Percent Changes in CPI for All Urban Consumers (CPI-U), US City Avera

Question

Percent Changes in CPI for All Urban Consumers (CPI-U), US City Average  
Seasonally adjusted changes from preceding month
    Category  
  Mar
2010  
  Apr
2010  
  May
2010  
  Jun
2010  
  Jul
2010  
  Aug
2010  
  Sep
2010  
  Unadjusted
12 months
ended Sep 2010  
 
  All items 
 0.1 
 -0.1 
 -0.2 
 -0.1 
 0.3 
 0.3 
 0.1 
 1.1 
 
  Food (all) 
 0.2 
 0.2 
 0 
 0 
 -0.1 
 0.2 
 0.3 
 1.4 
 
  Food (at home) 
 0.5 
 0.2 
 0 
 -0.1 
 -0.1 
 0 
 0.3 
 1.4 
 
  Food (away from home) 
 0 
 0.1 
 0.1 
 0.1 
 0 
 0.3 
 0.3 
 1.4 
 
  Energy (all) 
 0 
 -1.4 
 -2.9 
 -2.9 
 2.6 
 2.3 
 0.7 
 3.8 
 
  Gasoline (all types) 
 -0.8 
 -2.4 
 -5.2 
 -4.5 
 4.6 
 3.9 
 1.6 
 5.1 
 
  Fuel oil 
 0.7 
 2.3 
 -1.4 
 -3.2 
 -1.6 
 0.9 
 0.8 
 11.8 
 
  Energy services 
 1.4 
 -0.5 
 -0.5 
 -1.6 
 0.8 
 0.4 
 -0.8 
 1.5 
 
  Electricity 
 2.1 
 0.7 
 -0.4 
 -2.2 
 0.5 
 0.2 
 -0.3 
 1.1 
 
  All items less food and energy 
 0 
 0 
 0.1 
 0.2 
 0.1 
 0 
 0 
 0.8 
 
  New vehicles 
 0.1 
 0 
 0.1 
 0.1 
 0.1 
 0.3 
 0.1 
 2.1 
 
  Used cars and trucks 
 0.5 
 0.2 
 0.6 
 0.9 
 0.8 
 0.7 
 -0.7 
 12.9 
 
  Apparel 
 -0.4 
 -0.7 
 0.2 
 0.8 
 0.6 
 -0.1 
 -0.6 
 -1.2 
 
  Services less energy services (all) 
 0.1 
 0.2 
 0.1 
 0.1 
 0.1 
 0 
 0.1 
 0.8 
 
  Shelter 
 -0.1 
 0 
 0.1 
 0.1 
 0.1 
 0 
 0 
 -0.4 
 
  Transportation services 
 0.4 
 0.4 
 0.4 
 0 
 0 
 0.1 
 0.3 
 3 
 
  Medical care services 
 0.3 
 0.3 
 0 
 0.4 
 0 
 0.2 
 0.8 
 3.7

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 .

ID: 100361

sameerspice · Answer

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.

tanubarsainyan · Answer

Can some one please explin part 1

abhimahna · Answer

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.

NL15 · Answer

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/

mgpangwi · Answer

THE KEYWORD IS INFERABLE : Why is Q2 and Q3 'No'? The point is that you can answer all the questions with the given data. It's not asking if the statement is correct in relevance to the data. Please explain the logic behind the OA

aryan0406 · Answer

Hey guyz,
Statement 1 says: From March 2010 to September 2010, there was greater month-to-month variability in the "All items" category than in the "All items less food and energy" category.
If we calculate the variability from month to month then for "all items" category it will be: 0.2, 0.1, 0.1, 0.4, 0, 0.2 and for "All items less food and energy" category values are 0, 0.1, 0.1, 0.1, 0.1, 0. Now comparing both of these we can see that 3 among 6 are more in category "all items" and 2 are same and 1 is more in "All items less food and energy" category. So shouldn't the answer be NO because for all the months "all items" category does not have more value and it is nowhere mentioned in the statement that we are looking for the sum of all variability. It just says month-to-month variability which suggests that for each month we have to see.

Anugmat740 · Answer

1. From March 2010 to September 2010, there was greater month-to-month variability in the All-items category than in the All items less food and energy category.   - Yes

The variation in the values for All items less food category is unchanged for the 4 months.
But, The variation in the values for All items is changing every month.

2) The CPI-U for electricity increased 0.5% from July to August 2010. -  No

Let CPI-U for electricity in the month of June is 1000x.
In July = 1005x
In August = 1007x
Now change from july to august is ~0.01% and not 0.5%

3) There was no change in the CPI-U for all items less food and energy from May 2010 to September 2010.  - No

There is change in the value in the month May, June and July.

pinkman27 · Answer

can somebody explain part 3?

siddhantvarma · Answer

Hey  aryan0406 
I'm not sure if you're going to look at this, but I hope this helps others who might have the same doubt.

