In: Statistics and Probability
Sales/SqFt
701.97 209.93 364.92 443.04 399.20 264.64 571.59 642.25 461.45 638.82 484.38 581.09 267.71 572.84 586.48 368.73 351.47 458.24 987.12 357.45 405.77 680.80 368.02 303.95 393.90 562.12 494.88 310.07 373.46 235.81 413.08 625.22 274.30 542.62 178.56 375.33 329.09 297.37 323.17 468.84 352.57 380.34 398.12 312.15 452.16 698.64 367.19 431.93 367.06 400.53 414.36 481.11 538.06 330.48 249.93 291.87 517.40 551.58 386.81 427.50 453.94 512.46 345.27 234.04 348.33 348.47 294.95 361.14 467.71 403.78 245.74 339.94 400.82 326.54
MedAge
34.4 41.2 40.3 35.4 31.5 36.3 35.1 37.6 34.9 34.8 36.2 32.2 30.9 37.7 34.3 32.4 32.1 31.4 30.4 33.9 35.6 35.9 33.6 37.9 40.6 37.7 36.4 40.9 35.0 26.4 37.1 30.3 31.3 29.6 32.9 40.7 29.3 37.3 39.8 33.9 35.0 35.0 35.9 33.0 30.9 38.5 40.5 32.1 34.8 38.0 37.0 34.7 36.4 36.8 32.2 34.8 36.7 33.8 34.2 39.0 34.9 39.3 35.6 36.0 41.1 24.7 40.5 32.9 30.3 36.2 32.4 43.5 41.6 31.4
Following is the scatter plot the given data;
Scatter plot shows a very weak negitive relationship between the variables.
Let Y denote Sales/SqFt and X denote MedAge.
Following is the output of regression analysis generated by excel
SUMMARY OUTPUT | ||||||
Regression Statistics | ||||||
Multiple R | 0.059787205 | |||||
R Square | 0.00357451 | |||||
Adjusted R Square | -0.010264733 | |||||
Standard Error | 137.9420885 | |||||
Observations | 74 | |||||
ANOVA | ||||||
df | SS | MS | F | Significance F | ||
Regression | 1 | 4914.708539 | 4914.708539 | 0.258287967 | 0.6128524 | |
Residual | 72 | 1370017.425 | 19028.01979 | |||
Total | 73 | 1374932.133 | ||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
Intercept | 499.3391617 | 156.3353519 | 3.194025891 | 0.002082334 | 187.6903378 | 810.9879856 |
MedAge, X | -2.245190974 | 4.417750666 | -0.508220392 | 0.6128524 | -11.05181585 | 6.561433903 |
The regression equation is
y' = 499.339 - 2.245x
Following is the scatter plot with regression line: