paper （due in 26 hours）

	mean of not answering	mean of answering	mean difference	p-value
newreg	0.0536518	0.0434972	0.0101546	0.0042193
age	53.52564	58.35918	-4.833542	5.50E-55
female	0.5480307	0.5662047	-0.018174	0.0280706
vote00	0.6898449	0.7800407	-0.090196	2.45E-35
vote98	0.5427714	0.6105615	-0.06779	7.18E-17

Q2. No. Table 1 does not suggest that the control group will provide a good counterfactual for the treatment group's voting potential outcomes. Because table 1 shows the difference between treatment and comparison group is significant. It does not say anything with the control group.

However, it might prove the counterfactual to some degree if we treat people in treatment group who didn’t receive phone call as control group.

Q3. Yes, it is different. The mean of not answering in table 1 is not much different from the previous table but the mean of answering and difference is significantly different. The p-value is much smaller.

The reason is that answering the phone call will change voters’ behavior. The experiment might be successful.

Q4.

Q5. Adding covariates has an effect on the treatment effect because in increases the significance of getting a convincing variance in the experiment. Besides, on the relationship between the covariates, it indicates an increase in the precision of the estimates. On the outcome, it creates a predictive equation to use in the prediction of the coefficients being estimated. Lastly, on the treatment, the effect is that it is likely to have a positive outcome in general when applied in the right way.

Q6. a) Adding covariates in an RCT design increases the precision of the estimates hence improving on its significance and the regression adjustment design produces highly predictive results.

b) the magnitude and statistical significance of the point estimates for the two designs result in a positive and statistically significant because of the strong covariates added.

c) The two tables differ because of the possibility of there being a bias in the results. More so, in Table, the level of bias is low compared to Table 1.

Q7. Adding covariates to the regression in Table 2 reduced the bias because it caused the estimates to become more similar to the correct casual estimate. I think that it did because of the significance of the added covariates implying that the estimates achieved a better predictive value of the outcome. Additionally, the variable that reduced the bias the most is vote00 because it was increasing the precision of the regression equation and model.

Q8. I think that not all bias in the estimates was eliminated by adding the covariates to the regression because some of the estimates were not statistically significant hence the inability to eliminate bias in general.