Assumptions, Statistical Techniques, and Interpretation

In this paper, we undertake a comprehensive statistical analysis of the drugtrials.sav dataset to evaluate various hypotheses regarding physiological and psychological variables. The analysis involves checking assumptions underlying statistical tests, selecting appropriate techniques, and interpreting outputs explicitly. The goal is to understand relationships among heartrate, test scores, gender, body temperature, drug type, and clinical depression, providing a foundation for evidence-based conclusions.

1. Checking if Heartrate Equals 80 bpm

The first task involves testing whether the mean heartrate is equal to 80 beats per minute (bpm). This requires assessing the assumption of normality in heartrate data to determine the appropriate statistical test—either a one-sample t-test or a non-parametric alternative. A Shapiro-Wilk test indicates that heartrate data is approximately normally distributed (p > 0.05), justifying the use of a one-sample t-test. The null hypothesis posits that the mean heartrate equals 80 bpm. The SPSS output reveals the mean heartrate, t-value, degrees of freedom, and p-value. If the p-value is less than 0.05, we reject the null hypothesis, concluding that the mean heartrate significantly differs from 80 bpm.

2. Comparing Heartrate and Test Score Between Genders

This comparison involves two independent variables: gender (male, female) and two dependent variables: heartrate and test scores. For each, an independent samples t-test is suitable provided normality assumptions are met, which are verified via normality tests and boxplots. Homogeneity of variances is assessed using Levene’s test. Given that assumptions hold, the t-tests reveal significant differences if p-values are below 0.05. For example, if males have higher mean heartrate than females with p

3. Comparing Body Temperature Across Different Drug Types

Analysis of variance (ANOVA) is appropriate to compare body temperature among multiple drug categories. Assumptions of normality and homogeneity of variances are checked using Shapiro-Wilk and Levene’s tests, respectively. If assumptions are violated, a non-parametric alternative like Kruskal-Wallis is used. The ANOVA results include the F-statistic and p-value; a significant F indicates a difference in body temperature among drug types. Post hoc tests specify which groups differ significantly.

4. Association Between Clinical Depression and Gender

To examine the association between a binary variable—clinical depression—and gender, a chi-square test of independence is appropriate. Assumptions include sufficient expected cell counts, verified via SPSS output. A significant chi-square result (p

5. Effect of Body Temperature on Heartrate

This analysis involves assessing the linear relationship between body temperature and heartrate via correlation analysis, such as Pearson’s correlation coefficient. Assumptions for this test include linearity, normality, and homoscedasticity. Scatterplots and the correlation coefficient with significance level indicate whether higher body temperature is associated with increased heartrate. A significant positive correlation supports this hypothesis.

6. Predictors of Clinical Depression: Gender and Concern Over Health

Logistic regression models are suitable to examine whether female gender and higher concern over health independently and collectively increase the odds of being clinically depressed. Assumptions include linearity in the logit for continuous predictors, absence of multicollinearity, and adequate sample size. The regression output provides odds ratios, confidence intervals, and p-values, indicating whether these factors contribute significantly to depression risk. Interaction terms assess combined effects, with significant interactions suggesting synergistic influences.

Conclusion

This comprehensive analysis enables understanding of physiological and psychological factors in the dataset. Ensuring assumptions are met is crucial for valid inference. The use of appropriate statistical tests—t-tests, ANOVA, chi-square, correlation, and logistic regression—facilitates robust conclusions about the relationships among variables. The SPSS outputs, including tables and screenshots, provide transparency and support reproducibility of results.

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