Goal Setting and GPACetin (2015) was interested in individual differences in goal setting behavior and performance in college students. To investigate the hypothesized association between the two variables, Cetin collected goal setting and grade point average (GPA) data from 166 college students. Goal setting was measured with a subscale from the Academic Self-Regulated Learning Scale. Students' GPA was measured via self-report. The goal_setting_gpa.csv data set simulates some of the data so that it corresponds to the results in the original study. We can use it to (a) the null hypothesis that there is no association between self-reported goal setting behavior and self-reported academic performance, and (b) construct a regression equation for predicting GPA from goal setting behavior.Cetin, B. (2015). Academic motivation and self-regulated learning in predicting academic achievement in college. Journal of International Education Research, 11(2), 95-106.(1) Download goal_setting_gpa.csv and open it in Jamovi.(2) Make sure the measure type and data type of the variables are correct.(3) From the Regression menu select Correlation Matrix. (4) In the Analysis panel, move gpa into the Variables box first and then goal_setting. We do it in this order because gpa is the variable we want to predict, which means that on a scatterplot, it should appear on the Y axis.(5) From the Correlation Coefficients menu select Pearson.(6) From the Hypothesis menu select Correlated.This specifies the alternative hypothesis, that the two varoables are correlated.(7) From the Additional Options menu select Report significance and N.(8) From the Plot menu select Correlation Matrix.(9) While still in the Analysis panel, from the Regression menu at the top, select Linear Regression.(10) Move gpa to the Dependent Variable box and move goal-setting to the Covariates box.(11) From the Model Fit menu select R, R2, and RMSE (which is the same thing as standard error of estimate).(12) Save the Jamovi file as Ch14_YourLastName.omv and use it to answer the questions on the worksheet.Instructions for submitting this assignment.(1) Locate Ch14_YourLastName.omv, and Ch14 Homework.docx where you saved them on your computer.(2) Click the title of this assignment (Ch14 Homework) to open the Upload Assignment page.(3) In the ASSIGNMENT SUBMISSION section, attach Ch14_YourLastName.omv and the Ch14 Homework file. Click Submit.(4) If you submit this assignment early enough, I might have time to provide feedback that you can use to improve it and possibly improve your grade. So soon after you submit it, return to this assignment to see if I've given you any feedback. Click the assignment title and on the Review Submission History page you'll see the details of your prior submissions. To revise a prior submission, click Start New, download and revise, be sure to save it, and upload it as previously.

Project Week 1 For these project assignments throughout the course you will need to reference the data in the ROI Excel spreadheet. (Attached) In this data set – the ROI data set - for 2 different majors (Business and Engineering), you are given a sample of the 20 best colleges according to ROI (ROI = Return on Investment) and their ‘School Type’, ‘Cost’, ’30-Year ROI’, and ‘Annual % ROI’. For each of the 2 majors create a pie chart using the column ‘School Type’. Comment on your results.For each of the 2 majors create a frequency distribution and histogram using the column ‘Annual % ROI’. Group with starting at 6% (0.06), ending at 11% (0.11), and go by 0.5% (0.005). For the histograms title your charts “Histogram Business Major: Annual % ROI” for Business majors and “Histogram Engineering Major: Annual % ROI” for Engineering Majors. Comment on your results.

Problem 1The Excel workbook Honeywell.xlsx contains the historical stock prices of the Honeywell International Incorporated, an American multinational company that produces a variety of commercial and consumer products, engineering services and aerospace systems for a wide variety of customers, from private consumers to major corporations and governments from 10/15/2017 to 4/15/2018 (courtesy of Yahoo Finance). A.Perform exponential smoothing forecasts on the Honeywell stock prices to forecast the price for 4/16/2018. Use successive values of 0.15, 0,35, 0.55, and 0.75 for the smoothing parameter α. Calculate the MSE of each forecast, Use the MSEs of your forecasts to determine the value of α that has provided the most accurate forecast. Describe qualitatively as to why such a value of α has yielded the most accurate forecast. B.Use your exponential smoothing forecast with 𝜶=𝟎.75, and perform adjusted exponential smoothing forecasts on the Honeywell stock prices to forecast the price for 4/16/2018. Use successive values of 0.15, 0.25, 0.45, and 0.85 for the trend parameter β. Use the MSEs of your forecasts to determine the value of β that has provided the most accurate forecast. Describe qualitatively as to why such a value of β has yielded the most accurate forecast.Problem 2The Helicopter Division of Aerospatiale is studying assembly costs at its Marseilles plant. Past data indicates the following labor hours per helicopter:Helicopter NumberLabor Hours12,00021,40031,23841,14251,07561,02979858957Using these data, apply simple linear regression, and examine the residual plot. What do you conclude? Construct a scatter chart and use the Excel Trendline feature to identify the best type of trendline that maximizes R2.Problem 3Develop a multiple regression model with categorical variables that incorporate seasonality for forecasting sales using the last three years of data in the Excel fileNew Car Sales.Perform regression analysis. Hint: Make sure all your p values are acceptably smallby consecutively removing the months with high p value and repeating the analys