# Linerar Model Project

(Sample) Curve-Fitting Project - Linear Model: Men's 400 Meter Dash
(LR-1)   Purpose: To analyze the winning times for the Olympic Men's 400 Meter Dash using a linear model
The winning times were gathered for the most recent 16 Summer Olympics, post-WWII. (More data was available, back to 1896.)

DATA: Summer Olympics:
Men's 400 Meter Dash
Winning Times |
Year | Time (seconds) |
1948 | 46.20 |
1952 | 45.90 |
1956 | 46.70 |
1960 | 44.90 |
1964 | 45.10 |
1968 | 43.80 |
1972 | 44.66 |
1976 | 44.26 |
1980 | 44.60 |
1984 | 44.27 |
1988 | 43.87 |
1992 | 43.50 |
1996 | 43.49 |
2000 | 43.84 |
2004 | 44.00 |
2008 | 43.75 |
| (LR-2)   SCATTERPLOT:As one would expect, the winning times generally show a downward trend, as stronger competition and training methods result in faster speeds.   The trend is somewhat linear. |

(LR-3)

Line of Best Fit (Regression Line)
y = 0.0431x + 129.84 where x = Year and y = Winning Time (in seconds)
(LR-4) The slope is 0.0431 and is negative since the winning times are generally decreasing.
The slope indicates that in general, the winning time decreases by   0.0431 second a year, and so the winning time decreases at an average rate of 4(0.0431) = 0.1724 second each 4-year Olympic interval.
(LR-5)   Values of r2 and r:
r2 = 0.6991

We know that the slope of the regression line is negative so the correlation coefficient r must be negative.
r=-0.6991=-0.84

Recall that r = 1 corresponds to perfect negative correlation and so r = 0.84 indicates moderately strong negative correlation (relatively close to -1 but not very strong).

(LR-6) Prediction: For the 2012 Summer Olympics, substitute x = 2012 to get y = 0.0431(2012) + 129.84   43.1 seconds.
The regression line predicts a winning time of 43.1 seconds for...
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