In: Statistics and Probability
Is there a correlation between mean course grade and mean attendance in data set 7176? What is the equation of the regression line? What is the best prediction for a mean attendance of 80%?
Class 7176 | ||
Unit Test 3 | Course Grade | Attendance |
238 | 63 | 96 |
208 | 55 | 48 |
258 | 89 | 96 |
264 | 84 | 96 |
324 | 98 | 100 |
0 | 62 | 44 |
0 | 56 | 66 |
274 | 87 | 96 |
274 | 83 | 96 |
0 | 0 | 18 |
179 | 71 | 100 |
268 | 86 | 100 |
241 | 60 | 87 |
0 | 8 | 26 |
278 | 84 | 96 |
307 | 89 | 87 |
294 | 87 | 100 |
175 | 76 | 74 |
129 | 66 | 87 |
284 | 82 | 100 |
297 | 90 | 79 |
255 | 74 | 74 |
268 | 88 | 100 |
215 | 77 | 39 |
146 | 71 | 87 |
304 | 88 | 100 |
Hence we see there is positive trend hence positive correlation between x and y
x-Mx | y-My | (x-Mx)^2 | (x-Mx)(y-My) |
-9.0769 | 15.5385 | 82.3905 | -141.0414 |
-17.0769 | -32.4615 | 291.6213 | 554.3432 |
16.9231 | 15.5385 | 286.3905 | 262.9586 |
11.9231 | 15.5385 | 142.1598 | 185.2663 |
25.9231 | 19.5385 | 672.0059 | 506.497 |
-10.0769 | -36.4615 | 101.5444 | 367.4201 |
-16.0769 | -14.4615 | 258.4675 | 232.497 |
14.9231 | 15.5385 | 222.6982 | 231.8817 |
10.9231 | 15.5385 | 119.3136 | 169.7278 |
-72.0769 | -62.4615 | 5195.083 | 4502.0355 |
-1.0769 | 19.5385 | 1.1598 | -21.0414 |
13.9231 | 19.5385 | 193.8521 | 272.0355 |
-12.0769 | 6.5385 | 145.8521 | -78.9645 |
-64.0769 | -54.4615 | 4105.852 | 3489.7278 |
11.9231 | 15.5385 | 142.1598 | 185.2663 |
16.9231 | 6.5385 | 286.3905 | 110.6509 |
14.9231 | 19.5385 | 222.6982 | 291.574 |
3.9231 | -6.4615 | 15.3905 | -25.3491 |
-6.0769 | 6.5385 | 36.929 | -39.7337 |
9.9231 | 19.5385 | 98.4675 | 193.8817 |
17.9231 | -1.4615 | 321.2367 | -26.1953 |
1.9231 | -6.4615 | 3.6982 | -12.426 |
15.9231 | 19.5385 | 253.5444 | 311.1124 |
4.9231 | -41.4615 | 24.2367 | -204.1183 |
-1.0769 | 6.5385 | 1.1598 | -7.0414 |
15.9231 | 19.5385 | 253.5444 | 311.1124 |
SS: 13477.8462 | SP: 11622.0769 |
Sum of X = 1874
Sum of Y = 2092
Mean X = 72.0769
Mean Y = 80.4615
Sum of squares (SSX) = 13477.8462
Sum of products (SP) = 11622.0769
Regression Equation = ŷ = bX + a
b = SP/SSX =
11622.08/13477.85 = 0.8623
a = MY - bMX = 80.46 -
(0.86*72.08) = 18.3089
ŷ = 0.8623X + 18.3089
For x=80, ŷ = 0.8623*80 + 18.3089=87.2929