Project One Fall 2008. II a: plot IIb: Survive = a + bfemale + cfare + e i. Is regression statistically significant?, yes, F-stat & probability ii.

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Presentation transcript:

Project One Fall 2008

II a: plot

IIb: Survive = a + b*female + c*fare + e i. Is regression statistically significant?, yes, F-stat & probability ii. Interpret constant term when female and fare are zero: intercept for male iii. Is coefficient on fare statistically significant? Yes, t-stat and probability iv. Interpret the coefficient on fare: Probability of survival increases by 0.14 for every 100 £ v. Is the constant term significantly different from coefficient on female?

Estimation Command: ===================== LS SURVIVE FARE FEMALE C Estimation Equation: ===================== SURVIVE = C(1)*FARE + C(2)*FEMALE + C(3) Substituted Coefficients: ===================== SURVIVE = *FARE *FEMALE View menu in Equation window: representations

View menu: Coefficient tests, Wald Wald Test: Equation: Untitled Null Hypothesis:C(1)=C(2) F-statistic Probability Chi-square Probability

IIc. Plot the fitted value of survive Vs. Fare: View Menu, Actual, Fitted, Residual Table

Select fare and Fitted and from Quick menu, Graph: scatter diagram

Confusing without labels

2d. Add age to the regression 2e: is it women and children first?

Investigating Class Class variable: 1, 2, 3 Genr first = 1*(class=1) + 0*(class>1) Genr third = 1*(class=3) + 0*(class<3) Genr second = 1-first-second

View: actual …, select & copy fitted

Regression: Survive vs. “class” & fare

Investigate Age and Gender Agefem=age*female

Plot of Survive Vs. Age for Females

Regression

Regression: survive Vs. “class”, fare, Age of female

Babies

Sort on Babies: Procs

Infants