# ipsRdbs: Introduction to Probability, Statistics and R for Data-Based Sciences

Sujit K. Sahu1*

1University of Southampton

*S.K.Sahu@soton.ac.uk

#### April 10, 2024

#### Abstract

This is a vignette for the `R` package ipsRdbs. This package contains data sets, programmes and illustrations discussed in the book, “Introduction to Probability, Statistics and R: Foundations for Data-Based Sciences” by Sahu (2023).

#### Package

ipsRdbs 1.0.0

# Contents

# 1 Introduction

This package complements the book, `Introduction to Probability, Statistics and R for Data-Based Sciences’ by Sahu (2023). The package distributes the data sets used in the book and provides code illustrating the statistical modeling of the data sets. In addition, the package provides code for illustrating various results in probability and statistics. For example, it provides code to simulate the Monty python game illustrating conditional probability, and gives simulation based examples to illustrate the central limit theorem and the weak law of large numbers. Thus the package helps a beginner reader in enhancing understanding of a few elementary concepts in probability and statistics, and introduces them to perform linear statistical modelling, i.e., regression and ANOVA which are among the key foundational concepts of data science and machine learning, more generally data-based sciences.

Figure 1: ipsRdbs book cover

## 1.1 Installing the required software packages

The reader is first instructed to install the `R` software package by searching for `CRAN` in the internet. The reader shoud then go onto the web-page https://cran.r-project.org/ and install the correct and latest version of the package on their own computer. Please note that `R` cannot be installed on a mobile phone. Once `R` has been installed, the next task is to install the frontend software package Rstudio, which provides an easier interface to work with `R`.

After installing `R` and `Rstudio`, the reader should launch the `Rstudio` programme in their computer. This will open up a four pane window with one named ‘Console’ or ‘Terminal’. This window accepts commands (or our instructions) and prints results. For example, the reader may type 2+2 and then hit the Enter button to examine the result. The reader is asked to search the internet for gentler introductions and videos.

In order to getting started here, thereader is aked to install the add-on `R` package `ipsRdbs` simply by issuing the `R` command

    install.packages("ipsRdbs", dependencies=TRUE)

without committing any typing mistakes. If this installation is successful, the reader can issue the following two commands to list all `R` objects (data sets and programmes) included in the package.

    library(ipsRdbs)
    ls("package:ipsRdbs")

Note that this command will only produce the intended results if only the package has been successfully installed in the first place.

## 1.2 How to learn more about the objects included in the package

All the listed objects, as the output of the `ls` command in the previous section, have associated help files. The reader can gain information for each of those object by asking for help by typing the question mark immediately followed by the object name, e.g. `?butterfly` or by issuing the command `help(butterfly)`.

The help files provide details about the objects and the user is able to run all the code included as illustrations at the end of the help file. This cam be done either by clicking the `Run Examples` link or simply by copy-pasting all the commands onto the command console in Rstudio. This is a great advantage of `R` as it allows the users to reproduce the results without having to learn all the commands and syntax correctly. After gaining this confidence, a beginner user can examine and experiment with the commands further. More details regarding the objects are provided in the book Sahu (2023).

The remainder of this vignette simply elaborates the help files for all the main objects and programmes included in the package. The main intention here is to enable the reader to reproduce all the results by actually running the commands and the code included already included in the help files.

Section 2 discusses all the data sets. All the `R` functions are discussed in 3.
Some summary remarks are provided in Section 4.

# 2 Data sets

## 2.1 `beanie`: Age and value of beanie baby toys.

This data set contains the age and the value of 50 beanie baby toys. Source: Beanie world magazine. This data set has been used as an example of simple linear regression modellinhg where the exercise is to predict the value of a beanie baby toy by knowing it’s age.

    head(beanie)
    #>      name age value
    #> 1    Ally  52    55
    #> 2   Batty  12    12
    #> 3   Bongo  28    40
    #> 4 Blackie  52    10
    #> 5   Bucky  40    45
    #> 6  Bumble  28   600
    summary(beanie)
    #>      name                age            value
    #>  Length:50          Min.   : 5.00   Min.   :  10.0
    #>  Class :character   1st Qu.:12.00   1st Qu.:  15.0
    #>  Mode  :character   Median :28.00   Median :  26.5
    #>                     Mean   :26.52   Mean   : 128.9
    #>                     3rd Qu.:40.00   3rd Qu.:  62.5
    #>                     Max.   :64.00   Max.   :1900.0
    plot(beanie$age, beanie$value, xlab="Age", ylab="Value", pch="*", col="red")

## 2.2 `bill`: Wealth and age of world billionaires

This data set contains wealth, age and region of 225 billionaires in 1992 as reported in the Fortune magazine. This data set can be used to illustrate exploratory data analysis by producing side-by-side box plots of wealth for billionaires from different continents of the world. It can also be used for multiple linear regression models, although such tasks have not been undertaken here.

    head(bill)
    #>   wealth age region
    #> 1   37.0  50      M
    #> 2   24.0  88      U
    #> 3   14.0  64      A
    #> 4   13.0  63      U
    #> 5   13.0  66      U
    #> 6   11.7  72      E
    summary(bill)
    #>      wealth            age         region
    #>  Min.   : 1.000   Min.   :  7.00   A:37
    #>  1st Qu.: 1.300   1st Qu.: 56.00   E:76
    #>  Median : 1.800   Median : 65.00   M:22
    #>  Mean   : 2.726   Mean   : 64.03   O:28
    #>  3rd Qu.: 3.000   3rd Qu.: 72.00   U:62
    #>  Max.   :37.000   Max.   :102.00
    library(ggplot2)
    gg <- ggplot2::ggplot(data=bill, aes(x=age, y=wealth)) +
    geom_point(aes(col=region, size=wealth)) +
    geom_smooth(method="loess", se=FALSE) +
    xlim(c(7, 102)) +
    ylim(c(1, 37)) +
    labs(subtitle="Wealth vs Age of Billionaires",
    y="Wealth (Billion US $)", x="Age",
    title="Scatterplot", caption = "Source: Fortune Magazine, 1992.")
    plot(gg)
    #> `geom_smooth()` using formula = 'y ~ x'

## 2.3 `bodyfat` : Body fat percentage and skinfold thickness of athletes

This data set contains body fat percentage data for 102 elite male athletes training at the Australian Institute of Sport. This data set has been used to illustrate simple linear regression in Chapter 17 of the book by Sahu (2023).

    summary(bodyfat)
    #>     Skinfold         Bodyfat
    #>  Min.   : 28.00   Min.   : 5.630
    #>  1st Qu.: 37.52   1st Qu.: 6.968
    #>  Median : 47.70   Median : 8.625
    #>  Mean   : 51.42   Mean   : 9.251
    #>  3rd Qu.: 58.15   3rd Qu.:10.010
    #>  Max.   :113.50   Max.   :19.940
    plot(bodyfat$Skinfold,  bodyfat$Bodyfat, xlab="Skin", ylab="Fat")

    plot(bodyfat$Skinfold,  log(bodyfat$Bodyfat), xlab="Skin", ylab="log Fat")

    plot(log(bodyfat$Skinfold),  log(bodyfat$Bodyfat), xlab="log Skin", ylab="log Fat")

    # Keep the transformed variables in the data set
    bodyfat$logskin <- log(bodyfat$Skinfold)
    bodyfat$logbfat <- log(bodyfat$Bodyfat)
    bodyfat$logskin <- log(bodyfat$Skinfold)
     # Create a grouped variable
    bodyfat$cutskin <- cut(log(bodyfat$Skinfold), breaks=6)
    boxplot(data=bodyfat, Bodyfat~cutskin, col=2:7)

    require(ggplot2)
    p2 <- ggplot(data=bodyfat, aes(x=cutskin, y=logbfat)) +
    geom_boxplot(col=2:7) +
    stat_summary(fun=mean, geom="line", aes(group=1), col="blue", linewidth=1) +
    labs(x="Skinfold", y="Percentage of log bodyfat",
    title="Boxplot of log-bodyfat percentage vs grouped log-skinfold")
    plot(p2)

    n <- nrow(bodyfat)
    x <- bodyfat$logskin
    y <- bodyfat$logbfat
    xbar <- mean(x)
    ybar <- mean(y)
    sx2 <- var(x)
    sy2 <- var(y)
    sxy <- cov(x, y)
    r <- cor(x, y)
    print(list(n=n, xbar=xbar, ybar=ybar, sx2=sx2, sy2=sy2, sxy=sxy, r=r))
    #> $n
    #> [1] 102
    #>
    #> $xbar
    #> [1] 3.883108
    #>
    #> $ybar
    #> [1] 2.175978
    #>
    #> $sx2
    #> [1] 0.1084285
    #>
    #> $sy2
    #> [1] 0.09106112
    #>
    #> $sxy
    #> [1] 0.09566069
    #>
    #> $r
    #> [1] 0.9627095
    hatbeta1 <- r * sqrt(sy2/sx2) # calculates estimate of the slope
    hatbeta0 <- ybar - hatbeta1 * xbar # calculates estimate of the intercept
    rs <-  y - hatbeta0 - hatbeta1 * x  # calculates residuals
    s2 <- sum(rs^2)/(n-2)  # calculates estimate of sigma2
    s2
    #> [1] 0.006731453
    bfat.lm <- lm(logbfat ~ logskin, data=bodyfat)
    ### Check the diagnostics
    plot(bfat.lm$fit, bfat.lm$res, xlab="Fitted values", ylab = "Residuals", pch="*")
    abline(h=0)

