A university wants to know if there is a difference in weekly study hours between undergraduate students who work part-time jobs and those who do not work. Students report the number of hours they studied during the previous week. The goal of the study is to determine whether weekly study time differs between working and non-working students. Conduct the appropriate statistical test to evaluate whether the two groups differ in study hours.

A university wants to know if a six-week physical activity program improves student stress levels. Students complete a standardized stress questionnaire (multiple item rating scale) one week before the program begins and again immediately after the program ends. The goal of the study is to determine whether stress levels change after participation in the exercise program. Conduct the appropriate statistical test to determine whether there is a significant change in stress levels from before to after the program.

A university wants to know if a four-week mindfulness training program reduces student stress levels. Students complete a standardized stress questionnaire (multiple item rating scale) one week before the program begins and again immediately after the program ends. The goal of the study is to determine whether stress levels change after participation in the mindfulness program. Conduct the appropriate statistical test to determine whether there is a significant change in stress levels from before to after the program.

A university wants to know if a peer tutoring program improves final exam scores. At the end of the semester, the researcher collects final exam scores from two groups of students: those who participated in the peer tutoring program and those who did not participate. The goal of the study is to determine whether there is a difference in average exam performance between students who attended tutoring sessions and those who did not. Conduct the appropriate statistical test to evaluate whether there is a significant difference between the two groups.

Research Scenario B2 A university researcher is interested in examining whether student type (domestic or international) is associated with pet ownership. The researcher surveys a group of students and collects information on each student’s status (domestic or international) and whether they currently own a pet (yes or no). Understanding this relationship can help the university and local housing providers better anticipate student needs, such as pet-friendly housing options and support services. Conduct the correct Chi-Square analysis to test the hypothesis. Start with Research Scenario A2. Download DatasetA2 (provided above). First, read Lecture 4: R Basics. Make sure you have downloaded RStudio and have a basic understanding of the RStudio layout. Next, read the overview sections of Lecture 5: Chi-Square Goodness of Fit. and Lecture 5: Chi-Square Test of Independence. Determine which Chi-Square test should be used to test the research question. Once you have selected the correcy Chi-Square test, read the full instructions line by line. Write the code line by line. Report your interpretation of the code as hashtags inside your RScript file. Save your RScript File. Once you have finished conducting the test, read Lecture 4: R Markdown & Rpubs Read the instructions line by line in order to create an RMarkdown file. Save your RMarkdown file. Next, create an Rpubs html document. Save your Rpubs URL. After you finish conducting the analysis for Research Scenario A2, repeat steps 1 through 6 for Research Scenario B2

A university researcher is interested in understanding students’ beverage preferences to help campus dining services make informed decisions about which drinks to offer and in what quantities. The researcher surveys a group of students and asks each student to select their preferred beverage from four options: tea, coffee, soda, or water. The goal of the study is to determine whether students prefer these beverages equally or whether certain drinks are favored more than others. The results can be used to improve resource allocation, reduce waste, and support health-related initiatives on campus. Conduct the correct Chi-Square analysis to test the hypothesis. Start with Research Scenario A2. Download DatasetA2 (provided above). First, read Lecture 4: R Basics. Make sure you have downloaded RStudio and have a basic understanding of the RStudio layout. Next, read the overview sections of Lecture 5: Chi-Square Goodness of Fit. and Lecture 5: Chi-Square Test of Independence. Determine which Chi-Square test should be used to test the research question. Once you have selected the correcy Chi-Square test, read the full instructions line by line. Write the code line by line. Report your interpretation of the code as hashtags inside your RScript file. Save your RScript File. Once you have finished conducting the test, read Lecture 4: R Markdown & Rpubs Read the instructions line by line in order to create an RMarkdown file. Save your RMarkdown file. Next, create an Rpubs html document. Save your Rpubs URL. After you finish conducting the analysis for Research Scenario A2, repeat steps 1 through 6 for Research Scenario B2.

Overview The purpose of this assignment is to learn how to conduct and report Pearson and Spearman correlations in RStudio. You will answer the two research questions below using the datasets provided below and the code provided in Module 4. Datasets DatasetA.xlsxDownload DatasetA.xlsx DatasetB.xlsxDownload DatasetB.xlsx Research Questions What is the relationship between how much students study (hours) and their exam score (percentage)? What is the relationship between how much a person uses their phone (hours) and how much they sleep (hours)? Lecture 4, Part 1: Correlations in Module 4 Set Up R and Import the Data Install and load all required packages (e.g., readxl, ggpubr) Download the datasets onto your computer. Import the datasets into RStudio. Name each dataset clearly (e.g., DatasetA, DatasetB) Descriptive Statistics For each dataset: Calculate the mean and standard deviation for both variables. Clearly identify the independent variable and dependent variable. Part 3: Check Normality For each dataset: Create histograms to visually inspect skewness and kurtosis of each variable. Conduct Shapiro–Wilk tests for to check the normality of each variable. Decide whether to use: Pearson correlation (both variables are normal), or Spearman correlation (one or both variables are not normal). Part 4: Correlation Analysis For each research question: Run the appropriate correlation test. Determine whether the results are statistically significant. Interpret the direction (positive or negative). Interpret the strength (weak, moderate, or strong). Part 5: Scatterplots For each dataset: Create a scatterplot showing the relationship between the two variables. Include a line of best fit. Add clear x-axis and y-axis labels. Use the plot to assess direction, strength, linearity, and outliers. Part 6: Report the Results For each research question, report the results, including: Means and standard deviations Correlation coefficient (r or ρ) Degrees of freedom p-value Strength and direction of the relationship Lecture 4, Part 3: RMarkdown and Rpubs Convert your R Script file (code file) into an R Markdown file (presentation file) Convert your R Markdown file into an Rpubs URL (which is a sharable html file, accessible to anyone)