Session 5 — Practical Work: Distributions in Excel

Decision Making Statistics — S04

Author

M. Kachour

Published

June 8, 2026

This session is a computer lab. Students reuse the tools from Sessions 1 to 4 in Excel; no new theory is introduced.

1 Session goals

Lab session

The objective is to apply previously studied formulas, not to introduce new notions. Before opening the Excel files, review the lecture notes from Sessions 1, 3, and 4.

By the end of the session, you should be able to:

choose the correct Excel function for a combinatorial or probabilistic calculation,
distinguish exact probabilities from cumulative probabilities,
compare exact binomial values with Poisson and Normal approximations,
interpret numerical outputs in a business context.

Exam tip

Even in Excel, the first step is always the same: identify the model before entering a formula.

2 Exercise 1 — Combinatorial Analysis

2.1 Context and mission

Review the context of the application exercise in Session 1. Then open the file PW - Session 05 - Exercise1.xlsx.

Key formulas

For combinatorial analysis:

\[ P_n = n! \]

\[ A_n^k = \frac{n!}{(n-k)!} \]

\[ C_n^k = \binom{n}{k} = \frac{n!}{k!(n-k)!} \]

Exercise

After reading the Session 1 lecture and Appendix 1, complete the following tables in PW - Session 05 - Exercise1.xlsx:

Table 1: compute permutations \(P_n = n!\) for the proposed values of \(n\).
Table 2: compute arrangements \(A_n^k\) for the proposed pairs \((n,k)\).
Table 3: compute combinations \(C_n^k\) for the proposed pairs \((n,k)\).

Goal: perform and interpret the formulas of combinatorial analysis in Excel.

To go further: complete Table 4 and produce the graph of \(C(n,k)\) as a function of \(k\) for a fixed value of \(n\).

Solution

A practical Excel strategy is:

for permutations: =FACT(n) or =PERMUT(n,n);
for arrangements: =PERMUT(n,k);
for combinations: =COMBIN(n,k).

Example with cell references:

Table 1: =FACT(B2)
Table 2: =PERMUT(B2,C2)
Table 3: =COMBIN(B2,C2)

Interpretation reminders:

permutations count all possible orderings of \(n\) objects;
arrangements count ordered selections of \(k\) objects among \(n\);
combinations count unordered selections of \(k\) objects among \(n\).

3 Exercise 2 — Discrete Random Variables

3.1 Context and mission

Review the context of the application exercise in Session 3. Then open the file PW - Session 05 - Exercise2.xlsx.

3.2 Binomial and Poisson calculations

Functions to recognize

For a binomial random variable \(X \sim B(n,p)\):

\[ P(X=k) = \binom{n}{k}p^k(1-p)^{n-k} \]

For a Poisson random variable \(X \sim \mathcal{P}(\lambda)\):

\[ P(X=k) = \frac{e^{-\lambda}\lambda^k}{k!} \]

For cumulative probabilities, compute \(P(X \leq k)\).

Exercise

After reading the Session 3 lecture and Appendices 2 and 3, complete Tables 1, 2, 3.1, 3.2, and 3.3 of PW - Session 05 - Exercise2.xlsx:

Table 1: Binomial distribution — compute \(P(X=k)\) using BINOM.DIST
Table 2: Cumulative binomial distribution — compute \(P(X \leq k)\) using BINOM.DIST with cumulative=TRUE
Table 3.1: Poisson distribution — compute \(P(X=k)\) using POISSON.DIST
Table 3.2: Cumulative Poisson distribution — compute \(P(X \leq k)\) using POISSON.DIST with cumulative=TRUE
Table 3.3: compare binomial values with their Poisson approximation

Goal: perform and interpret the formulas of the discrete random variable and the Binomial–Poisson approximation.

To go further: complete Tables 4.1 and 4.2 with a graphical illustration of the Binomial–Poisson approximation.

Solution

Typical Excel formulas are:

exact binomial probability: =BINOM.DIST(k,n,p,FALSE)
cumulative binomial probability: =BINOM.DIST(k,n,p,TRUE)
exact Poisson probability: =POISSON.DIST(k,lambda,FALSE)
cumulative Poisson probability: =POISSON.DIST(k,lambda,TRUE)

For the approximation table, first compute:

\[ \lambda = np \]

then compare:

\[ B(n,p) \approx \mathcal{P}(np) \]

when \(n\) is large and \(p\) is small.

Interpretation: the approximation is usually good when the binomial setting has many trials and a small success probability.

4 Exercise 3 — Continuous Random Variables

4.1 Mission 1.1 — Binomial distribution for large \(n\)

Review the context of the application exercise in Session 4. Then open PW - Session 05 - Exercise3.xlsx.

