Use the tables to create scatterplots and determine which scatterplot shows a linear association.(1 point)
Responses
x 5 8 2 4 6 12 10 1 3 7
y 550 800 250 500 500 400 650 150 400 750x 5 8 2 4 6 12 10 1 3 7 y 550 800 250 500 500 400 650 150 400 750
x 5 8 2 4 6 12 10 1 3 7
y 550 800 350 450 700 1000 900 250 400 750x 5 8 2 4 6 12 10 1 3 7 y 550 800 350 450 700 1000 900 250 400 750
x 5 8 2 4 6 12 10 1 3 7
y 800 700 350 450 500 1000 900 250 400 750x 5 8 2 4 6 12 10 1 3 7 y 800 700 350 450 500 1000 900 250 400 750
x 5 8 2 4 6 12 10 1 3 7
y 550 800 250 300 700 1000 650 300 100 700
Use the tables to create scatterplots and determine which scatterplot shows a non-linear association.(1 point)
Responses
x 50 46 98 59 78 64 68 53 70 72
y 75 71 90 55 70 100 90 25 40 76x 50 46 98 59 78 64 68 53 70 72 y 75 71 90 55 70 100 90 25 40 76
x 50 46 80 59 78 64 68 53 70 68
y 90 100 57 80 60 75 72 82 70 67x 50 46 80 59 78 64 68 53 70 68 y 90 100 57 80 60 75 72 82 70 67
x 50 46 80 59 78 64 68 53 70 68
y 35 30 75 45 70 50 55 36 62 67x 50 46 80 59 78 64 68 53 70 68 y 35 30 75 45 70 50 55 36 62 67
x 50 46 90 59 78 64 68 53 70 72
y 75 71 100 80 90 82 85 77 87 89
Use the tables to create scatterplots and determine which scatterplot shows a linear association.(1 point)
Responses
x 5.0 4.6 8.5 5.9 7.8 5.3 7.0 7.2
y 12.0 9.0 15.0 11.0 13.0 8.0 14.5 10.0x 5.0 4.6 8.5 5.9 7.8 5.3 7.0 7.2 y 12.0 9.0 15.0 11.0 13.0 8.0 14.5 10.0
x 5.0 4.6 8.5 7.8 6.8 5.3 7.0 7.2
y 12.0 8.5 6.0 10.0 12.0 10.0 12.0 10.0x 5.0 4.6 8.5 7.8 6.8 5.3 7.0 7.2 y 12.0 8.5 6.0 10.0 12.0 10.0 12.0 10.0
x 5.0 4.6 5.9 7.8 6.4 6.8 5.3 7.2
y 9.5 9.0 11.0 13.0 11.5 12.0 10.0 12.5x 5.0 4.6 5.9 7.8 6.4 6.8 5.3 7.2 y 9.5 9.0 11.0 13.0 11.5 12.0 10.0 12.5
x 5.0 4.6 8.5 7.8 6.4 6.8 7.0 7.2
y 3.7 2.7 6.5 2.0 5.6 3.0 7.8 6.7
To determine which scatterplot shows a linear association, we need to create scatterplots for each set of data.
Scatterplot 1:
x = [5, 8, 2, 4, 6, 12, 10, 1, 3, 7]
y = [550, 800, 250, 500, 500, 400, 650, 150, 400, 750]
Scatterplot 2:
x = [5, 8, 2, 4, 6, 12, 10, 1, 3, 7]
y = [550, 800, 350, 450, 700, 1000, 900, 250, 400, 750]
Scatterplot 3:
x = [5, 8, 2, 4, 6, 12, 10, 1, 3, 7]
y = [800, 700, 350, 450, 500, 1000, 900, 250, 400, 750]
Scatterplot 4:
x = [5, 8, 2, 4, 6, 12, 10, 1, 3, 7]
y = [550, 800, 250, 300, 700, 1000, 650, 300, 100, 700]
After plotting the data, we can visually determine that Scatterplot 1 shows a linear association. It has the most clear line of best fit compared to the other scatterplots.
To determine which scatterplot shows a non-linear association, we need to create scatterplots for each set of data.
Scatterplot 1:
x = [50, 46, 98, 59, 78, 64, 68, 53, 70, 72]
y = [75, 71, 90, 55, 70, 100, 90, 25, 40, 76]
Scatterplot 2:
x = [50, 46, 80, 59, 78, 64, 68, 53, 70, 68]
y = [90, 100, 57, 80, 60, 75, 72, 82, 70, 67]
Scatterplot 3:
x = [50, 46, 80, 59, 78, 64, 68, 53, 70, 68]
y = [35, 30, 75, 45, 70, 50, 55, 36, 62, 67]
Scatterplot 4:
x = [50, 46, 90, 59, 78, 64, 68, 53, 70, 72]
y = [75, 71, 100, 80, 90, 82, 85, 77, 87, 89]
After plotting the data, we can visually determine that Scatterplot 3 shows a non-linear association. The data points do not form a straight line, indicating a non-linear relationship between x and y.
To determine which scatterplot shows a linear association, we need to create scatterplots for each set of data.
Scatterplot 1:
x = [5.0, 4.6, 8.5, 5.9, 7.8, 5.3, 7.0, 7.2]
y = [12.0, 9.0, 15.0, 11.0, 13.0, 8.0, 14.5, 10.0]
Scatterplot 2:
x = [5.0, 4.6, 8.5, 7.8, 6.8, 5.3, 7.0, 7.2]
y = [12.0, 8.5, 6.0, 10.0, 12.0, 10.0, 12.0, 10.0]
Scatterplot 3:
x = [5.0, 4.6, 5.9, 7.8, 6.4, 6.8, 5.3, 7.2]
y = [9.5, 9.0, 11.0, 13.0, 11.5, 12.0, 10.0, 12.5]
Scatterplot 4:
x = [5.0, 4.6, 8.5, 7.8, 6.4, 6.8, 7.0, 7.2]
y = [3.7, 2.7, 6.5, 2.0, 5.6, 3.0, 7.8, 6.7]
After plotting the data, we can visually determine that Scatterplot 1 shows a linear association. The data points are forming a relatively clear line, indicating a linear relationship between x and y.