Tutorial lab: nouns, bill averages and expenditure bands

Practise the same ideas with the original word cards and shopping bills. These are new questions on familiar data, so you can focus on the procedure rather than learning a new dataset.

Tutorial 3.1: nested decisions #

LongNoun = 0; ShortNoun = 0
for each row X in Words {
    if (X.PartOfSpeech == "Noun") {
        if (X.LetterCount > 7) { LongNoun = LongNoun + 1 }
        else { ShortNoun = ShortNoun + 1 }
    }
}

The result is 7 long noun occurrences and 14 short noun occurrences:21 nouns in total. Non-nouns increase neither counter. Notice that “short” here means 7 or fewer; this task uses a different definition from the hypothesis lesson’s 5-or-fewer rule.

Tutorial 3.2: bills above average #

All 30 amounts total₹24,413. Average=24413/30≈₹813.7667. The 8 above-average BillNo values are 2,3,5,8,11,13,23,25. Use the unrounded average for comparisons.

Tutorial 3.2: low, medium and high expenditure #

The numerical width is(4174−92)/3≈1360.6667. The tutorial rounds cut points to define the integer bands above. Specify those bounds explicitly; they are the convention used here. Real bills can include paise, so a general solution would use continuous bounds or integer paise rather than leaving fractional amounts between integer bands.

A guessed huge initial Minimum is only safe with a guaranteed bound. Initializing Minimum and Maximum from the first actual bill avoids assuming that no larger bill can ever occur. Handle an empty dataset separately.