Presentation on theme: "Statistics at a Glance Part I Organizing, decribing, and analyzing data Part II Producing Data- Surveys, Experiments, and Observational studies Part III."— Presentation transcript:
Statistics at a Glance Part I Organizing, decribing, and analyzing data Part II Producing Data- Surveys, Experiments, and Observational studies Part III Probability Part IV Statistical Inference
Data or Personal Experience? Attendance Policy and Dances? Airplane Crash Deaths? Leukemia and Power Lines?
Where does data come from? Available Data Data that were produced in the past for some other purpose but that may help answer a present question. Producing Data Surveys Experiments Observational Studies Why might you use either method?
Part I Data Analysis Data Analysis: organizing, displaying, summarizing, and asking questions of data
Individuals and Variables Individuals are the objects described by a set of data. Individuals may be people, but they may also be animals or things. A variable is any characteristic of an individual. A variable can take different values for different individuals.
Categorical and Quantitative Variables A Categorical Variable places an individual into one of several groups or categories. A Quantitative Variable takes numerical values for which arithmetic operations such as adding and averaging make sense.
Meeting a new data set... Who are the individuals described by the data? What are the variables? Units? Why were the data gathered? When, where, how, and by whom were the data produced?
Education in the United States Who? What? Why? When, where, how, and by whom?
Distribution The distribution of a variable tells us that values the variable takes and how often it takes these values Birth month in the school? Favorite type of music? Values close together or spread out?
Exploratory Data Analysis Using statistical tools and ideas to help you examine data in order to describe their main features.
Many relationships between two variables are influenced by other variables lurking in the background (We'll see more examples like this later).
Part II Producing Data Surveys Select a sample Ask questions Draw Conclusions Where the data come from is important! (Ann Lander's)
Observational study slide Are surveys an observational study or experiment?
Why one or the other? What are the advantages of one or the other? Estrogen and Heart Attacks Example
Part III Probability If I flip a coin four times, am I guaranteed to get heads twice? The big idea of probability: Chance behavior is unpredictable in the short run but has a predictable pattern in the long run.
Probability: answers the question how likely This graph shows the probability for each possible number of correct guesses for an experiment in Mr. Bullard's class. If it the results of the experiment where complete chance, how likely would it be that 13 people would guess the correct answer in the experiment?
Part IV Statistical Inference Drawing conclusions from data
Statistical Inference A Gallup survey conducted an Internet survey of 1200 students, aged 13 to 17. They asked, Have you, yourself, ever cheated on a test or exam? Forty-eight percent said yes. If Gallup had asked the same question of all 13-to- 17 year old students, would exactly 48% have said yes?
Statistical Inference A Gallup survey conducted an Internet survey of 1200 students, aged 13 to 17. They asked, Have you, yourself, ever cheated on a test or exam? Forty-eight percent said yes. If they had selected a different sample of 1200 students to respond to the survey, would they have gotten the same percent?
Statistical Inference Even under the best of situations, variation is everywhere. When we study statistical inference, we learn that even with variation, we can be quite confident that between 45% and 51% of all teenage students would say that they have cheated on a test.