    ### Should be a random scatter
    qqnorm(bfat.lm$res, col=2)
    qqline(bfat.lm$res, col="blue")

    # All Points should be on the straight line
    summary(bfat.lm)
    #>
    #> Call:
    #> lm(formula = logbfat ~ logskin, data = bodyfat)
    #>
    #> Residuals:
    #>       Min        1Q    Median        3Q       Max
    #> -0.223408 -0.055661  0.000052  0.056390  0.152984
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -1.24988    0.09661  -12.94   <2e-16 ***
    #> logskin      0.88225    0.02479   35.59   <2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.08205 on 100 degrees of freedom
    #> Multiple R-squared:  0.9268, Adjusted R-squared:  0.9261
    #> F-statistic:  1266 on 1 and 100 DF,  p-value: < 2.2e-16
    anova(bfat.lm)
    #> Analysis of Variance Table
    #>
    #> Response: logbfat
    #>            Df Sum Sq Mean Sq F value    Pr(>F)
    #> logskin     1 8.5240  8.5240  1266.3 < 2.2e-16 ***
    #> Residuals 100 0.6731  0.0067
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    plot(bodyfat$logskin,  bodyfat$logbfat, xlab="log Skin", ylab="log Fat")
    abline(bfat.lm, col=7)
    title("Scatter plot with the fitted Linear Regression line")

    # 95% CI for beta(1)
    # 0.88225 + c(-1, 1) * qt(0.975, df=100) *  0.02479
    # round(0.88225 + c(-1, 1) * qt(0.975, df=100) *  0.02479, 2)
    # To test H0: beta1 = 1.
    tstat <- (0.88225 -1)/0.02479
    pval <- 2 * (1- pt(abs(tstat), df=100))
    x <- seq(from=-5, to=5, length=500)
    y <- dt(x, df=100)
    plot(x, y,  xlab="", ylab="", type="l")
    title("T-density with df=100")
    abline(v=abs(tstat))
    abline(h=0)
    x1 <- seq(from=abs(tstat), to=10, length=100)
    y1 <- rep(0, length=100)
    x2 <- x1
    y2 <- dt(x1, df=100)
    segments(x1, y1, x2, y2)
    abline(h=0)

    # Predict at a new value of Skinfold=70
    # Create a new data set called new
    newx <- data.frame(logskin=log(70))
    a <- predict(bfat.lm, newdata=newx, se.fit=TRUE)
    # Confidence interval for the mean of log bodyfat  at skinfold=70
    a <- predict(bfat.lm, newdata=newx, interval="confidence")
    # a
    #          fit      lwr     upr
    # [1,] 2.498339 2.474198 2.52248
    # Prediction interval for a future log bodyfat  at skinfold=70
    a <- predict(bfat.lm, newdata=newx, interval="prediction")
    a
    #>        fit      lwr      upr
    #> 1 2.498339 2.333783 2.662895
    #          fit      lwr      upr
    # [1,] 2.498339 2.333783 2.662895
    #prediction intervals for the mean
    pred.bfat.clim <- predict(bfat.lm, data=bodyfat, interval="confidence")
    #prediction intervals for future observation
    pred.bfat.plim <- suppressWarnings(predict(bfat.lm, data=bodyfat, interval="prediction"))
    plot(bodyfat$logskin,  bodyfat$logbfat, xlab="log Skin", ylab="log Fat")
    abline(bfat.lm, col=5)
    lines(log(bodyfat$Skinfold), pred.bfat.clim[,2], lty=2, col=2)
    lines(log(bodyfat$Skinfold), pred.bfat.clim[,3], lty=2, col=2)
    lines(log(bodyfat$Skinfold), pred.bfat.plim[,2], lty=4, col=3)
    lines(log(bodyfat$Skinfold), pred.bfat.plim[,3], lty=4, col=3)
    title("Scatter plot with the fitted line and prediction intervals")
    symb <- c("Fitted line", "95% CI for mean", "95% CI for observation")
    ## legend(locator(1), legend = symb, lty = c(1, 2, 4), col = c(5, 2, 3))
    # Shows where we predicted earlier
    abline(v=log(70))

    summary(bfat.lm)
    #>
    #> Call:
    #> lm(formula = logbfat ~ logskin, data = bodyfat)
    #>
    #> Residuals:
    #>       Min        1Q    Median        3Q       Max
    #> -0.223408 -0.055661  0.000052  0.056390  0.152984
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -1.24988    0.09661  -12.94   <2e-16 ***
    #> logskin      0.88225    0.02479   35.59   <2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.08205 on 100 degrees of freedom
    #> Multiple R-squared:  0.9268, Adjusted R-squared:  0.9261
    #> F-statistic:  1266 on 1 and 100 DF,  p-value: < 2.2e-16
    anova(bfat.lm)
    #> Analysis of Variance Table
    #>
    #> Response: logbfat
    #>            Df Sum Sq Mean Sq F value    Pr(>F)
    #> logskin     1 8.5240  8.5240  1266.3 < 2.2e-16 ***
    #> Residuals 100 0.6731  0.0067
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 2.4 `bombhits`: Number and frequency of bombhits in London

This data set contains the number of bomb hits in 576 areas in London during World War II. Data sourced from Shaw and Shaw (2019), see also Hand {} (1993). (David J. Hand and Ostrowski 1993) (Shaw and Shaw 2019) This data set has been used to illustrate elementary concepts of statistical inference in Chapter 9 of the book Sahu (2023).

     summary(bombhits)
    #>    numberhit         freq
    #>  Min.   :0.00   Min.   :  1.0
    #>  1st Qu.:1.25   1st Qu.: 14.0
    #>  Median :2.50   Median : 64.0
    #>  Mean   :2.50   Mean   : 96.0
    #>  3rd Qu.:3.75   3rd Qu.:181.5
    #>  Max.   :5.00   Max.   :229.0
     # Create a vector of data
     x <- c(rep(0, 229), rep(1, 211), rep(2, 93), rep(3, 35), rep(4, 7), 5)
     y <- c(229, 211, 93, 35, 7, 1) # Frequencies
     rel_freq <- y/576
     xbar <- mean(x)
     pois_prob <- dpois(x=0:5, lambda=xbar)
     fit_freq <- pois_prob * 576
      #Check
      cbind(x=0:5, obs_freq=y, rel_freq =round(rel_freq, 4),
      Poisson_prob=round(pois_prob, 4), fit_freq=round(fit_freq, 1))
    #>      x obs_freq rel_freq Poisson_prob fit_freq
    #> [1,] 0      229   0.3976       0.3950    227.5
    #> [2,] 1      211   0.3663       0.3669    211.3
    #> [3,] 2       93   0.1615       0.1704     98.1
    #> [4,] 3       35   0.0608       0.0528     30.4
    #> [5,] 4        7   0.0122       0.0122      7.1
    #> [6,] 5        1   0.0017       0.0023      1.3
      obs_freq <- y
      xuniques <- 0:5
      a <- data.frame(xuniques=0:5, obs_freq =y, fit_freq=fit_freq)
      barplot(rbind(obs_freq, fit_freq),
      args.legend = list(x = "topright"),
      xlab="No of bomb hits",
      names.arg = xuniques,  beside=TRUE,
      col=c("darkblue","red"),
      legend =c("Observed", "Fitted"),
      main="Observed and Poisson distribution fitted frequencies
      for the number of bomb hits in London")

## 2.5 `cement`: Breaking strength of cement

Contains data regarding breaking strength of cement. This data set has been used to illustrate analysis of variance in Chapter 19 of the book Sahu (2023).

    summary(cement)
    #>     strength        gauger     breaker
    #>  Min.   :4160   Min.   :1   Min.   :1
    #>  1st Qu.:4660   1st Qu.:1   1st Qu.:1
    #>  Median :5100   Median :2   Median :2
    #>  Mean   :5118   Mean   :2   Mean   :2
    #>  3rd Qu.:5565   3rd Qu.:3   3rd Qu.:3
    #>  Max.   :6200   Max.   :3   Max.   :3
    boxplot(data=cement, strength~gauger, col=1:3)

    boxplot(data=cement, strength~breaker, col=1:3)

## 2.6 `cfail`: Number of weekly computer failures

This data set contains weekly number of failures of a university computer system over a period of two years. Source: Hand {} (1993). (David J. Hand and Ostrowski 1993) Like the `bombhits` example this data set has been used to illustrate elementary concepts of statistical inference in Chapter 9 of the book Sahu (2023).

## 2.7 `cheese` : Taste of cheese

This data set is from a testing of cheese experiment. This data set has been used to illustrate multiple linear regression modeling in Chapter 18 of the book Sahu (2023).

    summary(cheese)
    #>      Taste         AceticAcid          H2S             LacticAcid
    #>  Min.   : 0.70   Min.   : 87.97   Min.   :   20.01   Min.   :0.860
    #>  1st Qu.:13.55   1st Qu.:188.20   1st Qu.:   53.74   1st Qu.:1.250
    #>  Median :20.95   Median :227.03   Median :  207.46   Median :1.450
    #>  Mean   :24.53   Mean   :284.18   Mean   : 2702.98   Mean   :1.442
    #>  3rd Qu.:36.70   3rd Qu.:358.84   3rd Qu.: 1950.35   3rd Qu.:1.667
    #>  Max.   :57.20   Max.   :637.78   Max.   :26876.31   Max.   :2.010
    #>      logH2S
    #>  Min.   : 2.996
    #>  1st Qu.: 3.977
    #>  Median : 5.329
    #>  Mean   : 5.942
    #>  3rd Qu.: 7.575
    #>  Max.   :10.199
    pairs(cheese)

    GGally::ggpairs(data=cheese)

    cheese.lm <- lm(Taste ~ AceticAcid +  LacticAcid + logH2S, data=cheese, subset=2:30)
     # Check the diagnostics
     plot(cheese.lm$fit, cheese.lm$res, xlab="Fitted values", ylab = "Residuals")
     abline(h=0)

