we need to find the line of best fit which is the same with equation of the regression line for the weight in terms of height

A line of best fit is a straight line that is the best approximation of the given set of data.

A more accurate way of finding the line of best fit is the least square method.

let

the height value be the x-values

the weight values be the y-values

y = mx + b, where m is slope, b is y-intercept is the line we are looking for

Step 1: Calculate the mean of the x-values and the mean of the y-values: (x0, y0)

x0 = (60+61+62+62+64+65+65+67+68+68)/10 = 64.2

x0 = = 64.2

y0 = (100+105+110+112+115+120+124+130+132+137)/10 = 118.5

y0 = 118.5

Step 2: Compute the sum of the squares of the x-values.

60^2+61^2+62^2+62^2+64^2+65^2+65^2+67^2+68^2+68^2= 41292

Step 3: Compute the sum of each x-value multiplied by its corresponding y-value.

60*100+61*105+62*110+62*112+64*115+65*120+65*124+67*130+68*132+68*137 = 76391

Step 4: Calculate the slope of the line using the formula:

m = (sum (xy) - x0*y0)/(sumx^2 - (sum x^2)/n)

m = (76391 - 64.2*118.5)/(41292 - 41292/10)

m = 1.85086

Step 5: Compute the y-intercept of the line by using the formula:

b = y0 -m*x0

b = 118.5 - 1.85086*64.2

b = -0.325112

Step 6: Use the slope and the y -intercept to form the equation of the line.

y = 1.85086x - 0.325112

y = 1.851x - 0.325

the equation of the regression line for the weight in terms of height is y = 1.851x - 0.325 where x represent the height and y represents the weight.