MAT Data Analysis and Sufficiency Syllabus

In the Concept and Answer Framework for DI, what should you do before attempting any calculation in a set?

Scan the entire data set first — read the title, units, headings, footnotes, and legend — to understand what each number represents, so you frame each question correctly and avoid unit errors.

What is the recommended order for attempting questions within a single DI set to maximize score?

Attempt the directly readable/single-step questions first (data lookups), then the single-source calculation questions, and leave multi-source or 'cannot be determined' style questions for last.

In DI's answer framework, when should you choose 'Cannot be determined' as the answer?

Choose it only when the required value depends on data not given and that cannot be derived from the provided sources — never merely because the calculation looks long.

What is Data Sufficiency (DS) as a question type?

A DS question gives a problem followed by two statements; the task is not to solve for the value but to decide whether the statements, alone or together, provide enough information to answer the question definitively.

State the five standard answer options of a typical Data Sufficiency question.

(A) Statement 1 alone is sufficient but statement 2 alone is not; (B) Statement 2 alone is sufficient but statement 1 alone is not; (C) Both together are sufficient but neither alone is; (D) Each alone is sufficient; (E) Both together are still not sufficient.

In quant-based Data Sufficiency, when is a statement 'sufficient' to answer a value question?

A statement is sufficient when it allows you to determine exactly one definite value (a unique answer); if it yields two or more possible values, it is insufficient.

In a Yes/No Data Sufficiency question, what makes a statement sufficient?

A statement is sufficient if it always gives a definite 'Yes' or always gives a definite 'No' — consistency of the answer matters, not whether the answer is yes; a statement that sometimes yields yes and sometimes no is insufficient.

What is the recommended sequence for evaluating the two statements in a DS problem?

Evaluate Statement 1 alone, then Statement 2 alone (independently, forgetting Statement 1), and only if neither alone is sufficient, combine them; this prevents carrying information improperly between statements.

In DS, after finding Statement 1 alone sufficient, which answer options remain possible?

Only (A) or (D) remain: it is (D) if Statement 2 alone is also sufficient, otherwise (A); options B, C, and E are eliminated.

In DS, after finding Statement 1 alone NOT sufficient, which answer options remain?

Only (B), (C), or (E) remain; options (A) and (D) are eliminated because they require Statement 1 alone to be sufficient.

In quant DS, why must you consider negative numbers, zero, and fractions when testing a statement?

Because a statement like 'x² = 16' or 'x > y' can have multiple solutions across these number types; ignoring negatives/zero/fractions can make you wrongly judge a statement sufficient.

What distinguishes reasoning-based Data Sufficiency from quant-based Data Sufficiency?

Reasoning-based DS tests whether statements give enough information to deduce logical relationships, arrangements, rankings, or directions (no numeric computation needed), whereas quant-based DS tests whether enough information exists to compute a numeric value.

In a reasoning-based DS question about a seating arrangement, when are the two statements together sufficient?

When combining the constraints fixes the required position/relationship to exactly one possibility; if more than one valid arrangement still satisfies both statements, they are insufficient even together.

In reasoning DS about blood relations, what makes a statement insufficient?

A statement is insufficient if it leaves the gender or generational link ambiguous, allowing more than one possible relationship (e.g., the relation could be 'brother' or 'sister').

What is the core task in a Quantitative Comparison (QC) question?

You are given two quantities, Column A and Column B, and must determine whether A is greater, B is greater, the two are equal, or the relationship cannot be determined from the given information.

State the four standard answer choices of a Quantitative Comparison question.

(A) Quantity A is greater; (B) Quantity B is greater; (C) The two quantities are equal; (D) The relationship cannot be determined from the information given.

In Quantitative Comparison, what operations can be performed on both columns without changing the relationship?

You may add or subtract the same quantity from both, or multiply/divide both by the same positive number; you must NOT multiply or divide both by a variable of unknown sign, since a negative multiplier reverses the inequality.

In Quantitative Comparison, when should you pick 'cannot be determined'?

Pick it when plugging in different valid values for the variables yields different relationships (e.g., A > B for one choice and A < B or A = B for another).

What is a fast QC strategy when both columns contain the same term added to different parts?

Cancel the common term from both columns, then compare what remains; this simplifies the comparison without computing full values.

What does 'Data Consistency' mean in data analysis?

Data consistency means the values across a data set agree with each other and with stated totals/relationships — e.g., the parts sum to the given total, percentages add to 100%, and no figure contradicts another.

How do you check the consistency of a pie chart's data?

Verify that all the sector percentages add up to 100% (or that all central angles sum to 360°); if they don't, the data is inconsistent.

In a table, what is one common consistency check using row and column totals?

The grand total computed by summing all row totals must equal the grand total computed by summing all column totals; a mismatch signals an inconsistency or error in the data.

Why is a data consistency check useful before answering a DI set?

It confirms you have correctly interpreted units and totals; if a quick check (parts vs. total) fails, you likely misread the table, preventing several wrong answers downstream.

What is 'trend analysis' in the context of data interpretation?

Trend analysis examines how a variable changes over a sequence (usually time) to identify the overall direction — increasing, decreasing, stable, or fluctuating — and its rate of change.

How do you distinguish a 'trend' from a 'pattern' in data?

A trend is the general long-term direction of movement (up/down/flat), while a pattern is a recurring or regular behaviour such as a cycle, seasonality, or repeating shape that may occur within or around the trend.

In trend analysis, what does a 'cyclical pattern' look like in a line graph?

A cyclical pattern shows values rising and falling in repeated waves over regular intervals, returning to similar levels periodically rather than moving in one steady direction.

What does a 'monotonically increasing' trend mean in a data series?

It means every successive value is greater than (or equal to) the previous one — the series never decreases over the observed period.

How do you compute the Compound Annual Growth Rate (CAGR) to summarize a multi-year trend?

CAGR = ((Final value / Initial value)^(1/n) − 1) × 100, where n is the number of years (periods); it gives the constant annual rate that links the start and end values.

In a trend with both growth and decline phases, how do you find the year of maximum value (peak)?

Compare all the data points and select the highest one; on a line graph it is the topmost point where the slope changes from positive (rising) to negative (falling).

What is the difference between absolute change and percentage change when analyzing a trend?

Absolute change is the raw difference (New − Old) in the same units; percentage change is that difference relative to the old value × 100. A large absolute change can be a small percentage change if the base is large, and vice versa.

In pattern analysis, what does an 'outlier' in a data set indicate and how is it spotted?

An outlier is a value that deviates markedly from the surrounding trend/pattern; it is spotted as a point far from the line or from neighbouring values, and may signal a special event, an error, or a break in the pattern.