     # Should be a random scatter
     qqnorm(cheese.lm$res, col=2)
     qqline(cheese.lm$res, col="blue")

     summary(cheese.lm)
    #>
    #> Call:
    #> lm(formula = Taste ~ AceticAcid + LacticAcid + logH2S, data = cheese,
    #>     subset = 2:30)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -18.0718  -5.9432   0.3657   5.3919  25.0876
    #>
    #> Coefficients:
    #>              Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -31.41789    9.95702  -3.155  0.00414 **
    #> AceticAcid    0.00478    0.01484   0.322  0.74997
    #> LacticAcid   21.70560    8.69079   2.498  0.01945 *
    #> logH2S        3.84994    1.21284   3.174  0.00396 **
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 10.06 on 25 degrees of freedom
    #> Multiple R-squared:  0.6631, Adjusted R-squared:  0.6227
    #> F-statistic:  16.4 on 3 and 25 DF,  p-value: 4.222e-06
     cheese.lm2 <- lm(Taste ~ LacticAcid + logH2S, data=cheese)
     # Check the diagnostics
     plot(cheese.lm2$fit, cheese.lm2$res, xlab="Fitted values", ylab = "Residuals")
     abline(h=0)

     qqnorm(cheese.lm2$res, col=2)
     qqline(cheese.lm2$res, col="blue")

     summary(cheese.lm2)
    #>
    #> Call:
    #> lm(formula = Taste ~ LacticAcid + logH2S, data = cheese)
    #>
    #> Residuals:
    #>     Min      1Q  Median      3Q     Max
    #> -17.343  -6.529  -1.164   4.844  25.618
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  -27.592      8.982  -3.072  0.00481 **
    #> LacticAcid    19.888      7.959   2.499  0.01885 *
    #> logH2S         3.946      1.136   3.475  0.00174 **
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 9.942 on 27 degrees of freedom
    #> Multiple R-squared:  0.6517, Adjusted R-squared:  0.6259
    #> F-statistic: 25.26 on 2 and 27 DF,  p-value: 6.552e-07
     # How can we predict?
     newcheese <- data.frame(AceticAcid = 300, LacticAcid = 1.5, logH2S=4)
     cheese.pred <- predict(cheese.lm2, newdata=newcheese, se.fit=TRUE)
     cheese.pred
    #> $fit
    #>        1
    #> 18.02409
    #>
    #> $se.fit
    #> [1] 3.111924
    #>
    #> $df
    #> [1] 27
    #>
    #> $residual.scale
    #> [1] 9.942372
     # Obtain confidence interval
     cheese.pred$fit + c(-1, 1) * qt(0.975, df=27) * cheese.pred$se.fit
    #> [1] 11.63895 24.40923
     # Using R to predict
     cheese.pred.conf.limits <- predict(cheese.lm2, newdata=newcheese, interval="confidence")
     cheese.pred.conf.limits
    #>        fit      lwr      upr
    #> 1 18.02409 11.63895 24.40923
     # How to find prediction interval
     cheese.pred.pred.limits <- predict(cheese.lm2, newdata=newcheese, interval="prediction")
     cheese.pred.pred.limits
    #>        fit       lwr      upr
    #> 1 18.02409 -3.351891 39.40007

## 2.8 `emissions` : Exhaust emissions of cars

Data on the nitrous oxide content of exhaust emissions from a set of cars was collected by the Australian Traffic Accident Research Bureau to explore the relationship between several measures of nitrous oxide emissions. Like the `cheese` data set, this has been used to illustrate multiple linear regression modeling in Chapter 18 of the book Sahu (2023).

    summary(emissions)
    #>      Make              Odometer         Capacity         CS505
    #>  Length:54          Min.   :  7285   Min.   :1.300   Min.   : 3.675
    #>  Class :character   1st Qu.: 44208   1st Qu.:1.600   1st Qu.: 8.364
    #>  Mode  :character   Median : 80164   Median :2.000   Median :10.598
    #>                     Mean   : 84444   Mean   :2.346   Mean   :10.949
    #>                     3rd Qu.:117248   3rd Qu.:2.900   3rd Qu.:13.125
    #>                     Max.   :232574   Max.   :5.000   Max.   :21.475
    #>       T867             H505            ADR27           ADR37
    #>  Min.   : 1.158   Min.   : 1.340   Min.   :0.480   Min.   :0.340
    #>  1st Qu.: 4.188   1st Qu.: 4.884   1st Qu.:1.053   1st Qu.:1.005
    #>  Median : 6.769   Median : 8.795   Median :1.475   Median :1.423
    #>  Mean   : 7.217   Mean   : 9.376   Mean   :1.525   Mean   :1.478
    #>  3rd Qu.:10.001   3rd Qu.:12.906   3rd Qu.:1.981   3rd Qu.:2.029
    #>  Max.   :15.982   Max.   :21.465   Max.   :2.851   Max.   :2.862
    #>     logCS505        logT867          logH505          logADR27
    #>  Min.   :1.302   Min.   :0.1466   Min.   :0.2927   Min.   :-0.73397
    #>  1st Qu.:2.124   1st Qu.:1.4322   1st Qu.:1.5850   1st Qu.: 0.05191
    #>  Median :2.361   Median :1.9104   Median :2.1742   Median : 0.38857
    #>  Mean   :2.318   Mean   :1.8185   Mean   :2.0440   Mean   : 0.33895
    #>  3rd Qu.:2.575   3rd Qu.:2.3026   3rd Qu.:2.5577   3rd Qu.: 0.68382
    #>  Max.   :3.067   Max.   :2.7715   Max.   :3.0664   Max.   : 1.04765
    #>     logADR37
    #>  Min.   :-1.078810
    #>  1st Qu.: 0.004947
    #>  Median : 0.352585
    #>  Mean   : 0.290615
    #>  3rd Qu.: 0.707568
    #>  Max.   : 1.051611

     rawdata <- emissions[, c(8, 4:7)]
     pairs(rawdata)

    # Fit the model on the raw scale
    raw.lm <- lm(ADR37 ~ ADR27 + CS505  + T867 + H505, data=rawdata)
    old.par <- par(no.readonly = TRUE)
    par(mfrow=c(2,1))
    plot(raw.lm$fit, raw.lm$res,xlab="Fitted values",ylab="Residuals", main="Anscombe plot")
    abline(h=0)
    qqnorm(raw.lm$res,main="Normal probability plot", col=2)
    qqline(raw.lm$res, col="blue")

    # summary(raw.lm)
    logdata <- log(rawdata)
    # This only logs the values but not the column names!
    # We can use the following command to change the column names or you can use
    # fix(logdata) to do it.
    dimnames(logdata)[[2]] <- c("logADR37", "logCS505", "logT867", "logH505", "logADR27")
    pairs(logdata)

    log.lm <- lm(logADR37 ~ logADR27 + logCS505  + logT867 + logH505, data=logdata)
    plot(log.lm$fit, log.lm$res,xlab="Fitted values",ylab="Residuals", main="Anscombe plot")
    abline(h=0)
    qqnorm(log.lm$res,main="Normal probability plot", col=2)
    qqline(log.lm$res, col="blue")

    summary(log.lm)
    #>
    #> Call:
    #> lm(formula = logADR37 ~ logADR27 + logCS505 + logT867 + logH505,
    #>     data = logdata)
    #>
    #> Residuals:
    #>       Min        1Q    Median        3Q       Max
    #> -0.140390 -0.038389 -0.003712  0.040204  0.153275
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -0.12997    0.43073  -0.302 0.764119
    #> logADR27     0.94173    0.24140   3.901 0.000292 ***
    #> logCS505    -0.16790    0.16086  -1.044 0.301712
    #> logT867      0.15736    0.08986   1.751 0.086177 .
    #> logH505      0.09997    0.01857   5.385 2.04e-06 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.06038 on 49 degrees of freedom
    #> Multiple R-squared:  0.9852, Adjusted R-squared:  0.984
    #> F-statistic: 815.9 on 4 and 49 DF,  p-value: < 2.2e-16
    log.lm2 <- lm(logADR37 ~ logADR27 + logT867 + logH505, data=logdata)
    summary(log.lm2)
    #>
    #> Call:
    #> lm(formula = logADR37 ~ logADR27 + logT867 + logH505, data = logdata)
    #>
    #> Residuals:
    #>       Min        1Q    Median        3Q       Max
    #> -0.157932 -0.039221 -0.004817  0.038672  0.157626
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -0.57484    0.06231  -9.226 2.25e-12 ***
    #> logADR27     0.69588    0.05289  13.157  < 2e-16 ***
    #> logT867      0.24471    0.03275   7.472 1.10e-09 ***
    #> logH505      0.09030    0.01610   5.608 8.85e-07 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.06043 on 50 degrees of freedom
    #> Multiple R-squared:  0.9849, Adjusted R-squared:  0.984
    #> F-statistic:  1086 on 3 and 50 DF,  p-value: < 2.2e-16
    plot(log.lm2$fit, log.lm2$res,xlab="Fitted values",ylab="Residuals", main="Anscombe plot")
    abline(h=0)
    qqnorm(log.lm2$res,main="Normal probability plot", col=2)
    qqline(log.lm2$res, col="blue")

