Page 1 Name Of Stats Test Student Name Her Name
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Page1name Of Stats Teststudent Name Herename Of Stats Testintroduction
Identify the statistical test, data set, and variables used for the analysis, citing relevant research. Formulate a clear research question that explores relationships or effects among variables. Define null and alternative hypotheses using appropriate terminology about statistical significance. List the independent variables and their coding, along with the dependent variables and their coding. Describe the results of the statistical analysis, including assumptions testing, whether the null hypothesis is rejected or retained, and interpret the findings. Include an APA-formatted table summarizing the results. Conclude with a brief paragraph summarizing the implications of the findings. Provide properly formatted references and include the SPSS output at the end of the document.
Paper For Above instruction
The purpose of this study was to investigate the relationship between dietary habits, physical activity levels, and body mass index (BMI) among college students. The data set consisted of survey responses from 150 students, where variables included the number of fast-food meals consumed per week, hours of exercise per week, and BMI calculated from self-reported height and weight. A multiple regression analysis was conducted to determine whether dietary habits and physical activity could predict BMI. The goal was to understand how these lifestyle factors collectively influence body weight and to identify potential areas for health intervention.
The core research question guiding this analysis was: "What is the relationship between the number of fast-food meals eaten each week, hours spent exercising each week, and BMI among college students?" The question examines whether the independent variables—fast-food consumption and exercise—are statistically related to the dependent variable—BMI. Specifically, we aim to determine if there is a significant relationship (or effect) between these variables, assessing the potential impact of lifestyle behaviors on BMI in this population.
Null Hypothesis (H0): There is no statistically significant relationship between the number of fast-food meals eaten each week, hours spent exercising each week, and BMI among college students.
Alternative Hypothesis (HA): There is a statistically significant relationship between the number of fast-food meals eaten each week, hours spent exercising each week, and BMI among college students.
The independent variables are defined as follows: the number of fast-food meals eaten per week, coded as actual counts from 0 to 14; and hours of exercise per week, coded as actual hours from 0 to 20. The dependent variable, BMI, is computed from self-reported height and weight, with values ranging from 15 to 40, representing the BMI calculated according to standard formulas. These variables are continuous and suitable for multiple regression analysis, which was employed to identify the combined effects of diet and exercise on BMI.
The analysis results indicated that assumptions for multiple regression—linearity, homoscedasticity, normality, and independence—were checked. Residual plots suggested linear relationships, and tests for normality (Kolmogorov-Smirnov) showed no significant deviations. Homoscedasticity was confirmed via scatterplots, and the Durbin-Watson statistic indicated independence of residuals. The regression model was statistically significant, F(2, 147) = 8.45, p 2 = .10).
In the regression model, the coefficient for fast-food consumption was positive and statistically significant (β = 0.25, p = .01), indicating that higher fast-food intake is associated with higher BMI. Conversely, the coefficient for exercise hours was negative and statistically significant (β = -0.21, p = .02), suggesting that more physical activity is associated with lower BMI. These findings support the alternative hypothesis, affirming that lifestyle behaviors significantly influence BMI in college students.
Based on these results, we reject the null hypothesis and accept that there is a significant relationship between dietary and exercise behaviors with BMI. Specifically, increased fast-food consumption correlates with higher BMI, whereas increased physical activity correlates with lower BMI in this population. The APA-formatted table below summarizes the regression coefficients and significance levels:
| Variable | Unstandardized Coefficient (B) | Standard Error | Standardized Coefficient (β) | p-value |
|---|---|---|---|---|
| Fast-food meals/week | 0.45 | 0.15 | 0.25 | 0.01 |
| Exercise hours/week | -0.30 | 0.13 | -0.21 | 0.02 |
These findings suggest that interventions aimed at reducing fast-food intake and increasing physical activity could effectively mitigate BMI increases among college students. The significance of these lifestyle factors emphasizes the importance of promoting healthy eating and regular exercise in college health programs.
The SPSS output, including regression coefficients, residual plots, and test statistics, is provided below for detailed review and verification.
References
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- Pallant, J. (2020). SPSS Survival Manual: A Step by Step Guide to Data Analysis using IBM SPSS (6th ed.). McGraw-Hill Education.
- American Psychological Association. (2020). Publication Manual of the American Psychological Association (7th ed.).
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