1. Let us first determine "\\sum x_i , \\sum x^2_i , \\sum y_i , \\sum y^2_i , \\sum x_iy_i"
"\\sum x_i = 213 \\\\\n\n\\sum x^2_i = 4625 \\\\\n\n\\sum y_i = 3422 \\\\\n\n\\sum y^2_i = 1196690 \\\\\n\n\\sum x_iy_i = 73169"
n represents the sample size and thus n is equal to the number of ordered pairs.
n = 10
We can then determine the covariance using the formula
"s_{xy} = \\frac{\\sum x_iy_i - \\frac{(\\sum x_i)(\\sum y_i)}{n}}{n -1} \\\\\n\n= \\frac{ 73169 - \\frac{(213)(3422)}{10}}{10 -1} \\\\\n\n= 31.15"
Let us next determine the sample variance s2 using the formula:
"s^2 = \\frac{\\sum x^2_i - \\frac{(\\sum x_i)^2}{n}}{n-1} \\\\\n\ns^2_x = \\frac{ 4625 - \\frac{(213)^2}{10}}{10-1} = 9.78 \\\\\n\ns^2_y = \\frac{ 1196690 - \\frac{(3422)^2}{10}}{10-1} = 2568.16"
The sample standard deviation is the square root of the population sample:
"s_x = \\sqrt{s^2_x} = \\sqrt{9.78} = 3.12 \\\\\n\ns_y = \\sqrt{s^2_y} = \\sqrt{2568.16} = 50.67"
We can then determine the correlation coefficient r using the formula:
"r = \\frac{s_{xy}}{s_x s_y} \\\\\n\nr = \\frac{31.15}{3.12 \\times 50.67} = 0.197"
2. The relationship between the variables is weak, positive, linear relationship.
3. Next, we can determine the slope b using the formula:
"b = r\\frac{s_y}{s_x} \\\\\n\nb = 0.197 \\times \\frac{50.67}{3.12} = 3.199"
Next, we can determine the y-intercept a using the formula "a = \\bar{y} -b \\bar{x}" , where the sample mean is the sum of all values divided by the number of values.
"a = \\bar{y} -b \\bar{x} = \\frac{\\sum y_i}{n} -b \\frac{\\sum x_i}{n} \\\\\n\na = \\frac{3422}{10} -3.199 \\frac{213}{10} \\\\\n\n= 342.2 -68.138 = 274.062"
Finally, we then obtain the regression line:
"y = a + bx = 274.062 + 3.199x \\\\\n\ny(22) = 274.062 + 3.199 \\times 22 = 344.44 \\\\\n\ny(23) = 274.062 + 3.199 \\times 23 = 347.64"
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