QUESTION IMAGE
Question
writing the equation of the trend line
enter values for x and y in the table to plot points in the graph. use the regression calculator to compare the teams’ number of runs with their number of wins.
r=runs; w=wins
the equation of the trend line is
y = -0.15x + 23.21
y = -0.15x - 23.21
y = 0.15x + 23.21
y = 0.15x - 23.21
Step1: Analyze the relationship between R (x) and W (y)
From the data, as R (runs) increases, W (wins) generally increases? Wait, no, let's check the slope. Let's take two points. For example, when R = 808, W = 93; R = 563, W = 55. So as R decreases, W decreases. So the slope should be positive? Wait, no, wait: let's calculate the slope between (808,93) and (563,55). Slope $m=\frac{55 - 93}{563 - 808}=\frac{- 38}{-245}\approx0.155$, which is positive. So the slope is positive, around 0.15. Now check the y - intercept. Let's use point (808,93) and slope 0.15. $y=0.15x + b$. So $93 = 0.15\times808 + b$. $0.15\times808 = 121.2$. Then $b=93 - 121.2=-28.2$, but the options have 23.21. Wait, maybe another point. Let's take (609,69). $y = 0.15x + b$. $69=0.15\times609 + b$. $0.15\times609 = 91.35$. $b=69 - 91.35=-22.35$. Close to -23.21? Wait, no, maybe I mixed x and y. Wait, the x - axis is R (runs), y - axis is W (wins). Wait, maybe the equation is W (y) = 0.15R (x)+23.21? Wait, no, when x is 600, y would be 0.15600 + 23.21=90 + 23.21=113.21, which is too big. Wait, no, maybe the slope is positive, but let's check the options. The options are $y=-0.15x + 23.21$, $y=-0.15x - 23.21$, $y = 0.15x+23.21$, $y = 0.15x - 23.21$. Wait, when x increases, if slope is positive, y increases. Let's check with x = 808: $y = 0.15*808+23.21=121.2 + 23.21 = 144.41$, which is more than 93. No. Wait, maybe I got x and y reversed. Wait, maybe x is W and y is R? No, the problem says "the equation of the trend line" for R (x) and W (y). Wait, maybe the slope is negative? Wait, let's recalculate the slope between (808,93) and (768,94). Wait, 808 to 768 (decrease in x), 93 to 94 (increase in y). So slope is $\frac{94 - 93}{768 - 808}=\frac{1}{-40}=-0.025$, no. Wait, maybe I made a mistake. Wait, let's use regression. Let's calculate the mean of x (R) and y (W). Mean of R: (808 + 768+655+684+637+619+613+609+563)/9. Let's sum R: 808+768=1576; +655=2231; +684=2915; +637=3552; +619=4171; +613=4784; +609=5393; +563=5956. Mean x = 5956/9≈661.78. Mean y (W): (93+94+66+81+86+75+61+69+55)/9. Sum W: 93+94=187; +66=253; +81=334; +86=420; +75=495; +61=556; +69=625; +55=680. Mean y = 680/9≈75.56. Now, the slope $m=\frac{\sum(x_i - \bar{x})(y_i - \bar{y})}{\sum(x_i - \bar{x})^2}$. Let's calculate (x_i - \bar{x}) for R: 808 - 661.78≈146.22; 768 - 661.78≈106.22; 655 - 661.78≈-6.78; 684 - 661.78≈22.22; 637 - 661.78≈-24.78; 619 - 661.78≈-42.78; 613 - 661.78≈-48.78; 609 - 661.78≈-52.78; 563 - 661.78≈-98.78. (y_i - \bar{y}) for W: 93 - 75.56≈17.44; 94 - 75.56≈18.44; 66 - 75.56≈-9.56; 81 - 75.56≈5.44; 86 - 75.56≈10.44; 75 - 75.56≈-0.56; 61 - 75.56≈-14.56; 69 - 75.56≈-6.56; 55 - 75.56≈-20.56. Now calculate numerator: (146.2217.44)+(106.2218.44)+(-6.78 - 9.56)+(22.225.44)+(-24.7810.44)+(-42.78 - 0.56)+(-48.78 - 14.56)+(-52.78 - 6.56)+(-98.78 - 20.56). Let's compute each term: 146.2217.44≈2550; 106.2218.44≈1959; -6.78 - 9.56≈64.8; 22.225.44≈120.9; -24.7810.44≈-258.7; -42.78 - 0.56≈24.0; -48.78 - 14.56≈710.3; -52.78 - 6.56≈346.2; -98.78 - 20.56≈2031. Sum numerator: 2550+1959=4509+64.8=4573.8+120.9=4694.7 - 258.7=4436+24=4460+710.3=5170.3+346.2=5516.5+2031=7547.5. Denominator: sum of (x_i - \bar{x})^2. 146.22²≈21380; 106.22²≈11283; (-6.78)²≈46; 22.22²≈494; (-24.78)²≈614; (-42.78)²≈1820; (-48.78)²≈2380; (-52.78)²≈2786; (-98.78)²≈9758. Sum: 21380+11283=32663+46=32709+494=33203+614=33817+1820=35637+2380=38017+2786=40803+9758=50561. So slope $m=\frac{7547.5}{50561}\approx0.149\approx0.15$. Now, y - intercept $b=\bar{y}-m\bar{x}=75.56 - 0.15\times661.78=75.56 - 99.27=-23…
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$y = 0.15x - 23.21$