Normal approximation

When \(n\) is large and the binomial conditions are satisfied,

\[ B(n,p) \approx \mathcal{N}(np,\,np(1-p)) \]

so the mean is \(\mu=np\) and the standard deviation is

\[ \sigma = \sqrt{np(1-p)}. \]

Exercise

After reading the Session 4 lecture and Appendices 2 and 4, complete Tables 1.1 to 2.4 of PW - Session 05 - Exercise3.xlsx:

Tables 1.1–1.3: study the binomial distribution for large \(n\)
Tables 2.1–2.4: compute the Binomial–Normal approximation using NORM.DIST

Goal: perform and interpret the formulas for the continuous random variable and the Binomial–Normal approximation.

Solution

A useful Excel workflow is:

compute the binomial parameters \(n\) and \(p\);
derive the Normal parameters \(\mu=np\) and \(\sigma=\sqrt{np(1-p)}\);
use NORM.DIST to approximate probabilities.

For a cumulative approximation:

=NORM.DIST(x, mean, standard_dev, TRUE)

For an exact binomial value \(P(X=k)\), a continuity correction is often used:

\[ P(X=k) \approx P(k-0.5 \leq Y \leq k+0.5) \]

with \(Y \sim \mathcal{N}(np,np(1-p))\).

4.2 Mission 1.2 — Quantiles of the Normal distribution

Exercise

After reading Appendix 5, complete Table 3:

compute \(D_1\), \(Q_1\), the Median, \(Q_3\), and \(D_9\) for the Normal distribution using NORM.INV.

Goal: perform and interpret the quantile formulas for the Normal distribution.

Solution

Use the following probabilities:

\(D_1\): probability \(0.10\)
\(Q_1\): probability \(0.25\)
Median: probability \(0.50\)
\(Q_3\): probability \(0.75\)
\(D_9\): probability \(0.90\)

Typical Excel formulas:

=NORM.INV(0.10,mean,standard_dev)
=NORM.INV(0.25,mean,standard_dev)
=NORM.INV(0.50,mean,standard_dev)
=NORM.INV(0.75,mean,standard_dev)
=NORM.INV(0.90,mean,standard_dev)

Interpretation: NORM.INV(p, mean, standard_dev) returns the value \(x\) such that

\[ P(X \leq x)=p. \]

5 Appendices

5.1 Appendix 1 — Excel Functions for Combinatorial Analysis

Excel functions

COMBIN(number, number_chosen) — returns \(C_n^k\), the number of combinations
PERMUT(number, number_chosen) — returns \(A_n^k\), the number of arrangements without repetition
FACT(number) — returns \(n!\)

5.2 Appendix 2 — `BINOM.DIST`

Syntax

BINOM.DIST(number_s, trials, probability_s, cumulative)

number_s: number of successes
trials: number of independent trials
probability_s: probability of success per trial
cumulative: TRUE for \(P(X \leq k)\), FALSE for \(P(X = k)\)

5.3 Appendix 3 — `POISSON.DIST`

Syntax

POISSON.DIST(x, mean, cumulative)

x: number of events
mean: expected value \(\lambda\)
cumulative: TRUE for \(P(X \leq x)\), FALSE for \(P(X = x)\)

5.4 Appendix 4 — `NORM.DIST`

Syntax

NORM.DIST(x, mean, standard_dev, cumulative)

x: value at which to evaluate
mean: \(\mu\)
standard_dev: \(\sigma\)
cumulative: TRUE for \(F(x)=P(X \leq x)\), FALSE for the density \(f(x)\)

5.5 Appendix 5 — `NORM.INV`

Syntax

NORM.INV(probability, mean, standard_dev)

probability: a probability value \(p\)
returns the value \(x\) such that \(P(X \leq x)=p\)

1 Session goals

2 Exercise 1 — Combinatorial Analysis

2.1 Context and mission

3 Exercise 2 — Discrete Random Variables

3.1 Context and mission

3.2 Binomial and Poisson calculations

4 Exercise 3 — Continuous Random Variables

4.1 Mission 1.1 — Binomial distribution for large \(n\)

4.2 Mission 1.2 — Quantiles of the Normal distribution

5 Appendices

5.1 Appendix 1 — Excel Functions for Combinatorial Analysis

5.2 Appendix 2 — BINOM.DIST

5.3 Appendix 3 — POISSON.DIST

5.4 Appendix 4 — NORM.DIST

5.5 Appendix 5 — NORM.INV

5.2 Appendix 2 — `BINOM.DIST`

5.3 Appendix 3 — `POISSON.DIST`

5.4 Appendix 4 — `NORM.DIST`

5.5 Appendix 5 — `NORM.INV`