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# Linear Regression Project
# frequency of named hurricanes over time
# 1851-2022

# record variables and values - get into environment

NS <- c(6,  5,  8,  5,  5,  6,  4,  6,  8,  7,  8,  6,  9,  5,  7,  7,  9,  4,  10, 11, 8,  5,  5,  7,  6,  5,  8,  12, 8,  11, 7,  6,  4,  4,  8,  12, 19, 9,  9,  4,  10, 9,  12, 7,  6,  7,  6,  11, 10, 7,  13, 5,  10, 6,  5,  11, 5,  10, 12, 5,  6,  7,  6,  1,  6,  15, 4,  6,  5,  5,  7,  5,  9,  11, 4,  11, 8,  6,  5,  3,  13, 15, 20, 13, 8,  17, 11, 9,  6,  9,  6,  11, 10, 14, 11, 7,  10, 10, 16, 16, 12, 11, 14, 16, 13, 12, 8,  12, 14, 8,  12, 7,  10, 13, 10, 11, 8,  8,  18, 10, 13, 7,  8,  11, 9,  10, 6,  12, 9,  11, 12, 6,  4,  13, 11, 6,  7,  12, 11, 14, 8,  7,  8,  7,  19, 13, 8,  14, 12, 15, 15, 12, 16, 15, 28, 10, 15, 16, 9,  19, 19, 19, 14, 8,  11, 15, 17, 15, 18, 30, 21, 14)
YR<- c(1851:2022)

# next command created linear regression

LR  <-lm(NS~YR)
summary(LR)
## 
## Call:
## lm(formula = NS ~ YR)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.2491 -2.8462 -0.2045  1.8658 15.9058 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -86.638332  11.354784  -7.630  1.6e-12 ***
## YR            0.049868   0.005862   8.507  8.9e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.817 on 170 degrees of freedom
## Multiple R-squared:  0.2986, Adjusted R-squared:  0.2945 
## F-statistic: 72.38 on 1 and 170 DF,  p-value: 8.896e-15
# next command creates scatter plot

plot(YR, NS)

#   main = "Named Storms in the Atlantic", xlab ="", ylab = "Year", xlim = c(0.0,25.0), sub="Figure 1. 
#    Named Storms in the Atlantic 1851 - 2022")

abline(LR, col="red")

plot(YR,residuals(LR))

abline(a=0,b=0)

summary(cars) ```

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