You've reached the end of your free preview.

Want to read all 27 pages?

Unformatted text preview: Billy and Brad are brothers who are obsessed with baseball. They want to predict which teams will win based on the team payroll. They assume that better payers earn more money. Therefore they believe that teams with high payrolls are more likely to win. They want to use data to see if their belief is valid. They plan to create a trendline to predict winning percentage based on total payroll. Determine the input and output variables for this situation: Input Variable: Output Variable: payroll of players. win percentage Billy and Brad find the following data for the last few years for Major League Baseball teams in the United States. Create a scatterplot of the data. Then determine which type of trendline is most appropriate for this data. Add the trendline to your scatterplot. Follow this link to the textbook to review steps for creating a trendline in Excel: Record the equation of your trendline here: y = 0.1318x + 69.133 Billy's favorite team is the Oakland A's. In 2018, the Oakland A's payroll was a total of $50.7 million. Brad's favorite team is the New York Yankees. In 2018, the Yankee's payroll was a total of $158.5 million. Use your trendline to predict the winning percentage for each team. Billy's favorite team is the Oakland A's. In 2018, the Oakland A's payroll was a total of $50.7 million. Brad's favorite team is the New York Yankees. In 2018, the Yankee's payroll was a total of $158.5 million. Use your trendline to predict the winning percentage for each team. Oakland A's predicted winning percentage: 78 NY Yankee's predicted winning percentage: 85 Does the scatterplot and trendline support Billy and Brad's assumption that teams with high payrolls are likely to win a greater percentage of their games than teams with lower payrolls? Why or why not? Type your answer here What kind of situation would lead Billy and Brad to re-evaluate their reasoning? Type your answer here s_-_trendlines Team Payroll (in millions of dollars) 216.10 146.66 127.40 119.57 115.16 114.40 110.05 108.16 103.53 102.60 101.81 101.69 94.20 92.44 Insert Scatterplot here Win Percentage 93.33 92.33 91.33 84.67 82.67 94.33 75.67 85.67 83.67 88.67 82.33 80.33 73.00 78.33 f(x) = 0.1318173351x + 69.1332018403 Win Percentage 100 90 80 70 60 50 40 30 20 10 0 0.00 50.00 100.00 150.00 200 30 20 10 88.62 83.43 80.10 73.72 73.22 70.97 70.83 68.22 67.66 67.28 64.74 61.01 56.81 48.52 39.76 27.07 85.33 69.00 78.00 82.67 75.33 82.67 80.00 81.33 85.00 87.67 80.00 68.67 67.67 67.33 74.67 77.67 0 0.00 50.00 100.00 150.00 200 References: 2018403 Win Percentage 100.00 150.00 200.00 250.00 100.00 150.00 200.00 250.00 seanlahman.com/baseball-archive/statistics Juanita sells cars for a car dealership. Her pay is based on the number of cars she sells each month. She wants to take a couple of weeks off for vacation and wants to find the month when car sales will be slowest. Juanita assumes that the number of cars she sells per month will follow the same trend as national car sales. She wants to create a trendline to predict the number of car sales based on the month. She also assumes that this year's sales will be similar to last year's sales. Determine the input and output variables for this situation: Input Variable: Output Variable: number of months cars sold Juanita finds this data for the number of car sales for March through February of last year. Create a scatterplot of the data. Then determine which type of trendline is most appropriate for this data. Add the trendline to your scatterplot. Follow this link to the textbook to review steps for creating a trendline in Excel: Record the equation of your trendline here: y= 355.26x^2-3436.1x+212449 Juanita is thinking of going on vacation in either April or September. She wants to predict how many cars will be sold nationwide in April and September of this year. Because April was month 2 and September was month 7, she found the predicted value for months 2 and 7. predict how many cars will be sold nationwide in April and September of this year. Because April was month 2 and September was month 7, she found the predicted value for months 2 and 7. Predicted cars sold in month 2 (April): Predicted cars sold in month 7 (September): 209493 204441 The vertex of the parabola shows Juanita the month with the lowest car sales. Use the formula x = -b/(2a) to find the x-coordinate of the vertex of the parabola. Use the table on the right to see which month corresponds to the x-coordinate of the vertex. When should Juanita take her vacation? she should take it in August because that was the least amount of car sales. What other things should Juanita consider, other than potential car sales, when deciding the month for her vacation? wheres she vacationing too and the price of tickets on the number of cars she s off for vacation and wants month will follow the same dline to predict the number t this year's sales will be ation: March through February of h type of trendline is most atterplot. tructions_-_trendlines 26x^2-3436.1x+212449 or September. She wants to and September of this year. 7, she found the predicted Month Number 1 2 3 4 5 6 7 8 9 10 11 12 Total Number of Cars Sold 207717 209493 206083 207547 200303 200100 204441 209288 215730 213341 217663 220591 Insert Scatterplot here f(x) = 355.264985015x^2 - 3436.06368 Total Nu 225000 220000 215000 210000 205000 200000 195000 190000 185000 0 2 4 209493 204441 ith the lowest car sales. Use vertex of the parabola. Use s to the x-coordinate of the of car sales. potential car sales, when Month Name March April May June July August September October November December January February Month Number 1 2 3 4 5 6 7 8 9 10 11 12 Scatterplot here 355.264985015x^2 - 3436.0636863137x + 212448.977272727 Total Number of Cars Sold 0 0 0 0 0 0 0 0 0 0 2 4 6 8 10 12 14 References: James lives in Portland Oregon and has a microfiber couch he wants to sell. James plans on using the money to buy a new computer that costs $1150. He currently has $500 saved to purchase a new computer. James needs to determine whether he will get enough money from the sale of his couch to purchase the new computer. What assumptions does James need to make? how much someone is willing to pay for the car. What are James' input and output variables? Input Variable: Output Variable: sofa quality sale price First, estimate the quality of James's couch using a scale of 1-10, 10 being best. Complete a web search of prices of used couches in Portland, Oregon. Use a website like Craigslist to complete the search. Without thinking about the price, record your take on the quality of the couch using a scale of 1-10, 10 being best. After judging the quality of the sofa record the price of the sofa. Once you've gathered data on 15 couches and have filled out the table to the right. Create a scatterplot of the data. Then determine which type of trendline is most appropriate for this data. Add the trendline to your scatterplot. Create a scatterplot of the data. Then determine which type of trendline is most appropriate for this data. Add the trendline to your scatterplot. Follow this link to the textbook to review steps for creating a trendline in Excel: Record the equation of your trendline here: y = 131.24x - 437.68 Use your trendline to predict the sale price of James' sofa, based on it quality. Prediated Sale Price of James' sofa: 200 What sale price should James list in his advertisement? Does it appear he will get enough money from the sale of the couch to pay for the laptop? Looking at the craiglist I found almost the exact couch listed for $200, I don’t think its good enough quality to list it for much more so no it is not enough for the laptop. How did you decide which type of trendline to use? How strong was the pattern in your data? Why does the strengh of the trendline matter? Should the strengh of the trendline affect James' decision? It was very linear as the prices went up so did the quality. It matters because it shows the growth of the quality compared to the price. Yes the line should affects his decision and to realize where his couch fits. It was very linear as the prices went up so did the quality. It matters because it shows the growth of the quality compared to the price. Yes the line should affects his decision and to realize where his couch fits. uch he wants to sell. James costs $1150. He currently eeds to determine whether o purchase the new le of 1-10, 10 being best. ortland, Oregon. Use a thinking about the price, ale of 1-10, 10 being best. f the sofa. Once you've ble to the right. h type of trendline is most atterplot. Insert Scatterplot here Sofa Quality 1 2 3 4 5 6 7 8 Sale Price $50 $75 $100 $120 $200 $250 $300 $350 f(x) = 131.2428571429x - 437.6761904762 Sale $3,000 $2,500 $2,000 $1,500 $3,000 $2,500 9 10 11 12 13 14 15 tructions_-_trendlines 24x - 437.68 ofa, based on it quality. $450 $535 $705 850 $900 $1,700 $2,599 $2,000 $1,500 $1,000 $500 $0 200 ? Does it appear he will get he laptop? $200, I don’t think its good r the laptop. ow strong was the pattern in tter? Should the strengh of tters because it shows the growth his decision and to realize where 0 2 4 6 Advertisement: Super soft suede like material. Large / big 3 person sofa 90" width 36" high 36" depth. Sink into this sofa just once, and you'll know how it got its name. Designed with extradeep seats and three layers of thick padding on the arms and back, it's designed for unparalleled comfort. Scatterplot here 1.2428571429x - 437.6761904762 Sale Price 2 4 6 8 10 12 14 16 ...
View Full Document