The Consumer Price Index (CPI) measures the average prices of goods

The thing that bothers me with question like the third one, is when they ask, "was about the same".

What qualifies as "about the same"? 10% 20%?

How should we approach the subjective and ambiguous term, "about the same"? Or something simliar like, " slighty more/less"?

Hi Hitman,

It's actually a clue that the answer is likely to be false... as you say it's hard to work out exactly what that means...

In this instance read the question carefully. It's asking about 'unadjusted' prices - but all the monthly data we ahve is for 'adjusted' so we can;t have enough info.

So in this instance, we don't need to stress about what counts as about the same...

James

I find this question to be especially annoying because of part 2:

2. The seasonally adjusted CPI-U for all items is higher in March 2010 than it is in the preceding month.

In the question, all items means "average", rather than "each of the items". This is clarified only by the existence of a separate table row with the name "All Items".

You need to stop and check every detail of the table to infer the meaning of all items, and I feel there is not enough time in the exam to do that.

The Consumer Price Index (CPI) measures the average prices of goods and services purchased by consumers. In the United States, the CPI-U calculates the CPI for all urban consumers.


For each of the following, select Yes if the statement is inferable from the given information. Otherwise select No.

I hope below explanation will help:
1) I observed the number of time values was constant (no change in % of CPI(U), for ALL Item it was only 1 instance between July & Aug for the given period however for other item it was 2 (Mar-apr, aug-sep): Hence Answer => True
2) Straight forwards, whatever be the value of %, the value has decreased from 0.5 to .3, so regardless, what ever may be the case. It couldn't have increased in any universe. - FALSE
3) May (CPI-U) = 0.1% Sep Value: 0%: Clearly value much have decreased for CPI -U to be Zero.: - FALSE

I am open to learn, if there is better way to solve it.

I also interpreted the question as all items separately.
I don't think the question is clear, given that "all items" has a well established meaning in English. It's just poorly worded and I hope the test designers have learned from their mistake.

Can some one please explin part 1

If you see the seasonal adjusted changes for All items from March to Sept, you will find sometimes it is +ve (implies it is increasing ) and sometimes it is -ve(implies it is decreasing. While if you see the same for All Items less food and Energy, you will find out of 7 months given , 4 have value 0 and the other three have very small change in the value, thus, it is true that there is greater Month to Month variability in the former than the latter. Hence, TRUE.

Hello all,

I still dont understand the answer to question two. There are negative figures (shelter, apparel etc) so how can we say that 'The seasonally adjusted CPI-U for all items was higher in March 2010 than in the previous month.' ?

I'm really confused ...

I agree...I'm not a fan of the answer to #2. I actually looked at the "All Items" row when working on this problem and thought it was a trick since some of the individual items had negative adjustments to their CPI values. Hence, I chose "No" which proved to be the wrong answer.

I don't know if there's a good way to explain this one

Hello, for the 3rd questions, why couldn't I divide the Unadjusted rate for each by 12 (for the 12 months)? I assume that column was the data across 12 months that could be broken up. So for new car I had 2.1/12 = 0.175 each month and food away was 1.4/12 = .1166 and those two seemed like close numbers, so I chose True.

Hi, I'm a bit confused. The question asked us to "select Yes if THE STATEMENT IS INFERABLE FROM THE THE GIVEN INFORMATION". My understanding was that we do not have to compute but simply choose Yes if, the response can be reduced from the data. I've come to understand the importance of paying attention to wording in GMAT this is quite confusing. Can someone help please?

Solution provided by Manhattan:

This table is ridiculous, isn’t it? And three paragraphs to start, before I even get to the question? There’s no way I’m possibly going to look through all of this data in 2.5 minutes. (Hint: that’s one clue to solving these kinds of questions!)

In general, on Integrated Reasoning tables, read the information that tells you what the table is about and look at all of the big labels (such as the column headers), but do NOT try to read / remember / understand all of the actual data points. You’ll use whatever data you need when you get a specific question about that data—but not before!

In this case, the first paragraph tells us that this CPI-U thing calculates average prices on a bunch of things for a certain population (urban consumers in the US). The second paragraph starts off by listing a bunch of categories—ignore them. If you need to know the specific categories, you can come back here later. In fact, ignore most of the details here until we figure out what the questions are about.

The third paragraph explains that we generally prefer to use seasonally adjusted prices. A quick glance at the table shows us that it includes both adjusted and unadjusted numbers. It also explains why—ignore this. Many, if not most, people will have used a minute or more at this point, so don’t spend that time now. If you need to know why, you can come back here later to understand.

Finally, glance at the table labels. The columns list a bunch of individual months from 2010 and those are labeled seasonally adjusted changes from preceding month. Notice two important things here: these numbers are seasonally adjusted, not unadjusted, and each monthly figure is based on the change from the preceding month. The final column is for a whole 12-month period and these numbers are unadjusted. Finally, there are a million individual categories listed in the rows. ?

I’m not 100% sure that I understand the difference between adjusted and unadjusted—but I don’t care about that right now. I’ll figure that out if I need to based on a specific question. Just keep going!

Now it’s time to examine the statements; here’s the first one:

The changes in seasonally adjusted prices for used cars and trucks between March 2010 and September 2010 were in most cases less in magnitude than the changes in seasonally adjusted prices of new vehicles for the same period.

Let’s see. They mention two categories: used cars and trucks and new vehicles. Great, I only need to look at those two rows. They also specifically mention adjusted for these, so I only care about the monthly columns. Scan until you find the right rows; luckily, they’re already right next to each other in the table.

