head(women)
##   height weight
## 1     58    115
## 2     59    117
## 3     60    120
## 4     61    123
## 5     62    126
## 6     63    129
names(women)
## [1] "height" "weight"
attach(women)

These steps are to visiualize my data and see its general structure

plot(height,weight,ylab="Weight (lbs)", xlab= "Height (IN)", main =   "Womens weight and Height")

This is giving me a general scatterplot of the data with the first variable named as the predictor. I have given the axis more appropriate and descriptive names.

womensmod<- lm(weight~height)
womensmod
## 
## Call:
## lm(formula = weight ~ height)
## 
## Coefficients:
## (Intercept)       height  
##      -87.52         3.45

This regression is saying that for every increase of 1 pound we can expect a height increase of 3.45 inches. The intercept can’t really be interpreted in this case.

plot(height,weight,ylab="Weight (lbs)", xlab= "Height (IN)", main =   "Womens weight and Height")
abline(-87.52,3.45)

head(women)
##   height weight
## 1     58    115
## 2     59    117
## 3     60    120
## 4     61    123
## 5     62    126
## 6     63    129

For a person that is 58 inches tall we expect them to weigh 58*3.45 -87.52. 112.58 This is just over 2% off what we expected.

58*3.45 - 87.52
## [1] 112.58
(115-112.58)/115
## [1] 0.02104348
myresids <- womensmod$residuals
hist(myresids)

The data looks like it is not normal and skewwed left

qqnorm(myresids)
qqline(myresids)

The qq plot also shows the data is not very normal

summary(womensmod)
## 
## Call:
## lm(formula = weight ~ height)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7333 -1.1333 -0.3833  0.7417  3.1167 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -87.51667    5.93694  -14.74 1.71e-09 ***
## height        3.45000    0.09114   37.85 1.09e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.525 on 13 degrees of freedom
## Multiple R-squared:  0.991,  Adjusted R-squared:  0.9903 
## F-statistic:  1433 on 1 and 13 DF,  p-value: 1.091e-14

This is most of the basic regression and the steps to do it in R