    par(old.par)
    #####################################
    # Multicollinearity Analysis
    ######################################
    mod.adr27 <-  lm(logADR27 ~ logT867 + logCS505 + logH505, data=logdata)
    summary(mod.adr27) # Multiple R^2 = 0.9936,
    #>
    #> Call:
    #> lm(formula = logADR27 ~ logT867 + logCS505 + logH505, data = logdata)
    #>
    #> Residuals:
    #>       Min        1Q    Median        3Q       Max
    #> -0.060363 -0.020065 -0.005929  0.015471  0.153280
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -1.77188    0.02975  -59.56  < 2e-16 ***
    #> logT867      0.36474    0.01052   34.66  < 2e-16 ***
    #> logCS505     0.65020    0.02063   31.52  < 2e-16 ***
    #> logH505     -0.02901    0.01007   -2.88  0.00584 **
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.03537 on 50 degrees of freedom
    #> Multiple R-squared:  0.9936, Adjusted R-squared:  0.9932
    #> F-statistic:  2589 on 3 and 50 DF,  p-value: < 2.2e-16
    mod.t867 <-  lm(logT867 ~ logADR27 + logH505 + logCS505, data=logdata)
    summary(mod.t867) # Multiple R^2 = 0.977,
    #>
    #> Call:
    #> lm(formula = logT867 ~ logADR27 + logH505 + logCS505, data = logdata)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -0.46512 -0.04505  0.01989  0.05619  0.16509
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  4.64307    0.16840  27.572   <2e-16 ***
    #> logADR27     2.63215    0.07594  34.663   <2e-16 ***
    #> logH505      0.07192    0.02739   2.626   0.0114 *
    #> logCS505    -1.66719    0.09218 -18.086   <2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.09503 on 50 degrees of freedom
    #> Multiple R-squared:  0.977,  Adjusted R-squared:  0.9757
    #> F-statistic: 709.1 on 3 and 50 DF,  p-value: < 2.2e-16
    mod.cs505 <-  lm(logCS505 ~ logADR27 + logH505 + logT867, data=logdata)
    summary(mod.cs505) # Multiple R^2 = 0.9837,
    #>
    #> Call:
    #> lm(formula = logCS505 ~ logADR27 + logH505 + logT867, data = logdata)
    #>
    #> Residuals:
    #>       Min        1Q    Median        3Q       Max
    #> -0.197444 -0.025126  0.008913  0.028843  0.104478
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  2.64960    0.05473  48.411  < 2e-16 ***
    #> logADR27     1.46430    0.04646  31.519  < 2e-16 ***
    #> logH505      0.05760    0.01415   4.072 0.000166 ***
    #> logT867     -0.52029    0.02877 -18.086  < 2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.05309 on 50 degrees of freedom
    #> Multiple R-squared:  0.9837, Adjusted R-squared:  0.9828
    #> F-statistic:  1008 on 3 and 50 DF,  p-value: < 2.2e-16
    mod.h505 <-  lm(logH505 ~ logADR27 + logCS505 + logT867, data=logdata)
    summary(mod.h505) # Multiple R^2 = 0.5784,
    #>
    #> Call:
    #> lm(formula = logH505 ~ logADR27 + logCS505 + logT867, data = logdata)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -1.50570 -0.11784  0.07984  0.24783  0.85079
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  -9.3778     3.0009  -3.125 0.002959 **
    #> logADR27     -4.9041     1.7029  -2.880 0.005844 **
    #> logCS505      4.3236     1.0618   4.072 0.000166 ***
    #> logT867       1.6848     0.6417   2.626 0.011444 *
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.4599 on 50 degrees of freedom
    #> Multiple R-squared:  0.5784, Adjusted R-squared:  0.5531
    #> F-statistic: 22.87 on 3 and 50 DF,  p-value: 1.849e-09
    # Variance inflation factors
    vifs <- c(0.9936, 0.977, 0.9837, 0.5784)
    vifs <- 1/(1-vifs)
    #Condition numbers
    X <- logdata
    # X is a copy of logdata
    X[,1] <- 1
    # the first column of X is 1
    # this is for the intercept
    X <- as.matrix(X)
    # Coerces X to be a matrix
    xtx <- t(X) %*% X # Gives X^T X
    eigenvalues <- eigen(xtx)$values
    kappa <- max(eigenvalues)/min(eigenvalues)
    kappa <- sqrt(kappa)
    # kappa = 244 is much LARGER than 30!

    ### Validation statistic
    # Fit the log.lm2 model with the first 45 observations
    # use the fitted model to predict the remaining 9 observations
    # Calculate the mean square error validation statistic
    log.lmsub <- lm(logADR37 ~ logADR27 + logT867 + logH505, data=logdata, subset=1:45)
    # Now predict all 54 observations using the fitted model
    mod.pred <- predict(log.lmsub, logdata, se.fit=TRUE)
    mod.pred$fit # provides all the 54 predicted values
    #>            1            2            3            4            5            6
    #>  0.869733716  0.751945766  0.464377364  0.065106368 -0.750908661  0.676749888
    #>            7            8            9           10           11           12
    #>  0.863161161  0.533123773  0.724024242  0.101489732  0.758165626 -0.009235144
    #>           13           14           15           16           17           18
    #>  0.262467812  0.499359749  0.896844370 -0.512513262  0.860758626  0.288778088
    #>           19           20           21           22           23           24
    #>  0.638085289  0.691680440  0.047967101 -0.125585969  0.542661299 -0.298423547
    #>           25           26           27           28           29           30
    #> -0.607263492  0.529236907  0.296110240  0.476668407 -0.343488224  0.286358929
    #>           31           32           33           34           35           36
    #>  0.732443474  0.740078857 -0.002182982  0.421013296 -0.201423341  0.002081194
    #>           37           38           39           40           41           42
    #>  0.782644479 -0.305494632 -0.978948479  0.130633957  0.245486987  0.071403922
    #>           43           44           45           46           47           48
    #>  0.781436122  0.341921707  0.195907253  0.425131377 -0.094842350  0.754611045
    #>           49           50           51           52           53           54
    #>  0.444664924  0.353051544  0.805738645 -0.303182437 -0.356706826  1.052803842
    logdata$pred <- mod.pred$fit
    # Get only last 9
    a <- logdata[46:54, ]
    validation.residuals <- a$logADR37 - a$pred
    validation.stat <- mean(validation.residuals^2)
    validation.stat
    #> [1] 0.005814136

## 2.9 `err_age` : Error in guessing ages from photographs

This data set contains the errors in guessing ages of 10 Southampton mathematicians. This data set has been used as an exercise in obtaining summary statistics and performing exploratory data analysis in Chapter 2 of the book Sahu (2023).

    summary(err_age)
    #>      group         size          females      photo          sex
    #>  Min.   : 1   Min.   :2.000   Min.   :0   Min.   : 1.0   Length:550
    #>  1st Qu.:14   1st Qu.:2.000   1st Qu.:0   1st Qu.: 3.0   Class :character
    #>  Median :28   Median :3.000   Median :1   Median : 5.5   Mode  :character
    #>  Mean   :28   Mean   :2.727   Mean   :1   Mean   : 5.5
    #>  3rd Qu.:42   3rd Qu.:3.000   3rd Qu.:2   3rd Qu.: 8.0
    #>  Max.   :55   Max.   :3.000   Max.   :3   Max.   :10.0
    #>      race              est_age         tru_age         error
    #>  Length:550         Min.   :15.00   Min.   :22.0   Min.   :-23.0000
    #>  Class :character   1st Qu.:23.00   1st Qu.:22.0   1st Qu.: -4.0000
    #>  Mode  :character   Median :33.00   Median :35.5   Median :  0.0000
    #>                     Mean   :38.56   Mean   :39.5   Mean   : -0.9436
    #>                     3rd Qu.:55.00   3rd Qu.:55.0   3rd Qu.:  3.0000
    #>                     Max.   :80.00   Max.   :73.0   Max.   : 18.0000
    #>    abs_error
    #>  Min.   : 0.000
    #>  1st Qu.: 2.000
    #>  Median : 3.000
    #>  Mean   : 5.336
    #>  3rd Qu.: 7.000
    #>  Max.   :23.000

## 2.10 `ffood` : Service times in a fast food restaurant

Service (waiting) times (in seconds) of customers at a fast-food restaurant. Source: Unknown.

    summary(ffood)
    #>        AM               PM
    #>  Min.   : 38.00   Min.   :45.00
    #>  1st Qu.: 53.75   1st Qu.:59.75
    #>  Median : 63.50   Median :67.00
    #>  Mean   : 68.90   Mean   :66.80
    #>  3rd Qu.: 83.75   3rd Qu.:74.25
    #>  Max.   :107.00   Max.   :88.00
     # 95% Confidence interval for the mean waiting time usig t-distribution
     a <- c(ffood$AM, ffood$PM)
     mean(a) + c(-1, 1) * qt(0.975, df=19) * sqrt(var(a))/sqrt(20)
    #> [1] 59.25467 76.44533
     # Two sample t-test for the difference between morning and afternoon times
     t.test(ffood$AM, ffood$PM)
    #>
    #>  Welch Two Sample t-test
    #>
    #> data:  ffood$AM and ffood$PM
    #> t = 0.24929, df = 14.201, p-value = 0.8067
    #> alternative hypothesis: true difference in means is not equal to 0
    #> 95 percent confidence interval:
    #>  -15.9434  20.1434
    #> sample estimates:
    #> mean of x mean of y
    #>      68.9      66.8

## 2.11 `gasmileage` : Gas mileage of cars

This data set contains gas mileage obtained from four models of a car. This data set has been used to illustrate the concepts of analysis of variance
in Chapter 19 of the book Sahu (2023).

    summary(gasmileage)
    #>     mileage      model
    #>  Min.   :22.00   A:2
    #>  1st Qu.:24.00   B:4
    #>  Median :27.00   C:3
    #>  Mean   :26.55   D:2
    #>  3rd Qu.:28.50
    #>  Max.   :32.00
    y <- c(22, 26,  28, 24, 29,   29, 32, 28,  23, 24)
    xx <- c(1,1,2,2,2,3,3,3,4,4)
    # Plot the observations
    plot(xx, y, col="red", pch="*", xlab="Model", ylab="Mileage")

    # Method1: Hand calculation
    ni <- c(2, 3, 3, 2)
    means <- tapply(y, xx, mean)
    vars <- tapply(y, xx, var)
    round(rbind(means, vars), 2)
    #>        1  2     3    4
    #> means 24 27 29.67 23.5
    #> vars   8  7  4.33  0.5
    sum(y^2) # gives 7115
    #> [1] 7115
    totalSS <- sum(y^2) - 10 * (mean(y))^2 # gives 92.5
    RSSf <- sum(vars*(ni-1)) # gives 31.17
    groupSS <- totalSS - RSSf # gives 61.3331.17/6
    meangroupSS <- groupSS/3 # gives 20.44
    meanErrorSS <- RSSf/6 # gives 5.194
    Fvalue <- meangroupSS/meanErrorSS # gives 3.936
    pvalue <- 1-pf(Fvalue, df1=3, df2=6)

    #### Method 2: Illustrate using dummy variables
    #################################
    #Create the design matrix X for the full regression model
    g <- 4
    n1 <- 2
    n2 <- 3
    n3 <- 3
    n4 <- 2
    n <- n1+n2+n3+n4
    X <- matrix(0, ncol=g, nrow=n)       #Set X as a zero matrix initially
    X[1:n1,1] <- 1    #Determine the first column of X
    X[(n1+1):(n1+n2),2] <- 1   #the 2nd column
    X[(n1+n2+1):(n1+n2+n3),3] <- 1    #the 3rd
    X[(n1+n2+n3+1):(n1+n2+n3+n4),4] <- 1    #the 4th
    #################################
    ####Fitting the  full model####
    #################################
    #Estimation
    XtXinv <- solve(t(X)%*%X)
    betahat <- XtXinv %*%t(X)%*%y   #Estimation of the coefficients
    Yhat <- X%*%betahat   #Fitted Y values
    Resids <- y - Yhat   #Residuals
    SSE <- sum(Resids^2)   #Error sum of squares
    S2hat <- SSE/(n-g)   #Estimation of sigma-square; mean square for error
    Sigmahat <- sqrt(S2hat)

    ##############################################################
    ####Fitting the reduced model -- the 4 means are equal #####
    ##############################################################
    Xr <- matrix(1, ncol=1, nrow=n)
    kr <- dim(Xr)[2]
    #Estimation
    Varr <- solve(t(Xr)%*%Xr)
    hbetar <- solve(t(Xr)%*%Xr)%*%t(Xr)%*% y   #Estimation of the coefficients
    hYr = Xr%*%hbetar   #Fitted Y values
    Resir <- y - hYr   #Residuals
    SSEr <- sum(Resir^2)   #Total sum of squares
    ###################################################################
    ####F-test for comparing the reduced model with the full model ####
    ###################################################################
    FStat <- ((SSEr-SSE)/(g-kr))/(SSE/(n-g))  #The test statistic of the F-test
    alpha <- 0.05
    Critical_value_F <- qf(1-alpha, g-kr,n-g)  #The critical constant of F-test
    pvalue_F <- 1-pf(FStat,g-kr, n-g)   #p-value of F-test

    modelA <- c(22, 26)
    modelB <- c(28, 24, 29)
    modelC <- c(29, 32, 28)
    modelD <- c(23, 24)

    SSerror = sum( (modelA-mean(modelA))^2 ) + sum( (modelB-mean(modelB))^2 )
    + sum( (modelC-mean(modelC))^2 ) + sum( (modelD-mean(modelD))^2 )
    #> [1] 9.166667
    SStotal <-  sum( (y-mean(y))^2 )
    SSgroup <- SStotal-SSerror

    ####
    #### Method 3: Use the  built-in function lm directly

    #####################################
    aa <- "modelA"
    bb <- "modelB"
    cc <- "modelC"
    dd <- "modelD"
    Expl <- c(aa,aa,bb,bb,bb,cc,cc,cc,dd,dd)
    is.factor(Expl)
    #> [1] FALSE
    Expl <- factor(Expl)
    model1 <- lm(y~Expl)
    summary(model1)
    #>
    #> Call:
    #> lm(formula = y ~ Expl)
    #>
    #> Residuals:
    #>    Min     1Q Median     3Q    Max
    #> -3.000 -1.417  0.000  1.750  2.333
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)   24.000      1.612  14.892 5.77e-06 ***
    #> ExplmodelB     3.000      2.081   1.442   0.1994
    #> ExplmodelC     5.667      2.081   2.724   0.0345 *
    #> ExplmodelD    -0.500      2.279  -0.219   0.8336
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 2.279 on 6 degrees of freedom
    #> Multiple R-squared:  0.6631, Adjusted R-squared:  0.4946
    #> F-statistic: 3.936 on 3 and 6 DF,  p-value: 0.07227
    anova(model1)
    #> Analysis of Variance Table
    #>
    #> Response: y
    #>           Df Sum Sq Mean Sq F value  Pr(>F)
    #> Expl       3 61.333 20.4444  3.9358 0.07227 .
    #> Residuals  6 31.167  5.1944
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    ###Alternatively ###

    xxf <- factor(xx)
    is.factor(xxf)
    #> [1] TRUE
    model2 <- lm(y~xxf)
    summary(model2)
    #>
    #> Call:
    #> lm(formula = y ~ xxf)
    #>
    #> Residuals:
    #>    Min     1Q Median     3Q    Max
    #> -3.000 -1.417  0.000  1.750  2.333
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)   24.000      1.612  14.892 5.77e-06 ***
    #> xxf2           3.000      2.081   1.442   0.1994
    #> xxf3           5.667      2.081   2.724   0.0345 *
    #> xxf4          -0.500      2.279  -0.219   0.8336
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 2.279 on 6 degrees of freedom
    #> Multiple R-squared:  0.6631, Adjusted R-squared:  0.4946
    #> F-statistic: 3.936 on 3 and 6 DF,  p-value: 0.07227
    anova(model2)
    #> Analysis of Variance Table
    #>
    #> Response: y
    #>           Df Sum Sq Mean Sq F value  Pr(>F)
    #> xxf        3 61.333 20.4444  3.9358 0.07227 .
    #> Residuals  6 31.167  5.1944
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 2.12 `possum` : Body weight and length of possums in Australian regions

This data set has been used to illustrate multiple linear regression modeling and analysis of variance in Chapter 19 of the book Sahu (2023).

The data set contains body weight and length of possums (tree living furry animals who are mostly nocturnal (marsupial) caught in 7 different regions of Australia. Source: Lindenmayer and Donnelly (1995). (Lindenmayer and Donnelly 1995)

     head(possum)
    #>   Body_Weight Length Location
    #> 1         3.5   89.0        1
    #> 2         3.3   91.5        1
    #> 3         3.5   95.5        1
    #> 4         3.1   92.0        1
    #> 5         2.8   85.5        1
    #> 6         3.0   90.5        1
     dim(possum)
    #> [1] 101   3
     summary(possum)
    #>   Body_Weight       Length         Location
    #>  Min.   :1.80   Min.   :75.00   Min.   :1.000
    #>  1st Qu.:2.60   1st Qu.:84.00   1st Qu.:1.000
    #>  Median :2.80   Median :88.00   Median :3.000
    #>  Mean   :2.88   Mean   :87.16   Mean   :3.604
    #>  3rd Qu.:3.20   3rd Qu.:90.00   3rd Qu.:6.000
    #>  Max.   :4.20   Max.   :96.50   Max.   :7.000
     ## Code lines used in the book
     ## Create a new data set
     poss <- possum
     poss$region <- factor(poss$Location)
     levels(poss$region) <- c("W.A", "S.A", "N.T", "QuL", "NSW", "Vic", "Tas")
     colnames(poss)<-c("y","z","Location", "x")
     head(poss)
    #>     y    z Location   x
    #> 1 3.5 89.0        1 W.A
    #> 2 3.3 91.5        1 W.A
    #> 3 3.5 95.5        1 W.A
    #> 4 3.1 92.0        1 W.A
    #> 5 2.8 85.5        1 W.A
    #> 6 3.0 90.5        1 W.A
     # Draw side by side boxplots
     boxplot(y~x, data=poss, col=2:8, xlab="region", ylab="Weight")

     # Obtain scatter plot
     # Start with a skeleton plot
     plot(poss$z, poss$y, type="n", xlab="Length", ylab="Weight")
     # Add points for the seven regions
     for (i in 1:7) {
        points(poss$z[poss$Location==i],poss$y[poss$Location==i],type="p", pch=as.character(i), col=i)
        }
    ## Add legends
     legend(x=76, y=4.2, legend=paste(as.character(1:7), levels(poss$x)),  lty=1:7, col=1:7)

     # Start  modelling
     #Fit the model with interaction.
     poss.lm1<-lm(y~z+x+z:x,data=poss)
     summary(poss.lm1)
    #>
    #> Call:
    #> lm(formula = y ~ z + x + z:x, data = poss)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -0.67953 -0.15793  0.01999  0.14591  0.78255
    #>
    #> Coefficients:
    #>               Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -3.5842639  1.5008154  -2.388   0.0191 *
    #> z            0.0748795  0.0167202   4.478 2.27e-05 ***
    #> xS.A         0.3283443  2.3839136   0.138   0.8908
    #> xN.T         6.0527549  3.3898871   1.786   0.0777 .
    #> xQuL        -5.9670098  4.5482536  -1.312   0.1930
    #> xNSW        -2.4035332  2.7983463  -0.859   0.3927
    #> xVic         0.8984502  2.4135144   0.372   0.7106
    #> xTas        -0.1263706  2.4142962  -0.052   0.9584
    #> z:xS.A      -0.0031036  0.0280484  -0.111   0.9121
    #> z:xN.T      -0.0675469  0.0383301  -1.762   0.0815 .
    #> z:xQuL       0.0663129  0.0494622   1.341   0.1835
    #> z:xNSW       0.0256881  0.0318912   0.805   0.4227
    #> z:xVic      -0.0141738  0.0279034  -0.508   0.6128
    #> z:xTas      -0.0003129  0.0276672  -0.011   0.9910
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.2828 on 87 degrees of freedom
    #> Multiple R-squared:  0.6731, Adjusted R-squared:  0.6243
    #> F-statistic: 13.78 on 13 and 87 DF,  p-value: 4.8e-16
     plot(poss$z, poss$y,type="n", xlab="Length", ylab="Weight")
     for (i in 1:7) {
     lines(poss$z[poss$Location==i],poss.lm1$fit[poss$Location==i],type="l",
     lty=i, col=i, lwd=1.8)
     points(poss$z[poss$Location==i],poss$y[poss$Location==i],type="p",
     pch=as.character(i), col=i)
     }
     poss.lm0 <- lm(y~z,data=poss)
     abline(poss.lm0, lwd=3, col=9)
     # Has drawn the seven parallel regression lines
     legend(x=76, y=4.2, legend=paste(as.character(1:7), levels(poss$x)),
     lty=1:7, col=1:7)

