UFO

this linear model tries to answer to the question of how the unemployment rate affects the appareances of UFOS in the United States, the data gather here came from National UFO Reporting Center Report Index by State / Province src=http://www.nuforc.org/webreports/ndxloc.html

The unemployment rate came from : Unemployment rate src=http://www.bls.gov/web/laus/laumstrk.htm BLS

and then the population rate by state from the census.gov

Pop <- read.csv("Population.csv")
Ue <- read.csv("Unemployment.csv")
UFO <- read.csv("UFO.CSV")
# Merging the data first:Unemployment and Population and then PU with the amount of UFO reports
PU <- merge(Pop,Ue)
head(PU)

##         State Population Rank Rate
## 1     ALABAMA  4,822,023   42  6.0
## 2      ALASKA    731,449   47  6.4
## 3     ARIZONA  6,553,255   46  6.3
## 4  CALIFORNIA 38,041,430   41  5.9
## 5    COLORADO  5,187,582   10  4.0
## 6 CONNECTICUT  3,590,347   28  5.2

UFPU <- merge(PU, UFO)
UFPU$Population <- as.numeric(UFPU$Population)
head(UFPU)

##         State Population Rank Rate Count
## 1     ALABAMA         29   42  6.0   889
## 2      ALASKA         44   47  6.4   451
## 3     ARIZONA         38   46  6.3  3336
## 4  CALIFORNIA         25   41  5.9 11542
## 5    COLORADO         30   10  4.0  1938
## 6 CONNECTICUT         22   28  5.2  1217

plot(UFPU$Population,UFPU$Count)

plot(UFPU$Rate, UFPU$Count)
reg <-lm(Count~Rate+Population,data=UFPU)
summary(reg)

## 
## Call:
## lm(formula = Count ~ Rate + Population, data = UFPU)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2214.7  -888.2  -572.9   305.2  9430.0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   63.325   1344.161   0.047    0.963
## Rate         362.830    257.120   1.411    0.165
## Population    -3.681     18.523  -0.199    0.843
## 
## Residual standard error: 1924 on 46 degrees of freedom
## Multiple R-squared:  0.04159,    Adjusted R-squared:  -8.212e-05 
## F-statistic: 0.998 on 2 and 46 DF,  p-value: 0.3764

reg3 <-lm( Count~ Rate,data = UFPU)
summary(reg3)

## 
## Call:
## lm(formula = Count ~ Rate, data = UFPU)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2265.4  -914.1  -537.6   232.1  9438.1 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -8.982   1280.701  -0.007    0.994
## Rate         358.120    253.396   1.413    0.164
## 
## Residual standard error: 1905 on 47 degrees of freedom
## Multiple R-squared:  0.04076,    Adjusted R-squared:  0.02036 
## F-statistic: 1.997 on 1 and 47 DF,  p-value: 0.1642

library(ggplot2)

splot <- ggplot(aes(UFPU$Count,UFPU$Rate),data = UFPU)
splot + geom_point() + geom_smooth(method="lm")

cor(UFPU$Count,UFPU$Rate)

## [1] 0.2019033

according to the regression summary, the relation between unemployment and the appareances of UFO is statiscally insignificant since the P value is far from 0.05, also it seems that states whith high population rate tend to see less UFO, even though we would expect different.

You can also embed plots, for example:

plot(UFPU\(Rate,UFPU\)Count) abline(reg3)

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.