You will need the data for “Current Cap Hit” (CCH), goals and total points (goals plus assists) for this assignment and the other columns of the data can be deleted. Trim the data (delete the rows) fo

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You will need the data for “Current Cap Hit” (CCH), goals and total points (goals plus assists) for this assignment and the other columns of the data can be deleted. Trim the data (delete the rows) for forwards who played fewer than 20 games to eliminate minor league call-ups and players injured most of the season. a) Present two separate graphs: (i) salaries (CCH) on the y-axis and goals (G) on the x-axis and (ii) salaries (CCH) on the y-axis and points (PTS) on the x-axis. Use the Excel graph functions to label your axes and to draw a linear trend through the data in each graph. b) Estimate the impact on salary in dollars of (i) an additional goal and (ii) an additional point to the forwards on your team using the Excel regression function or linear trend line. [If you use regression, you must present the results. If you use the trend line, you must explain your calculations.] c) Which factor (goals or points) appears to explain salaries better? Explain your reasoning briefly. [Hint: If you use the regression function, you can obtain the coefficient of determination (R2 ), which tells you what proportion of the variation in the salary series is explained by the regression line (and therefore how much cannot be explained by the regression line alone).] d) A new star emerges on your team with 30 goals and 60 points in 2022-23. [You assume that there are no restrictions on the player’s salary.] Using your regression or trend line results, predict what this player’s salary will be. e) Estimate the impact on salary in percentage terms of (i) goals and (ii) points for forwards on your team. [Hint: You can use the @log function in Excel to create a log(salaries) series in Excel to estimate percentage impact using regression or the linear trendline.]

You will need the data for “Current Cap Hit” (CCH), goals and total points (goals plus assists) for this assignment and the other columns of the data can be deleted. Trim the data (delete the rows) fo
ECON336 2 Labour Economics 20 23 Assignment 1 (due Mon day, September 2 5, 20 23) at Noon This assignment must be completed independently, although you should feel free to discuss the assignment with other students or with the instructor. The instructor can be reached by e -mail , in the office during office hours, or by Zoom in the manner described in the course outline . You will need to download your data set for this assignment into an Excel spreadsheet or other suitable spreadsheet software and then produce graphs and fit regression lines. If you are unfamiliar with Excel (a valuable tool for economics) or another suitable spreadsheet program, you can get a tutorial from other students or friends or online by Googling “Excel primer on graphs” and “Excel primer on regression.” L ate assignments will be subject to a penalty of 20% of the value of the assignment per day . Please make sure that your name and student number are on your assignment when you submit it by email . Submit original files; d o not convert your files to pdf . The e mail address for submission of assignments will be sent to you during the week. Your first assignment is to explain the salaries of forwards for your assigned NHL team for the last NHL season (20 22-23). Your name and team are listed in the table below (page 2). You will need to collect information on player salaries and statistics. The recommended location is the Puck Pedia Player Dashboard at https://puckpedia.com/players where you can choose “player detail s” icon to choose your team from the list and the positions (choose “Centre,” “Left Wing,” and “Right Wing” to capture all of the forward positions) for the 20 22-23 season (the default so you don’t have to choose it). Click “Apply” to obtain the data for forwards on your assigned team, which can be copied and pasted to an Excel sheet. [Hint: Paste using “destination formatting”] You will need the data for “Current Cap Hit” (CCH), goals and total points (goals plus assists) for this assignment and the other columns of the data can be deleted. Trim the data (delete the rows) for forward s who played fewer than 20 games to eliminate minor league call -ups and players injured most of the season. a) Present two separate graphs: (i) salaries (C CH ) on the y -axis and goals (G) on the x -axis and (ii) salaries (CCH) on the y -axis and points (PTS) on the x -axis. Use the Excel graph functions to label your axes and to draw a linear trend through the data in each graph . b) Estimate the impact on salary in dollars of (i) an additional goal and (ii) an additional point to the forwards on your team using the Excel regression function or linear trend line. [If you use regression, you must present the results. If you use the trend line, you must explain your calculations.] c) Which factor (goals or points) appears to explain salaries better? Explain your reasoning briefly. [Hint: If you use the regression function, y ou can obtain the coefficient of determination (R 2), which tells you what proportion of the variation in the salary series is explained by the regression line (and therefore how much cannot be explained by the regression line alone).] d) A new star emerges on your team with 3 0 goals and 60 points in 20 22-23. [You assume that there are no restrictions on the player’s salary.] Using your regression or trend line results, predict what this player’s salary will be. e) Estimate the impact on salary in percentage terms of (i) goals and (ii) points for forwards on your team. [Hint: You can use the @log function in Excel to create a log(salaries) series in Excel to estimate percentage impact using regression o r the linear trendline.] f) Student Name Team Student Name Team Acharya, Harsh Manishkumar Anaheim Ng, Tina Buffalo Adegbosin, Derin Arizona Ngu, Jenny Calgary Adeluoye, Olabisi Olanrewaju Boston Nguyen, Ha Chau Carolina Ahamad, Nadim Buffalo Nkwoji, Amanda S. Chicago Ajayi, Temi Calgary Obaid, Subah Colorado Alam, Ahmed I. Carolina Ogunlade, Ajibola Abdul – Azeez Columbus Anderson, Alexis Chicago Olagbami, Obabi Dallas Bao, Tianqi Colorado Olaitan, Joba Detroit Bijoy, Takbir Hasan Khan Columbus Olukoju, A.J Edmonton Chowdhury, Zahid Dallas Patel, Rahul Rajeshbhai Florida Chowdhury, Noushin Detroit Patel, Ronak B. Las Vegas Eerab, Zanashin Islam Edmonton Peters, Adam Los Angeles Enahoro, Princess M. Florida Rifat, Hasibul Hasan Minnesota Fahim, Abrar Md Las Vegas Rifat, Md Raihan Rahman Montreal Friesen, Corban E. Los Angeles Rifty, Nabid Uddin Nashville Gebrebrhan, Samson f. Minnesota Rivera, Elijah r. New Jersey Gusmanov, Aslan Montreal Rutageruka, Cynthia Doreen NY Islanders Halili, Jeff Edward Y. Nashville Shitu, Halima NY Rangers Hui, Daxiang New Jersey Ssenkungu, Zachary Ottawa Idowu, Zainab NY Islanders Sukonna, Silvia Afrin Philadelphia Umeh, Ikechi NY Rangers Sun, Zengwei Pittsburgh Islam, Adnan Noor Ottawa Tan, Zhuo Min San Jose Kachale, Toderai I. Philadelphia Tesfamichael, Michael A. St. Louis Khan, Md Azmayen Inkiad Pittsburgh Thompson, Cameron T. Tampa Bay Khirman, Allen San Jose Uddin, Ikram Toronto Lam, Ling Yiu St. Louis Vardanyan, Suren Vancouver Le, Tram T. Tampa Bay Wang, Tian Washington Lee, Maya Toronto Xu, Xuezheng Montreal Li, Yujie Vancouver Xu, Yueyang Nashville Maheen, Masrur I. Washington Zhang, Xinlu New Jersey Mannan, Mohammed Faijul Anaheim Zhang, Steven NY Islanders Mehrin, Noor E Arizona Zou, Lei NY Rangers Msenya, Lightness Geofrey Boston

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