Let's make sure we understand Statement 1 correctly:

"From March 2010 to September 2010, there was greater month-to-month variability in the All items category than in the All items less food and energy category."

It says between March 2010 and September 2010, the overall trend in the month-to-month variability was least in the   All items less food and energy category 
If you have a different interpretation of Statement 1 from the one I derived, I want you to read it again closely. We're not interested in the variability for each consecutive month, rather, variablity accross the entire year on a month-to-month basis. So what we're looking for is if the category  All items less food and energy category   varied the least in the entire year on a month-to-month basis. Now if you look at the actual values, the values for each month of this category, ie 0, 0.1, 0.1, 0.1, 0.1, 0 are very close to each other and also repeat a lot => Variability is quite low.

Here's another way to look at it - if you were to plot these values on a graph, the Standard deviation will be very close to the mean (Standard deviation measures how far off the numbers are from the mean and in real world sense, they give you the variability). Ofc you don't need to think it from a Statistics lens, but it was intuitive for me to say that the variability here will be more because the values are much closer to each other.

For any other category, for ex let's take the example you gave, the values 0.2, 0.1, 0.1, 0.4, 0, 0.2 have more variability because these numbers are more spread apart. If you were to plot them on a graph, they standard deviation will be farther from the mean that it was for the previous case.

siddhantvarma · Answer

Part 3 says " There was no change in the CPI-U for all items less food and energy from May 2010 to September 2010." 
We want to check if the CPI-U for the category "all items less food and energy" stayed the same between May 2010 and September 2010. How would that be? If the value from May increased and decreased in a way that the value in September became the same as it was in May.

Look at the values in this category: 
 0, 0, 0.1, 0.2, 0.1, 0, 0

Remember, these are  percentage changes . I can look at these percentage changes and say there's no way September's value was the same as that of May. Why? Because from Apt to May, there was first an increase of 0.1%, then from May to June, an increase of 0.2% and then from June to July, an increase of 0.1%. The value of July = Value of September because the value didn't not change between July and September. 
However, May's value had increased 3 times, regardless of the amount or the percentage by which it increased till July. So there's no chance without any decrease that July's value can be the same as May's value. And if July's value can't be the same as May's value, September's value will also won't be the same as May's value.

Hence the answer is "NO".

Percent Changes in CPI for All Urban Consumers (CPI-U), US City Avera

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Category	Mar 2010	Apr 2010	May 2010	Jun 2010	Jul 2010	Aug 2010	Sep 2010	Unadjusted 12 months ended Sep 2010
All items	0.1	-0.1	-0.2	-0.1	0.3	0.3	0.1	1.1
Food (all)	0.2	0.2	0	0	-0.1	0.2	0.3	1.4
Food (at home)	0.5	0.2	0	-0.1	-0.1	0	0.3	1.4
Food (away from home)	0	0.1	0.1	0.1	0	0.3	0.3	1.4
Energy (all)	0	-1.4	-2.9	-2.9	2.6	2.3	0.7	3.8
Gasoline (all types)	-0.8	-2.4	-5.2	-4.5	4.6	3.9	1.6	5.1
Fuel oil	0.7	2.3	-1.4	-3.2	-1.6	0.9	0.8	11.8
Energy services	1.4	-0.5	-0.5	-1.6	0.8	0.4	-0.8	1.5
Electricity	2.1	0.7	-0.4	-2.2	0.5	0.2	-0.3	1.1
All items less food and energy	0	0	0.1	0.2	0.1	0	0	0.8
New vehicles	0.1	0	0.1	0.1	0.1	0.3	0.1	2.1
Used cars and trucks	0.5	0.2	0.6	0.9	0.8	0.7	-0.7	12.9
Apparel	-0.4	-0.7	0.2	0.8	0.6	-0.1	-0.6	-1.2
Services less energy services (all)	0.1	0.2	0.1	0.1	0.1	0	0.1	0.8
Shelter	-0.1	0	0.1	0.1	0.1	0	0	-0.4
Transportation services	0.4	0.4	0.4	0	0	0.1	0.3	3
Medical care services	0.3	0.3	0	0.4	0	0.2	0.8	3.7

Yes	No
		From March 2010 to September 2010, there was greater month-to-month variability in the All items category than in the All items less food and energy category.
		The CPI-U for electricity increased 0.5% from July to August 2010.
		There was no change in the CPI-U for all items less food and energy from May 2010 to September 2010.

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Percent Changes in CPI for All Urban Consumers (CPI-U), US City Avera