Chapter 1- Introduction to Data

# jon tukey professor dai trang le chapter 1

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Unformatted text preview: r to an approximate problem.&quot; Jon Tukey Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 21 / 32 Collecting Data to Understand Causality Example of Population vs. Sample Population : All students in Stat 10 Winter 2014. Question of Interest: Number of hours studied per week? Sample: A sample of 100 Stat 10 students. Measured Variables: Hours studied, Gender, Major. Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 22 / 32 Collecting Data to Understand Causality Example of Population vs. Sample Population : All students in Stat 10 Winter 2014. Question of Interest: Number of hours studied per week? Sample: A sample of 100 Stat 10 students. Measured Variables: Hours studied, Gender, Major. Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 22 / 32 Collecting Data to Understand Causality Coﬀee might have anti-cancer properties In 2009, researchers from Harvard Health News (May 2009) found that: Coﬀee drinkers were 50% less likely to get liver cancer than nondrinkers . This is an A. Observational B. Experimental Answer A. Observational Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 23 / 32 Collecting Data to Understand Causality Coﬀee might have anti-cancer properties In 2009, researchers from Harvard Health News (May 2009) found that: Coﬀee drinkers were 50% less likely to get liver cancer than nondrinkers . This is an A. Observational B. Experimental Answer A. Observational Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 23 / 32 Collecting Data to Understand Causality Coﬀee might have anti-cancer properties In 2009, researchers from Harvard Health News (May 2009) found that: Coﬀee drinkers were 50% less likely to get liver cancer than nondrinkers . This is an A. Observational B. Experimental Answer A. Observational Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 23 / 32 Collecting Data to Understand Causality Observational Studies An Observational Study uses groups that are already created and records the diﬀerences. In an observational study we observe diﬀerences in the explanatory variable and then notice whether these are related to diﬀerences in the response variable. Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 24 / 32 Collecting Data to Understand Causality Controlled/Randomized Experiments If we try to establish a connection between the explanatory and response variables, we design a control or randomized experiment experiment A controlled experiments is an experiment where each individual is assigned to either the control group or the treatment group. The sample sizes should be large enough to account for other variability. Assignment should be done randomly. Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 25 / 32 Collecting Data to Understand Causality Establishing Causality from Experimental Designed Study Establishing Causality means to show that an outcome is...
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