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Question 23 of 30

Q.(a) If the payment of ₹ 2,000 is made at the end of every quarter for 10 years at the rate of 8% per year, then find the amount of annuity. [(1.02)40=2.2080][(1.02)^{40} = 2.2080]

(OR)
(b) The total cost of 4 kg onion, 3 kg wheat and 2 kg rice is ₹ 320. The total cost of 2 kg onion, 4 kg wheat and 6 kg rice is ₹ 560. The total cost of 6 kg onion, 2 kg wheat and 3 kg rice is ₹ 380. Find the cost of each item per kg by Matrix Inversion method.
Puducherry TnboardTamil Nadu HSC First Year (DGE) Commerce Board 2023Subjective· 5mImportance★★★★★
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(a) Amount of the annuity =2000⋅(1.02)40−10.02=₹1,20,800= 2000\cdot\dfrac{(1.02)^{40}-1}{0.02} = ₹1{,}20{,}800. (b) By matrix inversion (det⁡A=50\det A=50), onion ₹20/kg, wheat ₹40/kg, rice ₹60/kg.

Part (a): Amount (future value) of an annuity. Payment P=₹2000P=₹2000 at the end of every quarter, 10 years, 8% per year.

Step 1 — Quarterly rate and periods. i=8%4=2%=0.02i = \dfrac{8\%}{4} = 2\% = 0.02; n=10×4=40n = 10\times4 = 40.

Step 2 — Amount formula (immediate annuity).

A=P⋅(1+i)n−1i=2000⋅(1.02)40−10.02.A = P\cdot\frac{(1+i)^n - 1}{i} = 2000\cdot\frac{(1.02)^{40} - 1}{0.02}.

Step 3 — Substitute (1.02)40=2.2080(1.02)^{40} = 2.2080.

A=2000⋅2.2080−10.02=2000⋅1.20800.02=2000×60.4=120800.A = 2000\cdot\frac{2.2080 - 1}{0.02} = 2000\cdot\frac{1.2080}{0.02} = 2000 \times 60.4 = 120800.

Part (b): Matrix-inversion method. Let onion =x=x, wheat =y=y, rice =z=z (₹/kg). The equations are

4x+3y+2z=320,2x+4y+6z=560,6x+2y+3z=380.4x+3y+2z=320,\quad 2x+4y+6z=560,\quad 6x+2y+3z=380.

So A=(432246623), B=(320560380)A=\begin{pmatrix}4&3&2\\2&4&6\\6&2&3\end{pmatrix},\ B=\begin{pmatrix}320\\560\\380\end{pmatrix}, and X=A−1BX=A^{-1}B.

Step 1 — Determinant.

det⁡A=4(4⋅3−6⋅2)−3(2⋅3−6⋅6)+2(2⋅2−4⋅6)=4(0)−3(−30)+2(−20)=50.\det A = 4(4\cdot3-6\cdot2) - 3(2\cdot3-6\cdot6) + 2(2\cdot2-4\cdot6) = 4(0) - 3(-30) + 2(-20) = 50.

Step 2 — Adjoint (transpose of the cofactor matrix):

adj A=(0−510300−20−201010).\text{adj }A = \begin{pmatrix}0 & -5 & 10\\ 30 & 0 & -20\\ -20 & 10 & 10\end{pmatrix}.

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