.exe/10_u-IgNslkr7j8P3jH-Iv-3LPXugSyVRXF19F_OAHM6Rx2f6 TnS_nmDkLwHKeBb8coqhT... Outlook w Workday nshe CHome - Canva Penorming a simple linear regression You are a robotics engineer working for a large tech company. For one of your artificially intelligent robots, you are looking to see if the size of the data file used to train the robot is predictive of the time the robot takes to complete a maze. You are going to collect a random sample of 9 attempts at completing the maze. You will note for each attempt the size of the data file (denoted by x, in MB) and the time (denoted by y, in seconds) to complete the maze. You will also note the product x'y of the size of the data file and the completion time for each attempt. (These products are written in the row labeled "xy"). (a) Click on "Take Sample" to see the results for your random sample. Take Sample n: 0 x: 0 y: 0 $:0 4:0 Σ.xy: D Data file size, x (In MB) Completion time, y (in seconds) Compute 181.7 143 P 39.1 75.6 371 320 X 138.9 93.4 52.8 Sample correlation coefficient (r): Slope (b): y-intercept (b): 201 343 S Send data to calculator Based on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table below, nt is the sample size and the symbol Σ xy means the sum of the values xy.) 435 25,983.1 14,506.1 24,192 27,918.9 32,036.2 22,968 35,002.8 32,815.2 17,881.6 138.9 X 252 124.3 264 203.2 88 (b) Write the equation of the least-squares regression line for your data. Then on the scatter plot for your data, graph this regression equation by plotting two points and then drawing the line through them. Round each coordinate to three decimal places. Regression equation: -0 Español 1800
.exe/10_u-IgNslkr7j8P3jH-Iv-3LPXugSyVRXF19F_OAHM6Rx2f6 TnS_nmDkLwHKeBb8coqhT... Outlook w Workday nshe CHome - Canva Penorming a simple linear regression You are a robotics engineer working for a large tech company. For one of your artificially intelligent robots, you are looking to see if the size of the data file used to train the robot is predictive of the time the robot takes to complete a maze. You are going to collect a random sample of 9 attempts at completing the maze. You will note for each attempt the size of the data file (denoted by x, in MB) and the time (denoted by y, in seconds) to complete the maze. You will also note the product x'y of the size of the data file and the completion time for each attempt. (These products are written in the row labeled "xy"). (a) Click on "Take Sample" to see the results for your random sample. Take Sample n: 0 x: 0 y: 0 $:0 4:0 Σ.xy: D Data file size, x (In MB) Completion time, y (in seconds) Compute 181.7 143 P 39.1 75.6 371 320 X 138.9 93.4 52.8 Sample correlation coefficient (r): Slope (b): y-intercept (b): 201 343 S Send data to calculator Based on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table below, nt is the sample size and the symbol Σ xy means the sum of the values xy.) 435 25,983.1 14,506.1 24,192 27,918.9 32,036.2 22,968 35,002.8 32,815.2 17,881.6 138.9 X 252 124.3 264 203.2 88 (b) Write the equation of the least-squares regression line for your data. Then on the scatter plot for your data, graph this regression equation by plotting two points and then drawing the line through them. Round each coordinate to three decimal places. Regression equation: -0 Español 1800
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 91E
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