Question.4020 - Step 1: Choose your Analysis For each option the goal is for you to conduct a regression analysis. Please review the options and choose one that interests you: Option 1. Does a BA 275 students midterm grade help predict how they will perform on the final exam? Use the BA 275 Past Exam Grades data file to model the midterm and final exam scores for last terms BA 275 students. Option 2. Is there a relationship between number of strike outs (K) and number of homeruns (HR) by player? Use the variable K as the explanatory variable and HR as the response in the MLB Player Stats data. Option 3. What is the relationship between advertised rent in Corvallis and number of bedrooms? Use the variables rent and rooms in the Corvallis Rentals data. Use rent as the response y. Option 4. How does defensive quarterback sacks influence win percentage in the NFL? Use the variable DEF_QB_SACKS which is the total number of quarterback sacks the defense made against the opposing teams for the season and WinPCT which is percent of wins over the season from the NFL dataset. Option 5. Does number of credits a BA 275 student is taking this term influence how many hours they study? Use credits and school_hours from the Student Information Survey. Option 6. What is the relationship between fuel efficiency and carbon emissions for current cars from the EPA? Use COMBCO2 and COMBFE from the EPA dataset. Use COMBCO2 as the response y. Step 2: Get the Data Go to Datasets and Legends tab in Module 0 and download the data that corresponds to your option choice. Identify the columns with the variables that correspond to your option. Pro Tip! Paste those two columns into a new worksheet. Step 3: Watch the Software Tutorial Watch the Week 9 - Excel Tutorials to learn how to create a scatterplot and perform a regression analysis in excel. Step 4: Create a scatterplot Select the two columns and create a scatterplot. The column with the explanatory variable x should be on the left and the column with the response variable y on the right. Make sure your explanatory variable is on the horizontal axis. Add a main title and axis titles. Edit the range of the axes if the data appears “zoomed out” or odd on your plot. Add a trendline and R^2 to the plot. Step 5: Obtain the table of coefficients and residual plot Use your option's question of interest to state the null and alternative hypotheses Use data analysis toolpak to run a regression analysis Be sure to put the y variable first then the x variable. Include column names (labels) when selecting your data so you to see the explanatory variable name in the output! Select labels. Select the residual and residual plot options. Save your table and residual plot for your post. Ways to show your work: Take a screenshot of your excel workbook. Only the work part not the entire screen. Use the math formula button in Canvas. Take a screenshot of handwritten work on a tablet. Neatly hand-write your formulas on paper take a picture with your phone and upload the image within your post. Image needs to .jpeg. (please no words/sentences)
Answer Below:
Week 9 Data analysis State and visualize I selected option 5. The research question is, “Does the number of credits impact the amount of time students dedicate to studying per week?” Scatterplot The relationship between credits taken and study per week is positive. The relationship is moderate since R- square is 0.0598 and it indicates that approximately 5.98% of the variance in study hours is explained by the number of credits. The relationship appears to be linear. LSR and Assessing fit The regression equation is School_hours = 0.066 +0.0966 * credits Residual Plot Testing the relationship Estimating the parameters For each additional credit a student takes, their study hours are expected to increase by approximately 0.97 hours per week, on average. The interval indicates that we are 95% confident that true increase in study hours per additional credit lies between 0.487 hours and 1.446 hours per week. Conclude There is a convincing evidence that a significant relationship between the number of credits a student is taking and their study hours. The t test statistic is 3.97, from 248 degrees of freedom and a p – value < 0.005. Therefore, we reject the null hypothesis.More Articles From Statistics
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