     n <- length(possum$Body_Weight)
     # Wrong model since Location is not a numeric covariate
     wrong.lm <- lm(Body_Weight~Location, data=possum)
     summary(wrong.lm)
    #>
    #> Call:
    #> lm(formula = Body_Weight ~ Location, data = possum)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -1.20753 -0.21060  0.01309  0.21309  1.35124
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  3.16630    0.07740  40.910  < 2e-16 ***
    #> Location    -0.07939    0.01801  -4.409 2.64e-05 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.4239 on 99 degrees of freedom
    #> Multiple R-squared:  0.1641, Adjusted R-squared:  0.1557
    #> F-statistic: 19.44 on 1 and 99 DF,  p-value: 2.643e-05

     nis <- table(possum$Location)
     meanwts <- tapply(possum$Body_Weight, possum$Location, mean)
     varwts <- tapply(possum$Body_Weight, possum$Location, var)
     datasums <- data.frame(nis=nis, mean=meanwts, var=varwts)
     datasums <- data.frame(nis=nis, mean=meanwts, var=varwts)
     modelss <- sum(datasums[,2] * (meanwts - mean(meanwts))^2)
     residss <- sum( (datasums[,2] - 1) * varwts)

     fvalue <- (modelss/6) / (residss/94)
     fcritical <- qf(0.95, df1= 6, df2=94)
     x <- seq(from=0, to=12, length=200)
     y <- df(x, df1=6, df2=94)
     plot(x, y, type="l", xlab="", ylab="Density of F(6, 94)", col=4)
     abline(v=fcritical, lty=3, col=3)
     abline(v=fvalue, lty=2, col=2)

     pvalue <- 1-pf(fvalue, df1=6, df2=94)

     ### Doing the above in R
     # Convert  the Location column to a factor

     localpossum <- possum
     localpossum$Location <- as.factor(localpossum$Location)
     summary(localpossum)  # Now Location is a factor
    #>   Body_Weight       Length      Location
    #>  Min.   :1.80   Min.   :75.00   1:33
    #>  1st Qu.:2.60   1st Qu.:84.00   2:12
    #>  Median :2.80   Median :88.00   3: 7
    #>  Mean   :2.88   Mean   :87.16   4: 6
    #>  3rd Qu.:3.20   3rd Qu.:90.00   5:13
    #>  Max.   :4.20   Max.   :96.50   6:13
    #>                                 7:17

     # Put the identifiability constraint:
     options(contrasts=c("contr.treatment", "contr.poly"))
     # Change to have easier column names
     colnames(localpossum) <- c("y", "z", "x")
     # Fit model M1
     possum.lm1 <- lm(y~x, data=localpossum)
     summary(possum.lm1)
    #>
    #> Call:
    #> lm(formula = y ~ x, data = localpossum)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -0.84167 -0.23333  0.01765  0.21765  0.96667
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  3.13333    0.06402  48.943  < 2e-16 ***
    #> x2          -0.49167    0.12397  -3.966 0.000143 ***
    #> x3          -0.01905    0.15304  -0.124 0.901214
    #> x4           0.33333    0.16322   2.042 0.043929 *
    #> x5          -0.37949    0.12043  -3.151 0.002182 **
    #> x6          -0.68718    0.12043  -5.706 1.34e-07 ***
    #> x7          -0.45098    0.10979  -4.108 8.53e-05 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.3678 on 94 degrees of freedom
    #> Multiple R-squared:  0.4026, Adjusted R-squared:  0.3644
    #> F-statistic: 10.56 on 6 and 94 DF,  p-value: 6.204e-09
     anova(possum.lm1)
    #> Analysis of Variance Table
    #>
    #> Response: y
    #>           Df  Sum Sq Mean Sq F value    Pr(>F)
    #> x          6  8.5667 1.42778  10.556 6.204e-09 ***
    #> Residuals 94 12.7137 0.13525
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
     possum.lm2 <- lm(y~z, data=localpossum)
     summary(possum.lm2)
    #>
    #> Call:
    #> lm(formula = y ~ z, data = localpossum)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -0.77843 -0.19072 -0.02632  0.20928  0.82708
    #>
    #> Coefficients:
    #>              Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -4.284148   0.633108  -6.767 9.36e-10 ***
    #> z            0.082202   0.007256  11.329  < 2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.3059 on 99 degrees of freedom
    #> Multiple R-squared:  0.5646, Adjusted R-squared:  0.5602
    #> F-statistic: 128.4 on 1 and 99 DF,  p-value: < 2.2e-16
     anova(possum.lm2)
    #> Analysis of Variance Table
    #>
    #> Response: y
    #>           Df  Sum Sq Mean Sq F value    Pr(>F)
    #> z          1 12.0139 12.0139  128.35 < 2.2e-16 ***
    #> Residuals 99  9.2665  0.0936
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
     # Include both location and length but no interaction
     possum.lm3 <-  lm(y~x+z, data=localpossum)
     summary(possum.lm3)
    #>
    #> Call:
    #> lm(formula = y ~ x + z, data = localpossum)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -0.67242 -0.19970  0.01845  0.17516  0.78209
    #>
    #> Coefficients:
    #>              Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -3.390403   0.816320  -4.153 7.27e-05 ***
    #> x2           0.057028   0.117868   0.484  0.62964
    #> x3           0.100261   0.119312   0.840  0.40288
    #> x4           0.152418   0.128260   1.188  0.23772
    #> x5          -0.176672   0.096536  -1.830  0.07044 .
    #> x6          -0.310958   0.104334  -2.980  0.00367 **
    #> x7          -0.161791   0.092289  -1.753  0.08288 .
    #> z            0.072719   0.009083   8.006 3.29e-12 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.2845 on 93 degrees of freedom
    #> Multiple R-squared:  0.6463, Adjusted R-squared:  0.6197
    #> F-statistic: 24.28 on 7 and 93 DF,  p-value: < 2.2e-16
     anova(possum.lm3)
    #> Analysis of Variance Table
    #>
    #> Response: y
    #>           Df Sum Sq Mean Sq F value    Pr(>F)
    #> x          6 8.5667  1.4278  17.643 1.521e-13 ***
    #> z          1 5.1876  5.1876  64.102 3.287e-12 ***
    #> Residuals 93 7.5262  0.0809
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
     # Include interaction effect
     possum.lm4 <-  lm(y~x+z+x:z, data=localpossum)
     summary(possum.lm4)
    #>
    #> Call:
    #> lm(formula = y ~ x + z + x:z, data = localpossum)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -0.67953 -0.15793  0.01999  0.14591  0.78255
    #>
    #> Coefficients:
    #>               Estimate Std. Error t value Pr(>|t|)
    #> (Intercept) -3.5842639  1.5008154  -2.388   0.0191 *
    #> x2           0.3283443  2.3839136   0.138   0.8908
    #> x3           6.0527549  3.3898871   1.786   0.0777 .
    #> x4          -5.9670098  4.5482536  -1.312   0.1930
    #> x5          -2.4035332  2.7983463  -0.859   0.3927
    #> x6           0.8984502  2.4135144   0.372   0.7106
    #> x7          -0.1263706  2.4142962  -0.052   0.9584
    #> z            0.0748795  0.0167202   4.478 2.27e-05 ***
    #> x2:z        -0.0031036  0.0280484  -0.111   0.9121
    #> x3:z        -0.0675469  0.0383301  -1.762   0.0815 .
    #> x4:z         0.0663129  0.0494622   1.341   0.1835
    #> x5:z         0.0256881  0.0318912   0.805   0.4227
    #> x6:z        -0.0141738  0.0279034  -0.508   0.6128
    #> x7:z        -0.0003129  0.0276672  -0.011   0.9910
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.2828 on 87 degrees of freedom
    #> Multiple R-squared:  0.6731, Adjusted R-squared:  0.6243
    #> F-statistic: 13.78 on 13 and 87 DF,  p-value: 4.8e-16
     anova(possum.lm4)
    #> Analysis of Variance Table
    #>
    #> Response: y
    #>           Df Sum Sq Mean Sq F value    Pr(>F)
    #> x          6 8.5667  1.4278 17.8561 2.194e-13 ***
    #> z          1 5.1876  5.1876 64.8768 3.833e-12 ***
    #> x:z        6 0.5696  0.0949  1.1873    0.3206
    #> Residuals 87 6.9565  0.0800
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
     anova(possum.lm2, possum.lm3)
    #> Analysis of Variance Table
    #>
    #> Model 1: y ~ z
    #> Model 2: y ~ x + z
    #>   Res.Df    RSS Df Sum of Sq      F   Pr(>F)
    #> 1     99 9.2665
    #> 2     93 7.5262  6    1.7404 3.5842 0.003065 **
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
     #Check the diagnostics for M3
     plot(possum.lm3$fit, possum.lm3$res,xlab="Fitted values",ylab="Residuals",
     main="Anscombe plot")
     abline(h=0)

     qqnorm(possum.lm3$res,main="Normal probability plot", col=2)
     qqline(possum.lm3$res, col="blue")

     rm(localpossum)
     rm(poss)

## 2.13 `puffin` : Nesting habits of puffins in Newfoundland

This object contains data regarding nesting habits of common puffin. Source: Nettleship (1972). (Nettleship 1972) This data set has been used to illustrate multiple linear regression modeling in Chapter 18 of the book Sahu (2023).

    head(puffin)
    #>   Nesting_Frequency Grass_Cover Mean_Soil_Depth Slope_Angle Distance_from_Edge
    #> 1                 0          15            27.8           8                 45
    #> 2                 0           0            41.9           8                 54
    #> 3                 0          20            36.8           5                 60
    #> 4                 0          30            37.7           8                 42
    #> 5                 0          75            45.5           5                 48
    #> 6                 0          15            51.4           8                 54
    dim(puffin)
    #> [1] 38  5
    summary(puffin)
    #>  Nesting_Frequency  Grass_Cover    Mean_Soil_Depth  Slope_Angle
    #>  Min.   : 0.000    Min.   : 0.00   Min.   :24.30   Min.   : 2.00
    #>  1st Qu.: 0.000    1st Qu.:40.00   1st Qu.:32.75   1st Qu.: 7.25
    #>  Median : 7.500    Median :60.00   Median :37.40   Median :10.00
    #>  Mean   : 7.684    Mean   :56.45   Mean   :37.72   Mean   :15.00
    #>  3rd Qu.:12.750    3rd Qu.:80.00   3rd Qu.:42.83   3rd Qu.:21.25
    #>  Max.   :25.000    Max.   :95.00   Max.   :51.40   Max.   :38.00
    #>  Distance_from_Edge
    #>  Min.   : 3.00
    #>  1st Qu.:18.00
    #>  Median :30.00
    #>  Mean   :30.39
    #>  3rd Qu.:44.25
    #>  Max.   :60.00
    pairs(puffin)

    puffin$sqrtfreq <- sqrt(puffin$Nesting_Frequency)
    puff.sqlm <- lm(sqrtfreq~ Grass_Cover + Mean_Soil_Depth + Slope_Angle
    +Distance_from_Edge, data=puffin)
    old.par <- par(no.readonly = TRUE)
    par(mfrow=c(2,1))
    qqnorm(puff.sqlm$res,main="Normal probability plot", col=2)
    qqline(puff.sqlm$res, col="blue")
    plot(puff.sqlm$fit, puff.sqlm$res,xlab="Fitted values",ylab="Residuals",
    main="Anscombe plot", col="red")
    abline(h=0)

    summary(puff.sqlm)
    #>
    #> Call:
    #> lm(formula = sqrtfreq ~ Grass_Cover + Mean_Soil_Depth + Slope_Angle +
    #>     Distance_from_Edge, data = puffin)
    #>
    #> Residuals:
    #>      Min       1Q   Median       3Q      Max
    #> -1.17058 -0.28555  0.07289  0.43606  1.08192
    #>
    #> Coefficients:
    #>                      Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)         4.4176153  0.7479664   5.906 1.27e-06 ***
    #> Grass_Cover         0.0004179  0.0045698   0.091   0.9277
    #> Mean_Soil_Depth     0.0486374  0.0181385   2.681   0.0114 *
    #> Slope_Angle        -0.0318777  0.0182695  -1.745   0.0903 .
    #> Distance_from_Edge -0.1189727  0.0134970  -8.815 3.45e-10 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 0.6217 on 33 degrees of freedom
    #> Multiple R-squared:  0.8853, Adjusted R-squared:  0.8714
    #> F-statistic: 63.67 on 4 and 33 DF,  p-value: 4.753e-15
    par(old.par)
    #####################################
    # F test for two betas at the  same time:
    ######################################
    puff.sqlm2 <- lm(sqrtfreq~ Mean_Soil_Depth + Distance_from_Edge, data=puffin)
    anova(puff.sqlm)
    #> Analysis of Variance Table
    #>
    #> Response: sqrtfreq
    #>                    Df Sum Sq Mean Sq F value    Pr(>F)
    #> Grass_Cover         1  7.307   7.307  18.904 0.0001241 ***
    #> Mean_Soil_Depth     1  0.820   0.820   2.122 0.1546449
    #> Slope_Angle         1 60.278  60.278 155.945 4.725e-14 ***
    #> Distance_from_Edge  1 30.034  30.034  77.700 3.453e-10 ***
    #> Residuals          33 12.756   0.387
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    anova(puff.sqlm2)
    #> Analysis of Variance Table
    #>
    #> Response: sqrtfreq
    #>                    Df Sum Sq Mean Sq  F value Pr(>F)
    #> Mean_Soil_Depth     1  0.513   0.513   1.2596 0.2694
    #> Distance_from_Edge  1 96.437  96.437 236.9547 <2e-16 ***
    #> Residuals          35 14.245   0.407
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    fval <-  1/2*(14.245-12.756)/0.387 # 1.924
    qf(0.95, 2, 33) # 3.28
    #> [1] 3.284918
    1-pf(fval, 2, 33) # 0.162
    #> [1] 0.1620835
    anova(puff.sqlm2, puff.sqlm)
    #> Analysis of Variance Table
    #>
    #> Model 1: sqrtfreq ~ Mean_Soil_Depth + Distance_from_Edge
    #> Model 2: sqrtfreq ~ Grass_Cover + Mean_Soil_Depth + Slope_Angle + Distance_from_Edge
    #>   Res.Df    RSS Df Sum of Sq     F Pr(>F)
    #> 1     35 14.245
    #> 2     33 12.756  2    1.4889 1.926 0.1618

## 2.14 `rice` : data set on rice yield

This data set has been used to illustrate multiple linear regression modeling in Chapter 18 of the book Sahu (2023). This data set has been obtained from the journal research article Bal and Ojha (1975). (Bal and Ojha 1975)

     summary(rice)
    #>      Yield           Days
    #>  Min.   :2508   Min.   :16.0
    #>  1st Qu.:3092   1st Qu.:23.5
    #>  Median :3318   Median :31.0
    #>  Mean   :3283   Mean   :31.0
    #>  3rd Qu.:3549   3rd Qu.:38.5
    #>  Max.   :3883   Max.   :46.0
     plot(rice$Days, rice$Yield, pch="*", xlab="Days", ylab="Yield")

     rice$daymin31 <- rice$Days-31
     rice.lm <- lm(Yield ~ daymin31, data=rice)
     summary(rice.lm)
    #>
    #> Call:
    #> lm(formula = Yield ~ daymin31, data = rice)
    #>
    #> Residuals:
    #>     Min      1Q  Median      3Q     Max
    #> -691.07 -217.65   45.85  271.77  612.14
    #>
    #> Coefficients:
    #>             Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)  3283.12     103.95  31.582 2.05e-14 ***
    #> daymin31       12.26      11.28   1.088    0.295
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 415.8 on 14 degrees of freedom
    #> Multiple R-squared:  0.07791,    Adjusted R-squared:  0.01205
    #> F-statistic: 1.183 on 1 and 14 DF,  p-value: 0.2951
     # Check the diagnostics
     plot(rice.lm$fit, rice.lm$res, xlab="Fitted values", ylab = "Residuals")
     abline(h=0)

     # Should be a random scatter
     # Needs a quadratic term

     qqnorm(rice.lm$res, col=2)
     qqline(rice.lm$res, col="blue")

     rice.lm2 <- lm(Yield ~ daymin31 + I(daymin31^2) , data=rice)
     old.par <- par(no.readonly = TRUE)
     par(mfrow=c(1, 2))
     plot(rice.lm2$fit, rice.lm2$res, xlab="Fitted values", ylab = "Residuals")
     abline(h=0)
     # Should be a random scatter
     # Much better plot!
     qqnorm(rice.lm2$res, col=2)
     qqline(rice.lm2$res, col="blue")

     summary(rice.lm2)
    #>
    #> Call:
    #> lm(formula = Yield ~ daymin31 + I(daymin31^2), data = rice)
    #>
    #> Residuals:
    #>     Min      1Q  Median      3Q     Max
    #> -303.96 -118.11   13.86  115.67  319.06
    #>
    #> Coefficients:
    #>                Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)   3668.6682    76.7086  47.826 5.33e-16 ***
    #> daymin31        12.2632     5.5286   2.218    0.045 *
    #> I(daymin31^2)   -4.5358     0.6744  -6.726 1.41e-05 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 203.9 on 13 degrees of freedom
    #> Multiple R-squared:  0.7942, Adjusted R-squared:  0.7625
    #> F-statistic: 25.08 on 2 and 13 DF,  p-value: 3.452e-05
     par(old.par) # par(mfrow=c(1,1))
     plot(rice$Days,  rice$Yield, xlab="Days", ylab="Yield")
     lines(rice$Days, rice.lm2$fit, lty=1, col=3)

     rice.lm3 <- lm(Yield ~ daymin31 + I(daymin31^2)+I(daymin31^3) , data=rice)
     #check the diagnostics
     summary(rice.lm3) # Will print the summary of the fitted model
    #>
    #> Call:
    #> lm(formula = Yield ~ daymin31 + I(daymin31^2) + I(daymin31^3),
    #>     data = rice)
    #>
    #> Residuals:
    #>     Min      1Q  Median      3Q     Max
    #> -281.97 -113.21   -6.11   97.75  330.92
    #>
    #> Coefficients:
    #>                 Estimate Std. Error t value Pr(>|t|)
    #> (Intercept)   3668.66815   79.32668  46.248 6.82e-15 ***
    #> daymin31        17.52493   14.49451   1.209     0.25
    #> I(daymin31^2)   -4.53580    0.69743  -6.504 2.92e-05 ***
    #> I(daymin31^3)   -0.03457    0.08751  -0.395     0.70
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #> Residual standard error: 210.8 on 12 degrees of freedom
    #> Multiple R-squared:  0.7968, Adjusted R-squared:  0.746
    #> F-statistic: 15.68 on 3 and 12 DF,  p-value: 0.0001876
     #### Predict at a new value of Days=31.1465

     # Create a new data set called new
     new <- data.frame(daymin31=32.1465-31)

     a <- predict(rice.lm2, newdata=new, se.fit=TRUE)
     # Confidence interval for the mean of rice yield  at day=31.1465
     a <- predict(rice.lm2, newdata=new, interval="confidence")
     a
    #>        fit      lwr      upr
    #> 1 3676.766 3511.904 3841.628
     #          fit      lwr      upr
     # [1,] 3676.766 3511.904 3841.628
     # Prediction interval for a future yield at day=31.1465
     b <- predict(rice.lm2, newdata=new, interval="prediction")
     b
    #>        fit      lwr      upr
    #> 1 3676.766 3206.461 4147.071
     # fit      lwr      upr
     #[1,] 3676.766 3206.461 4147.071

