Motor Trend Analysis — Automatic vs Manual Transmission

mtcars <- mtcars %>% mutate(am = factor(am, labels = c("Automatic", "Manual")))
# summary by transmission

mtcars %>% group_by(am) %>% summarise(n = n(), mean_mpg = mean(mpg), sd_mpg = sd(mpg))

## # A tibble: 2 x 4
##   am            n mean_mpg sd_mpg
##   <fct>     <int>    <dbl>  <dbl>
## 1 Automatic    19     17.1   3.83
## 2 Manual       13     24.4   6.17

# boxplot

ggplot(mtcars, aes(x = am, y = mpg, fill = am)) +
geom_boxplot() +
labs(title = "MPG by Transmission Type", x = "Transmission", y = "MPG") +
theme_minimal() + theme(legend.position = "none")

fit_unadj <- lm(mpg ~ am, data = mtcars)
fit_adj   <- lm(mpg ~ am + wt + hp, data = mtcars)

# tidy outputs

tidy_unadj <- broom::tidy(fit_unadj, conf.int = TRUE)
tidy_adj   <- broom::tidy(fit_adj, conf.int = TRUE)

tidy_unadj

## # A tibble: 2 x 7
##   term        estimate std.error statistic  p.value conf.low conf.high
##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
## 1 (Intercept)    17.1       1.12     15.2  1.13e-15    14.9       19.4
## 2 amManual        7.24      1.76      4.11 2.85e- 4     3.64      10.8

tidy_adj

## # A tibble: 4 x 7
##   term        estimate std.error statistic  p.value conf.low conf.high
##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
## 1 (Intercept)  34.0      2.64        12.9  2.82e-13  28.6      39.4   
## 2 amManual      2.08     1.38         1.51 1.41e- 1  -0.736     4.90  
## 3 wt           -2.88     0.905       -3.18 3.57e- 3  -4.73     -1.02  
## 4 hp           -0.0375   0.00961     -3.90 5.46e- 4  -0.0572   -0.0178

# report the coefficient for Manual (reference = Automatic)

coef_unadj <- tidy_unadj %>% filter(term == "amManual")
coef_adj   <- tidy_adj   %>% filter(term == "amManual")
coef_unadj; coef_adj

## # A tibble: 1 x 7
##   term     estimate std.error statistic  p.value conf.low conf.high
##   <chr>       <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
## 1 amManual     7.24      1.76      4.11 0.000285     3.64      10.8

## # A tibble: 1 x 7
##   term     estimate std.error statistic p.value conf.low conf.high
##   <chr>       <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
## 1 amManual     2.08      1.38      1.51   0.141   -0.736      4.90

par(mfrow = c(2,2))
plot(fit_adj)

confint(fit_adj)["amManual", ]

##      2.5 %     97.5 % 
## -0.7357587  4.9031790

summary(fit_adj)$coefficients["amManual", c("Estimate","Std. Error","t value","Pr(>|t|)")]

##   Estimate Std. Error    t value   Pr(>|t|) 
##  2.0837101  1.3764202  1.5138620  0.1412682

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4) Save and Knit to HTML

1. Save file: File → Save (or Ctrl/Cmd+S) — name it: motor_trend_report.Rmd
   
2. Click the Knit button (the ball of yarn icon) at the top of the script editor.
   • RStudio will run every chunk and produce motor_trend_report.html and open it in Viewer.
3. Inspect the Executive summary and the Model outputs — copy the numeric estimate and CI into the executive summary sentence if you want exact numbers there.

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5) If you get package or code errors

• If a package is missing: run install.packages("packagename") in Console, then re-Knit.
• If a chunk fails, read the red error message — often it’s a typo or a missing comma. Ask me the error text and I’ll tell you the exact line to fix.

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6) Make the report ~2 pages

• The template I gave is compact; keep plots small (fig.width/height default).
  
• If your HTML is long, shorten the Appendix or move tables to Appendix only.
• If needed, convert to PDF: File → Knit to PDF (requires LaTeX) — not necessary.

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7) Publish & share (RPubs — easiest)

1. After Knit, the HTML opens in Viewer. On the top-right of Viewer click Publish → RPubs.
   
2. Log in (create an account if needed).
   
3. Add a title and click Publish.
   
4. Copy the generated RPubs link — that’s what you submit for the presentation/report.

If RPubs button is missing, click the small gear icon in Viewer or use rpubsUpload() from the rsconnect package (I can show exact command if needed).

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8) Optional: Push to GitHub (for graders who want code)

1. Create a GitHub repo (e.g., motor-trend-report).
   
2. Add these files: motor_trend_report.Rmd and the knitted motor_trend_report.html.
   
3. Commit & push (or use GitHub web UI → Upload files).
   
4. Paste the repo URL in your submission.

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9) What to write in the short report (copy-paste language)

Use the executive summary I suggested earlier, then fill in the exact numbers after you knit and see them. Keep main body ~1–2 pages; put long model outputs in Appendix.

Example conclusion sentence (paste and edit after you see numbers): > After adjusting for weight and horsepower, manual transmissions are associated with an estimated 1.8 MPG higher fuel efficiency compared to automatics (95% CI: 0.5 to 3.0 MPG; p = 0.01). Residual diagnostics show no serious violations. Therefore, manual transmissions appear modestly better for MPG, though much of the raw difference is explained by vehicle weight and horsepower.

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10) Quick troubleshooting cheatsheet

• Knit stuck / error: read the first red error line. Usually library() missing → run install.packages("...").
• Plots too large: set fig.width and fig.height in chunk options, e.g. {r, fig.width=6, fig.height=3}.
• RPubs publish button missing: make sure the HTML is open in Viewer pane (not Source) and try rsconnect::rpubsUpload() if necessary.

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If you want, I’ll do one of these next right now (pick one):
A) Give the exact filled executive summary with numbers after I run the code (you paste output),
B) Produce a zipped folder of files you can upload to GitHub (I’ll show commands), or
C) Walk you step-by-step through publishing to RPubs (I’ll list exact clicks and expected screens).