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Chapter 1 Statistics: The Art and Science of Learning from Data. Learn …. What Statistics Is Why Statistics Is Important. Chapter 1. Learn… How Data is Collected How Data is Used to Make Predictions. Section 1.1. How Can You Investigate using Data?.

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Chapter 1 Statistics: The Art and Science of Learning from Data


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    1. Chapter 1Statistics: The Art and Science of Learning from Data • Learn …. What Statistics Is Why Statistics Is Important

    2. Chapter 1 • Learn… How Data is Collected How Data is Used to Make Predictions

    3. Section 1.1 How Can You Investigate using Data?

    4. Health Study • Does a low-carbohydrate diet result in significant weight loss?

    5. Market Analysis • Are people more likely to stop at a Starbucks if they’ve seen a recent TV advertisement for their coffee?

    6. Heart Health • Does regular aspirin intake reduce deaths from heart attacks?

    7. Cancer Research • Are smokers more likely than non-smokers to develop lung cancer?

    8. To search for answers to these questions, we… • Design experiments • Conduct surveys • Gather data

    9. Statistics is the art and science of: • Designing studies • Analyzing data • Translating data into knowledge and understanding of the world

    10. Example from the National Opinion Center at the University of Chicago: • General Social Survey (GSS) provides data about the American public • Survey of about 2000 adult Americans

    11. Example from GSS: Do you believe in life after death?

    12. Three Main Aspects of Statistics • Design • Description • Inference

    13. Design • How to conduct the experiment • How to select the people for the survey

    14. Description • Summarize the raw data • Present the data in a useful format

    15. Inference • Make decisions or predictions based on the data.

    16. Example from GSS: On a typical day, about how many hours do you personally watch television?

    17. What percentage of the people surveyed reported watching 0 hours of TV a day?

    18. Example: Harvard Medical School study of Aspirin and Heart attacks • Study participants were divided into two groups • Group 1: assigned to take aspirin • Group 2: assigned to take a placebo

    19. Example: Harvard Medical School study of Aspirin and Heart attacks • Results: the percentage of each group that had heart attacks during the study: • 0.9% for those taking aspirin • 1.7% for those taking placebo

    20. Example: Harvard Medical School study of Aspirin and Heart attacks Example: Harvard Medical School study of Aspirin and Heart attacks • Can you conclude that it is beneficial for people to take aspiring regularly?

    21. Section 1.2 We Learn About Populations Using Samples

    22. Subjects • The entities that we measure in a study • Subjects could be individuals, schools, countries, days,…

    23. Population and Sample • Population: All subjects of interest • Sample: Subset of the population for whom we have data

    24. Example Format • Picture the Scenario • Question to Explore • Think it Through • Insight • Practice the concept

    25. Example: The Sample and the Population for an Exit Poll • In California in 2003, a special election was held to consider whether Governor Gray Davis should be recalled from office. • An exit poll sampled 3160 of the 8 million people who voted.

    26. Example: The Sample and the Population for an Exit Poll Example: The Sample and the Population for an Exit Poll • What’s the sample and the population for this exit poll? • The population was the 8 million people who voted in the election. • The sample was the 3160 voters who were interviewed in the exit poll.

    27. Descriptive Statistics • Methods for summarizing data • Summaries usually consist of graphs and numerical summaries of the data

    28. Types of U.S. Households

    29. Inference • Methods of making decisions or predictions about a populations based on sample information.

    30. Parameter and Statistic • A parameteris a numerical summary of the population • A statistic is a numerical summary of a sample taken from the population

    31. Randomness • Simple Random Sampling: each subject in the population has the same chance of being included in that sample • Randomness is crucial to experimentation

    32. Variability • Measurements vary from person to person • Measurements vary from sample to sample

    33. Inferential Statistics are used: • To describe whether a sample has more females or males. • To reduce a data file to easily understood summaries. • To make predictions about populations using sample data. • To predict the sample data we will get when we know the population.