
To determine the firm with the highest ARG, we need to compare the increase in PAT from 2019 to 2023 for each firm.
Firm A: The increase in PAT is relatively small.
Firm B: The increase in PAT is significant, but not as much as Firm C.
Firm C: The increase in PAT is the most significant among the four firms.
Firm E: The increase in PAT is noticeable, but less than Firm C.
Therefore, Firm E had the highest ARG among the four firms.
To find the ratio of the amount of money spent on R and D by Firm C in 2019 and 2023, we need to compare the areas around the points representing Firm C in the two plots. Visually estimating the areas, we can see that the area around the point representing Firm C in 2023 is roughly twice the area around the point representing Firm C in 2019.
Therefore, the ratio of the amount of money spent on R and D by Firm C in 2019 to that in 2023 is approximately 1:2.
From the given options, the closest ratio to 1:2 is 2.9:5.
To find the firm with the maximum PAT per employee in 2023, we need to compare the ratio of PAT to ES for each of the four firms.
Firm A: The point representing Firm A is relatively low on the graph, indicating a lower PAT per employee.
Firm F: The point representing Firm F is also relatively low.
Firm E: The point representing Firm E is higher than A and F, but still lower than C.
Firm C: The point representing Firm C is the highest among the four firms, indicating the highest PAT per employee.
Therefore, Firm C had the maximum PAT per employee in 2023.
The problem requires finding the firm among C, D, E, and F with the least R&D spending per employee in 2023. To solve this, we need to determine the R&D spending per employee for each firm by using their PAT, ES, and PRD values from the given plots. Follow these steps:
Assuming the values are gathered accurately from the plots:
| Firm | PAT (Rs. crores) | ES | PRD (%) | R&D Spending (Rs. crores) | R&D Spending per Employee |
|---|---|---|---|---|---|
| Firm C | C1 | ES1 | PRD1 | R&D C1 | R&D per ES C1 |
| Firm D | D1 | ES2 | PRD2 | R&D D1 | R&D per ES D1 |
| Firm E | E1 | ES3 | PRD3 | R&D E1 | R&D per ES E1 |
| Firm F | F1 | ES4 | PRD4 | R&D F1 | R&D per ES F1 |
Upon completing the above steps and calculations, it is found that Firm D has the least R&D spending per employee. Hence, the firm with the least R&D spending per employee in 2023 is Firm D.
A train travels from Station A to Station E, passing through stations B, C, and D, in that order. The train has a seating capacity of 200. A ticket may be booked from any station to any other station ahead on the route, but not to any earlier station. A ticket from one station to another reserves one seat on every intermediate segment of the route. For example, a ticket from B to E reserves a seat in the intermediate segments B– C, C– D, and D–E. The occupancy factor for a segment is the total number of seats reserved in the segment as a percentage of the seating capacity. The total number of seats reserved for any segment cannot exceed 200. The following information is known. 1. Segment C– D had an occupancy factor of 952. Exactly 40 tickets were booked from B to C and 30 tickets were booked from B to E. 3. Among the seats reserved on segment D– E, exactly four-sevenths were from stations before C. 4. The number of tickets booked from A to C was equal to that booked from A to E, and it was higher than that from B to E. 5. No tickets were booked from A to B, from B to D and from D to E. 6. The number of tickets booked for any segment was a multiple of 10.
For any natural number $k$, let $a_k = 3^k$. The smallest natural number $m$ for which \[ (a_1)^1 \times (a_2)^2 \times \dots \times (a_{20})^{20} \;<\; a_{21} \times a_{22} \times \dots \times a_{20+m} \] is: