smc-1893-31-3×3 Matrix

MATRICES, FUR2 2021 VCAA 3

A market research study of shoppers showed that the buying preferences for the three olive oils, Carmani (`C`), Linelli (`L`) and Ohana (`O`), change from month to month according to the transition matrix `T` below.

`qquadqquadqquadqquad \ text(this month)`

`T = {:(qquad\ C quadquadqquad \ L quadquad \ O ),([(0.85,0.10, 0.05),(0.05,0.80,0.05),(0.10,0.10,0.90)]{:(C),(L),(O):} qquad text(next month)):}`

The initial state matrix `S_0` below shows the number of shoppers who bought each brand of olive oil in July 2021.

`S_0 = {:[(3200),(2000),(2800)]{:(C),(L),(O):} :}`

Let `S_n` represent the state matrix describing the number of shoppers buying each brand `n` months after July 2021.

How many of these 8000 shoppers bought a different brand of olive oil in August 2021 from the brand bought in July 2021? (1 mark)
Using the rule `S_(n+1) = T xx S_(n)`, complete the matrix `S_1` below. (1 mark)

`S_1 = {:[(3060),(text{_____}),(text{_____})]{:(C),(L),(O):} :}`
Consider the shoppers who were expected to buy Carmani olive oil in August 2021.
What percentage of these shoppers also bought Carmani olive oil in July 2021?
Round your answer to the nearest percentage. (1 mark)
Write a calculation that shows Ohana olive oil is the brand bought by 50% of these shoppers in the long run. (1 mark)
Further research suggests more shoppers will buy olive oil in the coming months.
A rule to model this situation is `R_(n+1) = T xx R_n + B`, where `R_n` represents the state matrix describing the number of shoppers `n` months after July 2021.

`qquadqquadqquadqquad \ text(this month)`
`T = {:(qquad\ C quadquadqquad \ L quadquad \ O ),([(0.85,0.10, 0.05),(0.05,0.80,0.05),(0.10,0.10,0.90)]{:(C),(L),(O):} ):} qquad text(next month) \ , \ B = {:[(200),(100),(k)]{:(C),(L),(O):} :}, \ R_0 = {:[(3200),(2000),(2800)]{:(C),(L),(O):} :}`

`k` represents the extra number of shoppers expected to buy Ohana olive oil each month.

If `R_2 = {:[(3333),(2025),(3642)]:}`, what is the value of `k`? (1 mark)

Show Answers Only

`1160`
`L = 1900 \ , \ O = 3040`
`89text(%)`
`text(See Worked Solutions)`
`200`

Show Worked Solution

a. `text{Consider matrix} \ T`

`text{Carmani – 15% bought new brand}`

`text{Linelli – 20%, Ohana – 10%}`

`:. text{Shoppers}`	`= 0.15 xx 3200 xx + 0.2 xx 2000 + 0.1 xx 2800`
	`= 1160`

b. `S_1 = [(0.85,0.10, 0.05),(0.05,0.80,0.05),(0.10,0.10,0.90)]{:[(3200),(2000),(2800)]:} = {:[(3060),(1900),(3040)]:}`

`:. \ L = 1900 \ , \ O = 3040`

c. `text{Carmani purchasers in August)} \ = 3060 \ text{(see part b)}`

`text{Carmani purchasers in July} = 3200`

`text{Carmani purchasers in both July and August}`

`= 0.85 xx 3200`

`= 2720`

`:.\ text{% of August purchasers who bought in July}`

`= 2720/3060 xx 100`

`= 88.88 …`

`=89text(%)`

d. `S = T^50 xx S_0 = {:[(2400),(1600),(4000)]:}`

`text{Total shoppers} = 8000`

`:.\ text{Ohana purchasers (in long run)}`

`= 4000/8000 xx 100`

`= 50text(%)`

e.	`R_1`	`= [(0.85,0.10, 0.05),(0.05,0.80,0.05),(0.10,0.10,0.90)] [(3200),(2000),(2800)] + [(200),(180),(k)]`
	`R_2`	`= [(0.85,0.10, 0.05),(0.05,0.80,0.05),(0.10,0.10,0.90)] [(3260),(2000),(k+3040)] + [(200),(100),(k)]`
		`= [(3171 + 0.05 (k + 3040)),(text{not required}),(text{not required})]`

