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Understanding DATA Concepts

Understanding DATA Concepts. Unit 2, Day 1. Learning Goals. I can use the terminology related to statistics including raw data, sample, population, variability, discrete, continuous, etc. I can give an example for each term discussed. What is the purpose of statistics ?.

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Understanding DATA Concepts

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  1. Understanding DATA Concepts Unit 2, Day 1

  2. Learning Goals • I can use the terminology related to statistics including raw data, sample, population, variability, discrete, continuous, etc. • I can give an example for each term discussed.

  3. What is the purpose of statistics? • To show relationships in data and determine how strong the relationship is.

  4. Where do statistics come from? • Everyday life, mostly as raw data • We can transform raw data into charts, diagrams, graphs etc…, which enables us to see trends.

  5. What are applications of statistics? • Medical research • Political decision making • Market research • The media

  6. What is variability? • This is when results from similar studies differ. • This may happen for many reasons: • Limited accuracy in measurement • Different samples • Variations in experiment conditions (eg. Different seasons, natural disasters, etc..)

  7. What is a population? • A group of individuals that is the focus of a study. For example, this could be the entire student body or all eligible voters for the next federal election.

  8. What is a sample? • A sample is merely a selection of individuals taken from a population. For example, our class could be used as a sample from the population of grade 12 students at St. David’s.

  9. Types of Data • Raw data - unprocessed information • Variable – quantity being measured • Qualitative data – non-numerical data (eg. Eye colour, car model, etc…) • Quantitative data – numerical data (eg. Size, height, etc…) • Continuous– any value in a given range (includes decimal values) • Discrete – certain values, usually integers (eg 2, 3, 4, 5, ….) • Categorical data – data that is given a label rather than measured numerically (eg. Blood type (A, A+, O, O- …), citizenship)

  10. Types of Data, continued… • Primary data – data that you collect via surveys, interviews, etc… • Secondary data – data collected by others (eg. Stats Canada, School data) • Experimental data – data produced by an experiment designed by researcher • Observational data – data collected through observation without experiment • Micro data and aggregate data – If you keep individual responses for data, its micro data (ie. test score for each student) . If you create an average from it, this is aggregate data (ie. average test score for the class).

  11. Examples 1. Mrs. Elliott records the amount of money each student makes at their part-time job. Give an example of micro and aggregate data from this. 2. Which of the following are quantitative? Determine if they are discrete or continuous. • a) Shoe size b) Hair colour c) Height

  12. More Examples… 3. Suppose that you have been hired to determine the level of support for each candidate running in the Kitchener-Conestoga area for a federal election. You decide to visit 200 homes in the area. State the population, the sample, the key variables for the study, as well as the type of data obtained (quantitative/qualitative).

  13. Last example… 4. Which of these study designs is experimental? How do you know? We want to see if taking Vitamin C daily can reduce the number of colds. a) Interview 200 people and ask if they had a cold in the past month and whether they were taking Vitamin C. b) Take 200 people and assign 100 to take Vitamin C daily and the other to take a placebo (pill with no therapeutic value). We follow them for a month to see which group has the most colds in the next month.

  14. Continued… c) Take 100 people who use Vitamin C and 100 who don’t. We make sure all are cold free. We follow the groups for a month to see who gets more colds.

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