PSY 350 Homework #5: ANOVA Spring 2018Computational Section:PSY 350 Homework #5: ANOVA Spring 2018In this assignment, you’ll use R to compare group means in the class survey data using an analysis of variance, and practice writing your results in APA style. To complete this assignment, you’ll need: - A computer with R and RStudio installed. You will need the ‘psych’ package.- The “PSY 350 Class Survey.csv” data file from Canvas (under “Homework Resources”). Download this file and save it in a folder where you can access it easily. - Access to the internet, so that you can reference R Tutorials 9 and 10 (also under “Homework Resources”). You will most certainly also want to reference previous tutorials.- Your class notes on writing style, and your APA manual or Easy Guide. This assignment is optional – you can earn 5 points of extra credit if you complete it on or before Friday, May 11 at midnight.Homework assignments are designed to follow from the R Tutorials, so make sure you’ve completed R Tutorials 9 and 10 before you begin this assignment. We’ll also use some basic principles and ideas from previous homework assignments. Remember that the tutorials include only some of the variables from the survey, whereas your full data set includes all of them. It’s best to practice an analysis within the tutorial first, then move over to RStudio and repeat that exact same analysis to ensure you’re getting thesame results. Then move on and adapt that analysis to new variables to answer the homework questions. This assignment consists of a computational section only. As I’m trying out a new approach to these assignments, rather than turning in your answers in a Word document, please enter each answer in the appropriate space in the Homework #5 assignment in Canvas. You can upload your screen shot to the Canvas assignment just as you always have.Please round all numeric answers to 2 decimal places, or your answer may not be counted as correct even if your computations are accurate .Computational Section:In this assignment, you will use correlation and regression to test whether students who were neater as children and more conscientious are more likely to have higher grade-point averages. Remember that to work with the conscientiousness variable, you need to calculate an average conscientiousness score for each participant using the rowMeans() function across the set of 10 conscientiousness items. 1. First, review your variables: (1 point)a. How many levels does the neatness variable have? b. Is conscientiousness a grouping variable or a continuous variable? 2. What is the correlation (r) between neatness as a child and conscientiousness as a college student? Is this correlation significant? (1 point)3. Test a simple linear regression model predicting GPA from neatness. (1 point total)a. What is the value of the F statistic? b. Is the F significant?c. What is the unstandardized regression coefficient for neatness?4. Now, test a simple linear regression model predicting GPA from conscientiousness. (1 point total)a. What is the value of the F statistic? b. Is the F significant?c. What is the unstandardized regression coefficient for conscientiousness?5. Finally, test a multiple regression model predicting GPA from both conscientiousness and neatness (you do not need to include an interaction). (1 point total)a. Which coefficients are significant now?b. Why?c. Report the standardized regression coefficients for this model (remember that you need to add a function to your regression model to get the standardized coefficients – see Tutorial 10.2!). As before, to show your work, submit a screen shot of your full RStudio window, showing as much of your script as possible, and make sure that your last name is included in the name of your data set (e.g., mine is “gibbons.data”). You will lose 2 points if these elements are not