`text{Equating matrices, solve for} \ k:`

`3171 + 0.05 (k + 3040) = 3333`

`:. k = 200`

MATRICES, FUR1 2021 VCAA 7 MC

The matrix `S_{n + 1}` is determined from the matrix `S_n` using the recurrence relation `S_{n + 1} = T xx S_n - C`, where

`T= [(0.6,0.1,0.3),(0.3,0.8,0.2),(0.1,0.1,0.5)] , qquad S_0 = [(21),(51),(31)], qquad S_1 = [(24.0),(54.3),(20.7)] `

and `C` is a column matrix.

Matrix `S_2` is equal to

A. `[(23.04),(55.78),(16.18)]`	B. `[(25.34),(56.28),(17.38)]`

C. `[(26.04),(54.78),(18.18)]`	D. `[(28.34),(55.28),(19.38)]`

E. `[(29.04),(53.78),(20.18)]`

Show Answers Only

`A`

Show Worked Solution

`S_1 = TS_0 – C`

♦ Mean mark 40%.

`[(24.0),(54.3),(20.7)]`	`= [(0.6,0.1,0.3),(0.3,0.8,0.2),(0.1,0.1,0.5)] [(21),(51),(31)] – C`
`[(24.0),(54.3),(20.7)]`	`= [(27),(53.3),(22.7)] – C`
`C`	`= [(27),(53.3),(22.7)] – [(24.0),(54.3),(20.7)] = [(3),(-1),(2)]`

`S_2 = TS_1 – C`

`S_2`	`= [(0.6,0.1,0.3),(0.3,0.8,0.2),(0.1,0.1,0.5)] [(24.0),(54.3),(20.7)] – [(3),(-1),(2)]`
	`= [(23.04),(55.78),(16.18)]`

`=> A`

MATRICES, FUR2 2020 VCAA 4

A second market research project also suggested that if the Westmall shopping centre were sold, each of the three centres (Westmall, Grandmall and Eastmall) would continue to have regular shoppers but would attract and lose shoppers on a weekly basis.

Let `R_n` be the state matrix that shows the expected number of shoppers at each of the three centres `n` weeks after Westmall is sold.

A matrix recurrence relation that generates values of `R_n` is

`R_(n+1) = TR_n + B`

`{:(quad qquad qquad qquad qquad qquad qquad qquad text(this week)),(qquad qquad qquad qquad qquad qquad quad \ W qquad quad G qquad quad \ E),(text(where)\ T = [(quad 0.78, 0.13, 0.10),(quad 0.12, 0.82, 0.10),(quad 0.10, 0.05, 0.80)]{:(W),(G),(E):}\ text(next week,) qquad qquad B = [(-400), (700), (500)]{:(W),(G),(E):}):}`

The matrix `R_2` is the state matrix that shows the expected number of shoppers at each of the three centres in the second week after Westmall is sold.

`R_2 = [(239\ 060), (250\ 840), (192\ 900)]{:(W),(G),(E):}`

Determine the expected number of shoppers at Westmall in the third week after it is sold. (1 mark)
Determine the expected number of shoppers at Westmall in the first week after it is sold. (1 mark)

Show Answers Only

`237\ 966`
`241\ 000`

Show Worked Solution

♦ Mean mark part (a) 50%.

a.	`R_3`	`= TR_2 + B`
		`= [(0.78, 0.13, 0.1),(0.12, 0.82, 0.1),(0.10, 0.05, 0.8)][(239\ 060),(250\ 840),(192\ 900)]+[(-400),(700),(500)] = [(237\ 966),(254\ 366),(191\ 268)]`

`:. text(Expected Westmall shoppers) = 237\ 966`

♦♦♦ Mean mark part (b) 20%.

b.	`R_2`	`= TR_1 + B`
	`R_1`	`= T^(-1)[R_2 – B]`
		`= [(241\ 000), (246\ 000), (195\ 000)]`

`:. text(Expected Westmall shoppers) = 241\ 000`