The question asks about the magnitude of the changes in the two categories, so we need to compare them, month by month, to see which changed more and which changed less. For March 2010, the magnitude of change for used cars and trucks was 0.5, but only 0.1 for new vehicles. For that month, then, the magnitude of change was larger for the used cars and trucks. Compare the other months. In each case, the magnitude of change (the distance from zero) is larger for used cars and trucks. (This was true even in September 2010—the change was negative, but it was -0.7, which is 0.7 units away from 0. The magnitude of change for new cars was only 0.1.)

The statement says that the change for used cars and trucks was in most cases less in magnitude, but that’s not true. During all of the months in question, however, the change in magnitude (from zero) for used cars and trucks was greater.

Select No for statement 1.

Here’s the second statement:

The seasonally adjusted CPI-U for all items was higher in March 2010 than in the previous month.

This time, the statement is directing me to the all items category in the month-by-month (adjusted) timeframe. That’s right at the top of the table. We’re specifically asked about March 2010 compared to the previous month, but the table doesn’t include the previous month, so how can it tell us anything at all about February?

Read the labels! Right above the months, the table says Seasonally adjusted changes from preceding month. (Emphasis added.) In other words, the figure for March 2010 is based upon the change from February 2010. That change is positive, so it is true that the number for March is higher than the number for February.

Select Yes for statement 2.

And finally, our third statement:

The seasonally unadjusted change in the price of new vehicles in August 2010 over the previous month was about the same as the seasonally unadjusted change in the price of food away from home over the same period.

Read carefully! What data do they want now? Seasonally UNadjusted this time, not adjusted! The only data we have for the unadjusted category is based on the 12 months ended Sep 2010—we don’t have month-by-month data! Therefore, I can’t tell anything at all about what happened in August 2010 vs. July 2010—not for the UNadjusted data.

Note something very tricky. If you gloss over the Un in the word unadjusted, you might just go check the Aug and Jul columns for new vehicles and food away from home. For new vehicles, the change was 0.3. For food away from home, the change was 0.3. Those two numbers are the same—and so you would pick Yes, and you would fall into the trap. We don’t have any unadjusted data for the month of August alone.

Select No for statement 3.

The answers to the three statements are No, Yes, No.

Finally, notice something. How many rows and cells did you need in order to answer all three statements here? The first statement required us to use most of the cells in two adjacent rows. The second statement required us to use one cell in one row. The third statement required us to use nothing—but, even if we fell into the trap, we’d still only have used two cells in two different rows (one of which was a row we’d already used previously).

In other words, we never have to look at the vast majority of the data in this table. They could have cut out 3/4 of the rows without changing the question at all—except, perhaps, reducing the anxiety of a lot of test-takers when they saw a table with nearly 20 rows.

Key Takeaways for Integrated Reasoning Table Analysis Questions
(1) You will NOT need to use all of the data, so when you see a crazy number of rows or columns, don’t let yourself get anxious; instead, remind yourself that you won’t need to look at most of this. Ditto for those three paragraphs of text at the top. Don’t bother to read the table thoroughly or take in all of the data—just know what kind of information is available, then return to whatever you need based on the question statements.

(2) For many table questions, the ability to re-sort the data is critically important to our ability to answer these questions efficiently. Sometimes, though, we don’t need to sort the data at all—as in this case. Learn how to decide when and how to re-sort the data.

(3) Read carefully. When you have a table, by definition, the questions are going to hinge around whether you’re examining the right cell or cells from the table. Make sure you read the statements carefully with this question in mind: What group/category are they asking me about now?

URL: https://www.manhattanprep.com/gmat/blog ... -analysis/

A bit late with the reply. In this case, Question 2 is fairly straightforward. you just to look at the 0.1 in the very first cell (A1), which indicates the prices have gone up by 0.1 since the previous month. Thus it was higher.

The question says if the answer is inferable or not, so as per my understanding all the answers should be Yes since we can infer them from the given information.
Please let me know if my understanding is correct.

Posting a reply to this- agree with previous users that the meaning of something being "inferable" is confusing and still trying to wrap my head around 1. I'm trying to figure out whether something being inferable means it's TRUE/FALSE or that we have the data to figure out if it's TRUE/FALSE. I think it has to do with having the data to figure out if it's true or false, given the answer to #3.

OAs are 1) No 2) Yes 3) No.

2's answer makes sense because you can infer that information from the chart- March 2010 is positive which means it increased from Feb 2010. 3's answer makes sense because we don't have the unadjusted for individual items- we cannot say if it is even true or false.

Now 1 didn't make sense until I thought about the specific wordings for magnitude- we don't have the absolute values of changes in seasonally adjusted prices (just percentage changes), hence we cannot determine magnitude.

The following table displays the percent changes in CPI-U (Consumer Price Index for Urban Consumers) for categories including services, food, and other goods and services that people buy for day-to-day living. All taxes directly associated with the purchase and use of items (such as, in the United States, sales tax) are included in the index. An increase in CPI-U by a certain fractional amount means an increase by that fractional amount in overall prices within the relevant category.

For analyzing general price trends in the economy, seasonally adjusted prices are usually preferred over unadjusted prices because adjusting eliminates the effects of changes that normally occur at the same time and in about the same magnitude every year-- such as price movements resulting from climatic conditions, production cycles, model changeovers, and holidays.



For each of the following, select Yes if the statement is inferable from the given information. Otherwise, select No.

Yes..............	No...............	Statements
		The changes in seasonally adjusted prices for transportation services between March 2010 and September 2010 were, on average, less than the changes in seasonally adjusted prices of energy services for the same period.
		The seasonally adjusted price for used cars and trucks was higher in Sep 2010 than it was in Mar 2010.
		The magnitude of the seasonally unadjusted price change of apparel in Sep 2010 over the previous year was more than the combined seasonally adjusted price changes in Mar 2010 and Apr 2010 over their respective previous month.