Data 8 Final Review Notes.pdf - Data 8 Final Review Notes KEY Term Equation Python expression CHAPTER 2 Causality and Experiments Observational study\u200b

# Data 8 Final Review Notes.pdf - Data 8 Final Review Notes...

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Data 8 Final Review Notes KEY: Term Equation Python expression CHAPTER 2: Causality and Experiments Observational study : one which scientists make conclusions based on data that they have observed but had no hand in generating. - Treatment: factor of interest - Outcome: measured in each individual EX: easy to think of observational studies where the individuals are people, like with eating chocolate and getting heart disease, but that is not always the case. Individuals could be states, like with the death penalty and its effects on murder rate. Association: any relationship between the treatment and the outcome. Casuality: if the treatment causes the outcome, we can say the association is causal EX: John and predicting what caused cholera, he made a map. Treatment was drinking water from broad street pump, he found a causal association - Compared outcomes of people who got the treatment and people who did not - Causality determined by more experiments, if the outcome happens ONLY with the treatment, treatment and control groups have to be comparable apart from treatment for determining causality Confounding factors: underlying difference between the groups that could explain the association and mess with conclusions about causality - Common in observational studies Randomization: good way to avoid confounding. Administer treatment to randomly selected individuals. - If you can randomize both treatment and control groups, you are running a randomized controlled experiment - Two important consequences - Allows us to account for the possibility that randomization produces very different groups - Allows us to make precise mathematical statistics about differences; justifiable conclusions
CHAPTER 3: Programming in Python Expressions(3.1): basic math inside python - Raising to a power: [a**b] = [a^b] - Remainder: [a%b] Assigning names: X = name X → ‘name’ Growth rate (3.2.1) : the relationship between two measurements of the same quantity taken at different times - To calculate: (changed - initial) / (initial) OR [(changed / initial) -1] - Annual growth rate: growth rate per year, accumulated annual growth rate (for example if there was growth of 35% for three years we would have 1.35*1.35*1.35) which is much larger - Growth rate average over a certain number of years given total rate: [(changed / initial) ** (1/t) - 1] where t is the number of years - To compute changed: [Initial * (1 + GR)**t] Call expressions (3.3): using functions - List of built in python functions explanations: - Import math functions:
Tables(3.4) Two ways to look at a table: - A sequence of named columns that each describe a single attribute of all items in a dataset - A sequence or rows that contain all information about items in a dataset Table operations:
CHAPTER 4: Data Types To figure out the type of data, use type(x) Numbers & Floats (4.1): integers and real numbers(float values [with a decimal point] in python) Limits of float: - Can only represent 15-16 sig figs for any number -

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