## 2.15 `wgain`: Weight gain of students starting college

This contains weight gain data from 68 first year students during their first 12 weeks in college. Source: Levitsky {} (2004). (Levitsky and Mrdjenovic 2004) This data set has been used to illustrate confidence intervals and \(t\)-tests
in part I of the book Sahu (2023).

    summary(wgain)
    #>     student         initial          final
    #>  Min.   : 1.00   Min.   :42.64   Min.   : 43.54
    #>  1st Qu.:17.75   1st Qu.:53.86   1st Qu.: 54.32
    #>  Median :34.50   Median :60.78   Median : 60.78
    #>  Mean   :34.50   Mean   :61.72   Mean   : 62.59
    #>  3rd Qu.:51.25   3rd Qu.:68.04   3rd Qu.: 68.49
    #>  Max.   :68.00   Max.   :99.79   Max.   :101.60
    # 95% Confidence interval for mean weight gain
    x <- wgain$final - wgain$initial
    mean(x) + c(-1, 1) * qt(0.975, df=67) * sqrt(var(x)/68)
    #> [1] 0.6334959 1.1008265
    # t-test to test the mean difference equals 0
    t.test(x)
    #>
    #>  One Sample t-test
    #>
    #> data:  x
    #> t = 7.4074, df = 67, p-value = 2.813e-10
    #> alternative hypothesis: true mean is not equal to 0
    #> 95 percent confidence interval:
    #>  0.6334959 1.1008265
    #> sample estimates:
    #> mean of x
    #> 0.8671612

# 3 Illustrated R functions

## 3.1 The butterfly function

This function draws a butterfly as on the front cover of the book. This is a plot obtained as follows. Initially a sequence of angles denoted by \(\theta\) is chosen in the range 0 to 24\(\pi\). Then, we specify two parameters \(a\) and \(b\) and evaluate the following equations. The illustrations show the effect of these two parameters on the shape of the resulting plot obtained by plotting \(x-y\) pairs. Successive points on the plot are joined by lines.
\[\begin{equation} r = \exp(\cos(\theta)) - a \cos(b \, \theta) + \sin\left(\frac{\theta}{12}\right) \\ x = r \sin(\theta) \\ y = -r \cos(\theta) \end{equation}\]

    butterfly(color = 6)

    old.par <- par(no.readonly = TRUE)
    par(mfrow=c(2, 2))
    butterfly(a=10, b=1.5, color = "seagreen")
    butterfly(color = 6)
    butterfly(a=5, b=5, color=2)
    butterfly(a=20, b=4, color = "blue")

    par(old.par) # par(mfrow=c(1, 1))

## 3.2 Simulation of the Monty Hall game

Figure 2: Monty Hall game

The function `?monty` simulates the famous Monty Hall game. This function is written by (Chivers 2012). The function takes the arguments: `strat`: short form for strategy which can take one of the three choices: * “stay” : Do not change the initial door chosen * “swap” : Swap the door chosen initially. * “random” : Randomly decide to stay or swap.

The other parameters are \(N\): How many games to play, defaults to 1000 and `print_games`, which is a logical argument that tells whether to print the results of each of the \(N\) games. Here are three illustrations of the games.

    monty("stay", print_games = FALSE)
    monty("switch", print_games = FALSE)
    monty("random", print_games = FALSE)

## 3.3 Functions illustrating the Central Limit Theorem (CLT)

There are two functions illustrating the CLT. The first one ‘?see_the_clt_for_Bernoulli’ simulates `nrep=10000` samples of size `nsize=10` with probability of success \(p=0.8\) by default. It then finds the means of the `nrep` samples and standardises the means by subtracting the overall mean and dividing by the sample standard deviation. It then draws a histogram of the sample means and superimposes the theoretical density of the standard normal distribution. The histogram will closely resemble the density of the standard normal distribution if the CLT approximation is good. The quality of the approximation depends on the sample size `nsize`. This can be observed from the illustrations provided below.

    a <- see_the_clt_for_Bernoulli()

    old.par <- par(no.readonly = TRUE)
    par(mfrow=c(2, 3))
    a30 <- see_the_clt_for_Bernoulli(nsize=30)
    a50 <- see_the_clt_for_Bernoulli(nsize=50)
    a100 <- see_the_clt_for_Bernoulli(nsize=100)
    a500 <- see_the_clt_for_Bernoulli(nsize=500)
    a1000 <- see_the_clt_for_Bernoulli(nsize=1000)
    a5000 <- see_the_clt_for_Bernoulli(nsize=5000)

    par(old.par)

The second function `?see_the_clt_for_uniform` demonstrates the CLT for sampling from the uniform distribution in the interval (0,1). As in the previous function for the Bernoulli distribution, this also takes two similar arguments and behaves very similarly as demonstrated below. But the sampling is done from the uniform distribution.

    a <- see_the_clt_for_uniform()

    old.par <- par(no.readonly = TRUE)
    par(mfrow=c(2, 3))
    a1 <- see_the_clt_for_uniform(nsize=1)
    a2 <- see_the_clt_for_uniform(nsize=2)
    a3 <- see_the_clt_for_uniform(nsize=5)
    a4 <- see_the_clt_for_uniform(nsize=10)
    a5 <- see_the_clt_for_uniform(nsize=20)
    a6 <- see_the_clt_for_uniform(nsize=50)

    par(old.par)
    ybars <- see_the_clt_for_uniform(nsize=12)

    zbars <- (ybars - mean(ybars))/sd(ybars)
    k <- 100
    u <- seq(from=min(zbars), to= max(zbars), length=k)
    ecdf <-  rep(NA, k)
    for(i in 1:k) ecdf[i] <- length(zbars[zbars<u[i]])/length(zbars)
    tcdf <- pnorm(u)
    plot(u, tcdf, type="l", col="red", lwd=4, xlab="", ylab="cdf")
    lines(u, ecdf, lty=2, col="darkgreen", lwd=4)
    symb <- c("cdf of sample means", "cdf of N(0, 1)")
    legend(x=-3.5, y=0.4, legend = symb, lty = c(2, 1),
    col = c("darkgreen","red"), bty="n")

This function also provides a plot of the estimated cumulative density function (cdf) of the standarised sample means and superimposes the cdf of the standard normal distribution. The CLT states that these cdf’s will be very close to each other as the sample size \(n \to \infty\).

## 3.4 Functions illustrating the Weak Law of Large Numbers (WLLN)

The function `?see_the_wlln_for_uniform` illustrates the WLLN for sampling from the uniform distribution in the interval (0, 1). Similar to the two functions demonstrating the CLT, this function takes two arguments `nsize` for sample size and `nrep` for number of replications. The function draws the histogram of the replicated sample means and draws an estimated density function of the samples. The below illustration shows that the estimated density gets more peaked as the sample size \(n\) increases. The WLLN says that the estimated density will gradually become just a spike as \(n \to \infty\).

    a1 <- see_the_wlln_for_uniform(nsize=1, nrep=50000)

    a2 <- see_the_wlln_for_uniform(nsize=10, nrep=50000)

    a3 <- see_the_wlln_for_uniform(nsize=50, nrep=50000)

    a4 <- see_the_wlln_for_uniform(nsize=100, nrep=50000)

    plot(a4, type="l", lwd=2, ylim=range(c(a1$y, a2$y, a3$y, a4$y)), col=1,
    lty=1, xlab="mean", ylab="density estimates")
    lines(a3, type="l", lwd=2, col=2, lty=2)
    lines(a2, type="l", lwd=2, col=3, lty=3)
    lines(a1, type="l", lwd=2, col=4, lty=4)
    symb <- c("n=1", "n=10", "n=50", "n=100")
    legend(x=0.37, y=11.5, legend = symb, lty =4:1, col = 4:1)

# 4 Discussion

The data sets included in this package and discussed in this vignette can be used in teaching and learning of introductory probability, statistics, `R` for data-based sciences with or without reading the book by the same author, (Sahu 2023). So that a beginner reader can understand the methods, and also being introductory in nature, this package has used very basic `R` commands and scripts throughout. But the package has illustrated the use of the advanced graphics package ggplot2 and function writing through the `butterfly` function. However, ipsRdbs does not intentionally teach R programming and does not expect the users to be proficient in adopting those techniques in their own data analysis and processing. It has been purposefully designed this way to interest the reader to further learn and use more advanced graphics and data manipulation and analysis capabilities of R.

# References

Bal, S., and T. P. Ojha. 1975. “Determination of Biological Maturity and Effect of Harvesting and Drying Conditions on Milling Quality of Paddy.” Journal of Agricultural Engineering Research 20 (4): 353–61. https://doi.org/https://doi.org/10.1016/0021-8634(75)90072-4.

Chivers, Corey. 2012. “Monty Hall by Simulation in R.” Posted in Probability, Rstats, Teaching.

David J. Hand, K. McConway, Fergus Daly, and E. Ostrowski. 1993. A Handbook of Small Data Sets. London: CRC Press.

Levitsky, Halbmaier, David, and Gordana. Mrdjenovic. 2004. “A Model for the Study of the Epidemic of Obesity.” International Journal of Obesity and Related Metabolic Disorders : Journal of the International Association for the Study of Obesity 28: 1435–42. https://doi.org/10.1038/sj.ijo.0802776.

Lindenmayer, Viggers, D. B., and C. F. Donnelly. 1995. “Morphological Variation Among Columns of the Mountain Brushtail Possum, Trichosurus Caninus Ogilby (Phalangeridae: Marsupiala).” Australian Journal of Zoology 43: 449–58.

Nettleship, David N. 1972. “Breeding Success of the Common Puffin (Fratercula Arctica L.) on Different Habitats at Great Island, Newfoundland.” Ecological Monographs 42: 239–68.

Sahu, Sujit K. 2023. Introduction to Probability, Statistics and R for Data-Based Sciences. 1st ed. London: Springer.

Shaw, L. P, and L. F. Shaw. 2019. “The Flying Bomb and the Actuary.” Significance Magazine 16: 12–17.