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Call:

lm(formula = states$blkleg ~ states$blkpct08 + states$gay.support +

states$evm)

Residuals:

Percent of state legislators who are black

Min       1Q   Median       3Q      Max

-10.2966  -1.0852  -0.3789   0.9918   5.9713

Coefficients:

Estimate   Std. Error t value

(Intercept)        -0.093265125  3.659622128  -0.025

states$blkpct08     0.736346433  0.041049315  17.938

states$gay.support  0.000004468  0.062096712   0.000

states$evm         -0.035312939  0.074852081  -0.472

Pr(>|t|)

(Intercept)                      0.980

states$blkpct08    <0.0000000000000002 ***

states$gay.support               1.000

states$evm                       0.639

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

Residual standard error: 2.627 on 46 degrees of freedom

Multiple R-squared:  0.882, Adjusted R-squared:  0.8743

F-statistic: 114.6 on 3 and 46 DF,  p-value: < 0.00000000000000022

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Hypothesis: We believe that higher instances of drunk driving will be met with a statistically significantly higher instances of unemployment and lower instances of college education.

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Big T-Tests

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Chelsea Campana

Cady Diener

Diagnostics

Hypothesis: When we ran our initial regression, we were attempting to determine whether or not the percentage of women who are active in the workforce has an impact on the percent of women that are in the state legislature. We expected to find with our residual plot there would be a high quantity of outliers compared to where the majority of data lies. We also believed that the residual would overestimate most of our data.

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Diagnostics

Team Riff Raff (Henry Goettler, Griffin Hansen, Andrew Wise)

Hypothesis: (Taken from merge assignment) In a very inactive America we believe data on how physically active a state’s adult population is will influence a number of other societal and political factors. Specifically we wanted to look into how physical activity can affect the political party representation of a state. In order to do this we looked into how the percent of the adult population that is active leads to a more democratic state. Physical activity is telling of political identification for a variety of reasons, chiefly because more active people are are more concerned with the environmental health of their surroundings, with environmental regulations being a central issue to the democratic party it is no wonder physically active people are more likely to vote blue.

Analysis: Examining our residual plot of the linear model showing the impact of physical activity on the state’s political alignment, there seems to be significant outliers and a weak pattern. The significant outliers marked in the table above, Colorado, Utah, Arkansas and Mississippi impact the relationship between a state’s political alignment and its level of activity. The residual plot we have is biased and homoscedastic shown by how the data points don’t follow a straight line and how the data points are distributed evenly across the plot. This means that our linear model was not great at explaining the variation in a state’s political alignment. If we had seen an even distribution of data points and a linear line instead of a parabolic line then we could say that there is less error in our linear model and the actual outcome better follow the predicted outcome better.

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We merged state's average temperature into the states data set. We expected to see that as a state's average temp went up, the  percentage of people over 64 would also go up.

Call:
lm(formula = total$over64 ~ total$statetemp + states$south)

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Logan Dingwall

Antonia Wright

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Hypothesis: We believe that states that have more unemployed people will lead to more children in poverty. Families in unemployed situations typically have less money which can lead to poverty, therefore their children will be raised in poverty.

There are multiple outliers and no distinct pattern.

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This graph demonstrates the polarity between states that do have fracking wells and states that do not. The clusters that we see that are close to the line have few to no fracking wells within their state. The outlier states are states that have huge numbers of fracking wells , and have large expanses of open rural land. There is a pattern in the results on the graph, which leads us to have little faith in the OLS results, which may be unreliable.

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Call:

lm(formula = states$unemploy ~ MinimumWage$wage)

Residuals:

Unemployment rate

Min      1Q  Median      3Q     Max

-1.7581 -0.6044  0.0722  0.6461  2.1285

Coefficients:

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

(Intercept)         3.477      1.616    2.15    0.037 *

MinimumWage$wage    0.217      0.204    1.06    0.293

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

Residual standard error: 1 on 48 degrees of freedom

Multiple R-squared:  0.023, Adjusted R-squared:  0.00265

F-statistic: 1.13 on 1 and 48 DF,  p-value: 0.293

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http://127.0.0.1:13392/graphics/plot_zoom_png?width=999&height=812

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Hypothesis: We believe that a state's obesity level will be closely correlated to the percent of the population that has a college degree or higher because education will allow individuals to learn more about the impacts of living a healthy lifestyle and also college campuses around the country foster an active lifestyle.

Call:

lm(formula = Large ~ college + prcapinc + urban, data = Data1)

Residuals:

Min      1Q  Median      3Q     Max

-6.3912 -1.5526  0.0647  1.4102  6.6222

Coefficients:

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

(Intercept) 45.5214967  2.6960841  16.884  < 2e-16 ***

college     -0.6030154  0.1389037  -4.341  7.7e-05 ***

prcapinc     0.0001281  0.0001468   0.873   0.3873

urban       -0.0642188  0.0290102  -2.214   0.0318 *

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

Residual standard error: 2.565 on 46 degrees of freedom

Multiple R-squared:  0.5655, Adjusted R-squared:  0.5371

F-statistic: 19.96 on 3 and 46 DF,  p-value: 1.983e-08

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merged rural population into the worlds data set and made a regression with the year women were first enfranchised.

As shown in the plot, we see that the two countries to the far right are the outliers effecting the prediction, those countries are Hungary and Chad.

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Regression formula with the merged data: new <- lm(college~education + density, data=total2)

We merged the variable the percentage of state's expenditures that go to education with the states data set. Our residual plot showed many outliers, but it did not show any sort of pattern. We were right about a few states that fall right on the line of our predictions, but we under predicted on a lot of states as well. Mainly Colorado, Virginia, and Vermont. We also over predicted the most with West Virginia.

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We hypothesized that states with higher religious populations would correlate to a higher hunting population.

Based on the residual plot that we created from our multiple regression analysis we have found that there are both patterns and major outliers. Missouri is the largest outlier of the states in the data set. there may be some other outliers but the other surprising points are in close proximity to other points so it seems less likely that they would be outliers. As for patterns there is a pretty obvious one. most of the data points fall into diagonal bands comprised of other data sets. We have no idea what would create these diagonal strips but it shows a clear pattern in the data.