1. Term Project Math 1040-SU13-Intro to Stats SLCC McGrade-Group 4

2. Summary of the Project  Purpose of the study: The purpose of this project is to pull together methods studied in class, by devising a research plan and executing statistical research to answer a question. We will then analyze the results from our collected research data.  Research Question: “For adult men, is hours of watching television per day related to weight?”  Study Design: We used basic data collection by surveying members of a selected population that we could easily contact-males ages 18-50. We used the Simple Random Method of sampling.

3. Graphs for both quantitative variables-HISTOGRAMS Histogram for weightHistogram for hours of TV watched

4. Graphs Continued-BOXPLOTS Boxplot for weight variable Boxplot for hours of TV watched variable

5. Data for both variables  Statistics for our weight variable:  Mean: 195.27 Standard Deviation: 44.40 Five-Number Summary: 110, 161.5, 190, 221.5, 370 Range: 260 Mode: 160, 180, 210 Outliers: 370  Min: 110 Max: 370 Q1: 161.5 Q3: 221.5 Median: 190  Statistics for our hours of TV variable:  Mean: 2.09 Standard Deviation: 1.34 Five-Number Summary: 0, 1, 2, 3, 9 Range: 9 Mode: 2 Outliers: 9  Min: 0 Max: 9 Q1: 1 Q3: 3 Median: 2

6. Correlation Data Statistics for testing the correlation between our two variables: Linear Correlation Coefficient: 0.279 Equation for Line of Regression: y=9.239x+175.958 Using Hours of TV per week as the explanatory variable and weight as the response variable.

7. Correlation continued-SCATTERPLOT

8. Results/Conclusion  Our R value is 0.279 and the critical value for a comparable sample size is about 0.195 (using table provided by website and the max of 100 for df (instead of 102 as needed)). Therefore, the value of our correlation coefficient is above the critical value and we must reject our null hypothesis (there is no relationship) and accept the alternative hypothesis: That there is a statistically significant relationship between hours of TV watched per day and weight.  We were able to indeed answer our original question. For adult males, ages 18-50, weight and amount of time spent watching TV per day are related. There exists a linear relationship between the two variables and as weight increases, so does the amount of time spent watching TV. Therefore, we can infer that men who weigh more tend to watch more TV and thus may not be exercising as much as they should be to be healthy. It is hard to tell if our sample accurately reflects the population. For total world population, obviously our sample size is extremely small to tell. However, if we look at the population of our state or country, I think that this could be the beginning of a trend that we could see. If repeating this project, I think that I would maybe want to conduct further surveys and get a larger sample size. I might also try a different sampling method and group participants in specific groups and see how different sample sizes correlate.

9. Members who participated: Patty Cross-Data/Statistics for the variables Anton Kodra-Group Leader-PowerPoint Presentation, results and Conclusion Laura Vanderhoff-Graphs-Histograms/Boxplots/Scatterplot Brindi Miller-Purpose of the study and Summary Page