Stat 301 - HW 4

Due midnight Friday, Feb. 9


Please upload each problem as a separate file.  Please remember to put your name(s) inside the file and if submitting jointly to join a HW 4 group before submitting. Remember to integrate your output with your discussion.  Points will be deducted if you are missing output. 


Problem 1 requires Excel or Google Sheets.  Problem 2 requires R. Please start the technology components early in the week so you can ask questions.


1) Recall the Literary Digest example (see Inv 1.16), where we blamed the poor estimate (41% voting for Roosevelt when actually 60.8% did) largely on an incomplete sampling frame (the wealthier Republicans were more likely to be sampled) and voluntary response bias (those who had more time/money to respond or who were more unhappy with the incumbent were more likely to respond). These seem like obvious explanations in hindsight, but should the Digest have realized this was happening? And could they have done anything about it?  Normally, we don’t know whether the size of the bias or even if there actually is bias until we know the parameter (we may never happen), but if we suspect a sampling method is biased, and if we have other information about the individuals in our sample, can we make adjustments in advance? For example, Digest postcard they sent out in 1936 also asked individuals to report whom they had voted for in 1932.

The goals of this exercise are to explore whether using this information would have been helpful to the Digest in predicting the 1936 election (related to the idea of post-stratification which you can learn more about in Stat 421), as well as to practice a few “spreadsheet skills” and think about data quality checks.

(a) Open the LitDigest1936.xlsx file in Excel or Google Sheets. This contains the raw counts for the three main candidates: Landon (column C), Roosevelt (column K), and William Lemke, Union Party (column S), in each state and overall (row 51), as well as the overall total number of straw votes cast in Digest poll in each state (column AA). For the 3 “major” candidates, what percentage of the poll respondents said they would vote for Republican Landon in the 1936 election? [Hint: Set up a column formula in row 53, using columns C and AA. Be sure to include the formula you used. You can type it out, or screen capture the formula bar, or in Excel for example you can got to Formulas > check Show Formulas]

Columns D-I is the breakdown of how all of the “Landon voters” in the 1936 Digest poll voted (or not) in 1932, for each individual state. For example, in Alabama, 3060 Digest respondents said they planned to vote for Landon.  Of those, 1,218 said they voted for the Republican candidate in 1932.

Focus on row 52 (state totals).

(b) Set up a formula for determining the number of respondents to the 1936 poll who said they voted in 1932 for either the Republican, Democratic, or Socialist or Other candidate. [Hint: Use columns D-G, L-O, and T-W.] What proportion of these voted for the Republican candidate. [Include your formulas.]

            Total voters in 1932 for Republican, Democrat, Socialist, Other:

            Proportion of 1932 R, D, S, O voters who voted Republican in 1932:

(c) Now examine the actual 1932 election results, what proportion of voters voted for the Republican (Hoover) candidate (among the three major candidates)?

(d) Is there evidence that the Literary Digest sampling methods tend to overrepresent Republican voters?  Cite your (numerical) evidence.

One way to adjust the 1936 poll results would be to “scale down” the number of Republican voters and “scale up” the number of Democratic voters. Consider the following ratios:




Socialist, Other

Non-voters, Missing





Dem: 1.1275

Rep: 0.871


(e) Verify that the first value is the ratio of the actual Republican turn out in 1932 to the Digest claimed turn out in 1932.

(Hint: Use your results from (b)-(d).)


(f) Start with the 1,293,669 Landon “voters” in the 1936 poll, arising from folks who voted Republican, Democrat, Socialist etc in the previous election.  Create a formula that multiplies each of these counts by the corresponding ratio (e.g., 0.782 * 920225), using 0.871 for nonvoters and missing, and then sum these “adjusted counts” from each party.  What is the adjusted number of Landon voters? (Remember to copy and paste your formulas as well.)


(g) Repeat (f) for Roosevelt (columns L-Q, 1.1275 for non-voters, missing). (Remember to include your formulas/documentation.)


(h) Using your results from (f) and (g), what is the adjusted percentage of voters for Roosevelt in 1936? Is this larger or smaller than the two-party breakdown without adjusting/closer or further from the actual vote in 1936?


(i) The graph below shows the results from this same process but applied to the individual states the Digest proportion planning to vote for Roosevelt (top graph), the actual proportion from the 1936 election results, and the adjusted proportions (middle graph).

dotplots showing literary digest predictions, adjusted proportions, and actual proportions

·         Does the Digest’s original method appear to be biased? Explain how you are deciding. 

·         Does the adjustment appear to help? How are you deciding? 

·         In the U.S. election, what really matters is the electoral vote; that is, which candidates has the most votes in the state.  Between the Digest poll and the adjusted proportions, how many states changed which candidate would receive their electoral votes?


(j) When I first went looking for the original Digest results, I first found the History Matters webpage, but soon realized there were some data errors on this page. Examine the data provided on that webpage.

·         If you check the totals, they don’t quite match up. Can you find the data entry errors? [Hint: Do any numerical values look suspicious to you? Do any states behave unusually?]

·         The State Unknown row also looks suspicious to me.  Why is it suspicious? Based on the values given in that row, what do you think the counts for Landon and Roosevelt for individuals with unknown states actually were?


Extra Credit:  Here is a screenshot from the original Digest article (I was able to use inter-library loan to get a pdf of the original article)

A table of numbers and a few digits

Description automatically generated with medium confidence

Suppose your boss asks you to get these data from the pdf file into the computer.  Do a little research – what would be an efficient way of extracting the data from the pdf file?!


Optional: More recently

·         2016 election issues.

·         2020 election issues.



2) The Current Population Survey (CPS) is “one of the oldest, largest, and most well-recognized surveys in the United States.  The CPS is immensely important, providing information on many of the things that define us as individuals and as a society – our work, our earnings, and our education.”  (Optional: video overview for (a)-(c).)


(a) Open the CPS webpage Follow the links for Technical Documentation and then Methodology.  Provide a brief summary of what you learn about how these data are collected (e.g., How often is the survey conducted?  How many people? What are the observational units? Do they use random sampling? Are any individuals excluded from the data? What is the idea behind “weighting”? Who stands to benefit from these data? Does anyone stand to be harmed by this data?)


(b) Open the Data page and follow the third link for Current Population Survey Datasets. Follow the link for Annual Social and Economic Supplements.  Download the CSV file from under Data and Documents.  This downloads a zip file. Extract the files, you want the pppub23.csv file.  Get these data into JMP or R.


In R: You can open a zipped file in R but for this assignment, I recommend using RStudio.  Select File > Import Dataset > From Text (base). Select the pppub23.csv file.  Select Yes for the Heading. The preview should update and convince you it is reading the columns in correctly.  Select Import. It will take a couple of minutes.

In JMP: After extracting the file, you should be able to open JMP and then select File > Open.  You need to change the file type to Text Files  and then you should be able to see the .csv file in the folder and open ppub23.csv.


How many observations are in the data file?


Make sure you are using pppub23 here on out


(c) Back on the CPS ASES webpage, open the Data Dictionary.

·         Find the description of the A_HRSPAY variable. (How did you find it?) What does this variable measure?  Who is measured for this variable? 

·         Which variable reports the biological sex of the respondent? How many categories are defined?


(d) In the data file, subset the data to only include the individuals with A_HRLYWK = 1 and A_HRSPAY > 0

 (Hints: See Investigation 2.1.

·         For R, try pppub23b = pppub23[which(pppub23$A_HRLYWK == 1 & pppub23$ A_HRSPAY > 0),]   You can also use nrow(pppub23b) to see how many observations you end up with.

·         In JMP, when a variable is highlighted in the left window, you can type in the variable name to help search for it. The Invert Row Selection command is helpful too. In R, maybe save it as pppub23b?).

Document your steps. How many individuals remain?


(e) Make a well-labeled histogram of the A_HRSPAY variable for the new subsetted data, also report the mean, standard deviation, and five number summary (with measurement units, make sure your results make sense in context!).  Write a one-sentence summary of the behavior of this distribution in context. 


In R: you can use



with(pppub23b, hist(A_HRSPAY))

with(pppub23b, iscamsummary(A_HRSPAY))



Analyze > Distribution (notice there is a search box for typing in the variable name)

Move A_HRSPAY to Y,Columns box

You might also want to try Graph > Graph Builder. There is a check box for mean and SD. You can also switch to a boxplot and hover to see the FNS values.


