In: Statistics and Probability
Evaluate your model using all factors outlined in the text. Assumptions, conditions, fit, outliers, etc.
Then explain your answer in words. Create a linear regression model that has 2 or 3 independent variables. Your choice of models…your choice of variables.
A1 Tim is a wedding planner. Looking at the data he has accessed, he is trying to simplify a model for how much a wedding will cost. Use the data to create a model with 2 or 3 Independent Variables, to predict Wedding Cost (dependent variable.) Evaluate your model using all factors outlined in the text.
| Couple's Income | Bride's age | Payor | Wedding cost | Attendance | Value Rating | var7 | 
| 130000 | 22 | Bride's Parents | 60700 | 300 | 3 | |
| 157000 | 23 | Bride's Parents | 52000 | 350 | 1 | |
| 98000 | 27 | Bride & Groom | 47000 | 150 | 3 | |
| 72000 | 29 | Bride & Groom | 42000 | 200 | 5 | |
| 86000 | 25 | Bride's Parents | 34000 | 250 | 3 | |
| 90000 | 28 | Bride & Groom | 30500 | 150 | 3 | |
| 43000 | 19 | Bride & Groom | 30000 | 250 | 3 | |
| 100000 | 30 | Bride & Groom | 30000 | 300 | 3 | |
| 65000 | 24 | Bride's Parents | 28000 | 250 | 3 | |
| 78000 | 35 | Bride & Groom | 26000 | 200 | 5 | |
| 73000 | 25 | Bride's Parents | 25000 | 150 | 5 | |
| 75000 | 27 | Bride & Groom | 24000 | 200 | 5 | |
| 64000 | 25 | Bride's Parents | 24000 | 200 | 1 | |
| 67000 | 27 | Groom's Parents | 22000 | 200 | 5 | |
| 75000 | 25 | Bride's Parents | 20000 | 200 | 5 | |
| 67000 | 30 | Bride's Parents | 20000 | 200 | 5 | |
| 62000 | 21 | Groom's Parents | 20000 | 100 | 1 | |
| 75000 | 19 | Bride's Parents | 19000 | 150 | 3 | |
| 52000 | 23 | Bride's Parents | 19000 | 200 | 1 | |
| 64000 | 22 | Bride's Parents | 18000 | 150 | 1 | |
| 55000 | 28 | Bride's Parents | 16000 | 100 | 5 | |
| 53000 | 31 | Bride & Groom | 14000 | 100 | 1 | |
| 62000 | 24 | Bride's Parents | 13000 | 150 | 1 | |
| 40000 | 26 | Bride's Parents | 7000 | 50 | 3 | |
| 45000 | 32 | Bride & Groom | 5000 | 50 | 5 | 
The regression has five key assumptions:
The assumptions are met.
The regression output is:
| R² | 0.734 | ||||
| Adjusted R² | 0.710 | ||||
| R | 0.857 | ||||
| Std. Error | 7126.932 | ||||
| n | 25 | ||||
| k | 2 | ||||
| Dep. Var. | Wedding cost | ||||
| ANOVA table | |||||
| Source | SS | df | MS | F | p-value | 
| Regression | 3,08,93,12,887.3528 | 2 | 1,54,46,56,443.6764 | 30.41 | 4.65E-07 | 
| Residual | 1,11,74,49,512.6472 | 22 | 5,07,93,159.6658 | ||
| Total | 4,20,67,62,400.0000 | 24 | |||
| Regression output | confidence interval | ||||
| variables | coefficients | std. error | t (df=22) | p-value | 95% lower | 
| Intercept | -7,002.4471 | ||||
| Attendance | 53.0309 | 27.2962 | 1.943 | .0649 | -3.5779 | 
| Couple's Income | 0.3124 | 0.0774 | 4.036 | .0006 | 0.1519 | 
The regression model is:
Wedding cost = -7,002.4471 + 53.0309*Attendance + 0.3124*Couple's Income