Create readme.md

Author: elmoallistair

Exploratory Data Analysis in Python/01 Read, clean, and validate/01 Read the codebook/readme.md

@@ -1,11 +1,24 @@
-# Display the number of rows and columns
-nsfg.shape
+## Read the codebook
 
-# Display the names of the columns
-nsfg.columns
+When you work with datasets like the NSFG, it is important to read the documentation carefully. If you interpret a variable incorrectly, you can generate nonsense results and never realize it. So, before we start coding, I want to make sure you are familiar with the NSFG codebook, which describes every variable.
 
-# Select column birthwgt_oz1: ounces
-ounces = nsfg['birthwgt_oz1']
+* Follow [this link](https://www.icpsr.umich.edu/icpsradmin/nsfg/index?studyNumber=9999) to get to the interactive codebook.
+* Type "birthweight" in the search field, UNSELECT the checkbox that says "Search variable name only", and press "Search". You should see a list of variables related to birthweight.
+* Click on "BIRTHWGT_OZ1" and read the documentation of this variable. For your convenience, it is also displayed here:
 
-# Print the first 5 elements of ounces
-print(ounces.head())
+![birthwgt_oz1 codebook](https://assets.datacamp.com/production/repositories/4025/datasets/0d2a0c18b63f3ddf056858c145a6bdc022d8656c/Screenshot%202019-03-31%2019.16.14.png)
+
+<hr>
+
+How many respondents refused to answer this question?
+
+**Possible Answers**
+
+* 1
+* 35
+* 48-49
+* 2967
+
+**Answer**
+
+> 1

Exploratory Data Analysis in Python/01 Read, clean, and validate/03 Validate a variable/readme.md

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+## Validate a variable
+
+In the NSFG dataset, the variable `'outcome'` encodes the outcome of each pregnancy as shown below:
+
+| Value | Label |
+|-------|-------------------|
+| 1 | Live birth |
+| 2 | Induced abortion |
+| 3 | Stillbirth |
+| 4 | Miscarrieage |
+| 5 | Ectopic pregnancy |
+| 6 | Current pregnancy |
+
+The `nsfg` DataFrame has been pre-loaded for you. Explore it in the IPython Shell and use the methods Allen showed you in the video to answer the following question: How many pregnancies in this dataset ended with a live birth?
+
+**Possible Answers**
+
+* 6489
+* 9538
+* 1469
+* 6
+
+**Answer**
+
+> 6489

Exploratory Data Analysis in Python/02 Distributions/06 Distribution of education/readme.md

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+## Distribution of education
+
+Let's begin comparing incomes for different levels of education in the GSS dataset, which has been pre-loaded for you into a DataFrame called `gss`. The variable `educ` represents the respondent's years of education.
+
+<hr>
+
+What fraction of respondents report that they have 12 years of education or fewer?
+
+**Possible Answers**
+ 
+* Approximately 22%
+* Approximately 31%
+* Approximately 47%
+* Approximately 53%
+
+**Answer**
+
+> Approximately 53%

Exploratory Data Analysis in Python/03 Relationships/08 Interpreting correlations/readme.md

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+## Interpreting correlations
+
+In the previous exercise, the correlation between income and vegetable consumption is about `0.12`. The correlation between age and vegetable consumption is about `-0.01`.
+
+<hr>
+
+Which of the following are correct interpretations of these results:
+
+* *A*: People with higher incomes eat more vegetables.
+* *B*: The relationship between income and vegetable consumption is linear.
+* *C*: Older people eat more vegetables.
+* *D*: There could be a strong nonlinear relationship between age and vegetable consumption.
+
+**Possible Answers**
+
+* A and C only.
+* B and D only.
+* B and C only.
+* A and D only.
+
+**Answer**
+
+> A and D only.

Exploratory Data Analysis in Python/04 Multivariate Thinking/01 Regression and causation/readme.md

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+## Regression and causation
+
+In the BRFSS dataset, there is a strong relationship between vegetable consumption and income. The income of people who eat 8 servings of vegetables per day is double the income of people who eat none, on average.
+
+<hr>
+
+Which of the following conclusions can we draw from this data?
+
+A. Eating a good diet leads to better health and higher income.
+
+B. People with higher income can afford a better diet.
+
+C. People with high income are more likely to be vegetarians.
+
+**Possible Answers**
+
+* A only.
+* B only.
+* B and C.
+* None of them.
+
+**Answer**
+
+> None of them.