(f) Describe what the shape of this distribution implies in this context. Is the comparison between the mean and median hourly wages consistent with this shape? Explain.


(g) Determine the median hourly wage for females and median hourly wage for males.  


In R

with(pppub23b, iscamsummary(A_HRSPAY, A_SEX))


Analyze > Distribution

Move A_HRSPAY to Y,Columns box

Move A_SEX to the By box

Use hotspot next to Distribution and select Stack. Or in the Graph Builder, drag A_SEX to the Group Y box on the right hand side.


(h) Examine the ratio: how much do women make for every $1 men make, “on average.”


(i) Which Race(s) are NOT present in this dataset? (Hint: Data Dictionary)


In R

with(pppub23b, table(PRDTRACE))


Use Analyze > Tabulate. Note, you can type in the variable name (prdtrace) to search for it. Notice it has a blue icon next it.  Right click on that icon and change it from Continuous to Nominal. Once you see the red icon, drag the variable into the Drop Zone for rows.


(j) Find the median hourly wages for the following categories. Document your steps. (This one is a bit of a technology challenge question.)


White (only) male


White (only) female


Black  (only) male


Black (only) female


Asian (only) male


Asian (only) female



(k) Summarize what you learn from these values.  Also, suggest 1-2 new variables (quantitative or categorical) that you think might explain some of these differences.


(l) Take the (natural) log of the income wages.

In R

logsal = log(pppub23b$A_HRSPAY)

If you have zeros in the dataset, can add one before taking the log. By default, “log” is natural log in R.


Create a new column and edit the formula. Type or use your mouse to select Transcendental > Log to create Log(Income_wages). Press OK.


Make a histogram and a normal probability plot [In R: try iscamaddnorm(logsal) as well as qqnorm(logsal)]

Would you say the log income-wages distribution is approximately normal?  Explain.



3) Open the Sampling From Finite Population applet. The “Sleep 1” population should be selected, displaying a population distribution of “sleep times” (how many hours of sleep, to the nearest quarter hour, the night before).

A picture containing text

Description automatically generated

(a) Include a screen capture of the population distribution.  Describe the shape, center, and variability of the distribution, as well as the population size. Use appropriate symbols to refer to the mean, standard deviation, and population size.

(b) Would you say it’s surprising to select an individual from this population who has slept less than 7 hours?  Justify your answer.

(c) Check the Show Sampling Options box and press Draw Samples. Include a screen capture of the generated sample. How many observations are in your sample?  What symbols would you use to refer to the mean and standard deviation of this sample?

(d) Change the Number of Samples from 1 to 999 to generate 1,000 different random samples from this population.  Include a screen capture of the resulting sampling distribution. What are the mean and standard deviation of this distribution? Are these close to what we would predict (Inv 2.4)?

(e) Would you say it’s surprising to get a random sample of 10 people from this population with an average sleep time of less than 7 hours? Justify your answer.

(f) Check the Overlay Normal Distribution box.  Would you say the normal distribution is a good model for this sampling distribution?  Does this agree with the validity conditions of the Central Limit Theorem? Explain. 

(g) Use the pull-down menu to the second sleep population and press Use Data.

What are the key differences and what is essentially the same compared to the population distribution in (a)?

(h) Would you say it’s surprising to get a random sample of 10 people from this population with an average sleep time of less than 7 hours?  Be VERY clear how you are deciding and include relevant output.

(i) Would you say the normal distribution is a good model for this sampling distribution? Does this agree with the validity conditions of the Central Limit Theorem? Explain.

(j) Repeat (h) and (i) for a random sample of 50 individuals from this population.



Additional references/discussion on income inequality

· Why do you think they focus on “median hourly wage” rather than the “mean hourly wage”?

·         How the mean and median wages have changed over time.  What this tells you about “income inequality” in the United States.

·         Elizabeth Warren speech

·         World Income Inequality database (e.g., USA)

Additional reference on working with large databases

·         Introductions to Databases and/or Introduction to Querying at the Databases for Many Majors website