# Preliminaries

This is a brief guide on how to use R and functions in tigerstats and related packages to do some very basic descriptive statistics. We will give “templates” for the functions, accompanied by no-frills examples of their use. Consult the function tutorials or other Help documents to learn more about the options for each function.

# One Factor Variable

## Graphics

$barchartGC(\sim variable, data = MyData)$

barchartGC(~seat,data=m111survey)

## Numerical Summaries

xtabs() and rowPerc():

seating <- xtabs(~seat,data=m111survey)
seating
## seat
##  1_front 2_middle   3_back
##       27       32       12
rowPerc(seating)
##
## seat 1_front 2_middle 3_back Total
##        38.03    45.07   16.9   100

# Two Factor Variables

## Graphics

$barchartGC(\sim exp + resp, data = MyData)$

barchartGC(~sex+seat,data=m111survey)

## Numerical Summaries

xtabs() and rowPerc():

sexSeat <- xtabs(~sex+seat,data=m111survey)
sexSeat
##         seat
## sex      1_front 2_middle 3_back
##   female      19       16      5
##   male         8       16      7
rowPerc(sexSeat)
##         seat
## sex      1_front 2_middle 3_back  Total
##   female   47.50    40.00  12.50 100.00
##   male     25.81    51.61  22.58 100.00

# One Numeric Variable

## Graphics

histogram(), densityplot(), or bwplot().

$function(\sim variable,data=myData)$

densityplot(~fastest,data=m111survey)

## Numerical Summaries

Use favstats():

favstats(~fastest,data=m111survey)
##  min   Q1 median    Q3 max     mean      sd  n missing
##   60 90.5    102 119.5 190 105.9014 20.8773 71       0

# One Factor and One Numeric

## Graphics

$histogram(\sim numeric \vert factor, data=MyData)$

$densityplot(\sim numeric \vert factor, data=MyData)$

$bwplot(numeric \sim factor, data=MyData)$

densityplot(~fastest|sex,data=m111survey)

## Numerical Summaries

favstats() again:

$favstats(numeric \sim factor, data=myData)$

favstats(fastest~sex,data=m111survey)
##      sex min Q1 median    Q3 max     mean       sd  n missing
## 1 female  60 90     95 110.0 145 100.0500 17.60966 40       0
## 2   male  85 99    110 122.5 190 113.4516 22.56818 31       0

# Two Numeric Variables

## Graphics

Scatter plots:

$xyplot(response \sim explanatory, data = myData)$

xyplot(GPA~fastest,data=m111survey,type=c("p","r"))

## Numerical Summaries

### Fitting a line to the data:

$lmGC(response \sim explanatory, data=myData)$

lmGC(GPA~fastest,data=m111survey)
##
##  Linear Regression
##
## Correlation coefficient r =  -0.1406
##
## Equation of Regression Line:
##
##   GPA = 3.5562 + -0.0034 * fastest
##
## Residual Standard Error: s   = 0.5053
## R^2 (unadjusted):        R^2 = 0.0198

### Fitting a polynomial to the data:

polyfitGC(OBP~Season,data=henderson,degree=2)
## Polynomial Regression, Degree = 2
##
## Residual Standard Error: s   = 0.0223
## R^2 (unadjusted):        R^2 = 0.289

## Prediction

fastGPAMod <- lmGC(GPA~fastest,data=m111survey)
predict(fastGPAMod,x=100)
## Predict GPA is about 3.216,
## give or take 0.5092 or so for chance variation.