States with higher percentage of educated adults will then have lower percentage of adults on probation. 

 

We believe that the warmer the state is, will have a close association with the percent of people over 65. As a states mean annual temperature in creases, the percent of senior citizens will also increase. We will measure this with the “over64” variable in the states data set.

lm(formula = total$cook.index ~ total$per.active)

Residuals:

Higher scores more Dem 

     Min       1Q   Median       3Q      Max 

-21.9077  -4.4554   0.8945   5.5688  14.6024 

Coefficients:

                 Estimate Std. Error t value Pr(>|t|)  

(Intercept)      -54.1561    26.2674  -2.062   0.0447 *

total$per.active   0.6714     0.3409   1.970   0.0546 .

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 8.704 on 48 degrees of freedom

Multiple R-squared:  0.07479, Adjusted R-squared:  0.05552 

F-statistic:  3.88 on 1 and 48 DF,  p-value: 0.05464

We believe the percentage of a state’s immigrant population will have an effect on its per capita income. With that being said, the higher the percentage of foreign born inhabitants will result in a lower per capita income.In return a lower percent of immigrants a higher per capita income.

We believe the percentage of a state’s immigrant population will have an effect on its per capita income and its democratic representation. With that being said, the higher the percentage of foreign born inhabitants will result in a lower per capita income.In return a lower percent of immigrants a higher per capita income.

Assignment 7 

Does a state’s fitness levels effect it’s politics?

Hypothesis: 

State’s with a higher percentage of the population that is obese will also have a lower score on the cook index scale. We believe this outcome will occur because in states with a higher conservation population there is in general higher unemployment rates that will correlate directly to obesity. A population with a lower income due to unemployment is more likely to buy cheap, unhealthy food which leads to weight gain and then obesity. 

Call:

lm(formula = Large ~ cook.index + unemploy, data = total)

Residuals:

    Min      1Q  Median      3Q     Max 

-8.9114 -2.0188  0.4673  2.5458  4.9347 

Coefficients:

                   Estimate Std.   Error       t value     Pr(>|t|)    

(Intercept)      24.82481    2.38730   10.399     8.93e-14 ***

cook.index    -0.21201      0.05104   -4.154      0.000137 ***

unemploy      0.78595      0.45289    1.735       0.089218 .  

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.192 on 47 degrees of freedom

Multiple R-squared:  0.3125, Adjusted R-squared:  0.2833 

F-statistic: 10.68 on 2 and 47 DF,  p-value: 0.0001498

The data proved that as states increase in obesity they are more likely to be conservative which was shown through a negative correlation with the cook index score. A lower score on the cook index scale shows that a state is more likely to vote republican. The data proved our hypothesis wrong that unemployment affected the obesity of a state because the P value indicated the correlation was not significant. 

Hypothesis: We believe that states that have more women representatives in legislature the less instances there will be of children in poverty. Women are typically more nurturing towards children and would help to create more programs that help families with children become less impoverished. 

The numbers that our regression produced were surprising to us because it showed that women in legislature really don’t have that much of an impact on lowering the amount of children in poverty. For every one woman increase in the legislature, the rate of impoverished children went down .001. Although they really didn’t have that much of an impact, they still had more than the other variables in our regression which were abortion rates and secularism. These two variables  had a 0.004% decrease and a 0.00012% decrease in children in percentage of children in  poverty. We were disappointed to see that the adjusted R-squared was 0.03421 meaning that about 3% of our data of the variation in the dependent variable is explained. Although our numbers were low this regression supported our hypothesis that the more women in the legislature the less children there will be in poverty. Overall, the numbers in this regression were not very reliable. 

Since there was little relationship between children in poverty and women representatives in legislature, we decided to run another variable. This time we ran unemployment and college education in relation to children in poverty. It is no surprise to us that there was a positive correlation between unemployment for every one percent increase in the unemployment there was a  0.013% increase in the amount of children in poverty. While a college education correlated to a 0.003% decrease in the amount of children in poverty. There was also a slight negative correlation between college education and children in poverty. These results were predictable but overall none of the variables we tested had a large impact on children in poverty. 

PSCI 2075

Professor Brown

11/04/2016

In class Assignment 7

Countries with higher rates of meat consumption will have a higher level of government efficiency. We believe this as when citizens are well fed and have the liberty to choose what they want to eat they will be more inclined to follow their governments laws. We also believe countries that produce high amounts of meat are usually on the more developed side which usually correlates to a more effective government. 

Call:
lm(formula = effectiveness ~ meat02, data = total)

Residuals:
Government effectiveness scale 
    Min      1Q  Median      3Q     Max 
-43.301  -8.837  -1.305   7.824  42.389 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 24.68858    2.11040   11.70   <2e-16 ***
meat02       0.49342    0.03758   13.13   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.76 on 151 degrees of freedom
Multiple R-squared:  0.5331, Adjusted R-squared:   0.53 
F-statistic: 172.4 on 1 and 151 DF,  p-value: < 2.2e-16

Regression Analysis: This regression output shows that our initial hypothesis was true. The coefficient for the variable meat02 shows that for every percentage increase on the government effectiveness scale, there is a .493 percentage increase in the amount of meat the country eats. This relationship showed a high level of significance, thus further proving our hypothesis. Although many reasons may account for this relationship, we still believe that it is because the more meat a country consumes, the more well fed and stable that country will be. Additionally, considering their freedom of choice to eat whatever they want, they will be more likely to abide by their governments laws.