Presentation on theme: "Welcome to Elementary Statistics with David LofteDavid Lofte."— Presentation transcript:
Welcome to Elementary Statistics with David LofteDavid Lofte
Dave Lofte I was a student at Hartnell from 1999 to 2006! Majored in Mathematics at Sonoma State University. I graduated in 2008 I Received my Masters in Mathematics in 2010, I kept studying for another year before moving back to Salinas
Statistics are used everywhere, so it is beneficial to be statistically literate.
Statistics are useful Statistics are useful for most academics doing research, except maybe philosophy. Statisticians are well paid ~72,000 median Salary. Employment of statisticians is projected to grow 13 percent from 2008 to 2018. Knowing Statistics is useful for many other jobs. Better longer listlist
Survey Time! Complete the survey, then find at least three people that have similar academic Goals!
Intro to Statistics Data are collections of observations (such as measurements, genders, survey responses). Statistics is the science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data. A Population is the complete collection of all individuals (scores, people, measurements, and so on) to be studied. The collection is complete in the sense that it includes all the of the individuals to be studied.
Statistical Thinking A Census is the collection of data from every member of the population. A Sample is a sub-collection of members selected from a population. When conducting statistical analysis it is important to consider: Context of the data Source of the data Sampling method conclusions practical implications
Context of Data When examining data it is important to consider the context of the data. Consider the statement below by John Allen Paulos. The problem isn’t with statistical tests themselves but with what we do before and after we run them. First, we count if we can, but counting depends a great deal on previous assumptions about categorization. Consider, for example, the number of homeless people in Philadelphia, or the number of battered women in Atlanta, or the number of suicides in Denver. Is someone homeless if he’s unemployed and living with his brother’s family temporarily? Do we require that a women self-identify as battered to count her as such? If a person starts drinking day in and day out after a cancer diagnosis and dies from acute cirrhosis, did he kill himself?
Source of Data Consider this news clip from UK based paper The Independent: Tobacco companies are funding research into infertility in a bid to counter widespread evidence that smoking drastically undermines the chances of conceiving. Philip Morris, one of the world's largest cigarette firms, is being accused by the anti-smoking lobby of attempting to deceive smokers into believing they can improve their chances of having children if they take vitamin supplements.
Sampling Method Collecting sample data fro a study can have a great influence on the result of the study. Literary digest poll of the 1934 Roosevelt vs Landon.(Demographics) Question: Do you think the site Rate my Professor has accurate data or results?(Self Selection)
Conclusions It is important to state conclusions carefully, to avoid claiming more than your results justify. Practical Implications Sometimes the Statistical significance of a study can differ from its practical significance. A recent sites the results of a study of the Atkins weight loss program. The mean weight loss of the program was 2.1 pounds after 1 year.
Statistical Significance Ex A: Apparently using MicroSort 13 out of 14 couples that wanted to have girls had girls. Normally there is about a 1 in 1000 chance of that happening. Ex B: What if instead of 13 out of 14, only 8 out of 14 couples had a baby girl? The results from example A would be Statistically Significant, while those of example B would not.
Types of Data A Parameter is a numerical measurement describing some characteristic of a population. A Statistic is a numerical measurement describing some characteristic of a sample. Quantitative (or numerical) data consist of numbers represent counts or measurements. Categorical (or qualitative or attribute) data consists of names or labels that are not numbers representing counts or measurements.
Discrete data result when the number of possible values is either a finite number or a "countable" number. Continuous (numerical) data result from infinitely many possible value that correspond to some continuous scale that covers a range of values without gaps, interruption, or jumps. The nominal level of measurement is characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high). Data are at the ordinal level of measurement if they can be arranged in some order, but differences (obtained by subtraction) between data values either cannot be determined or are meaningless.
The interval level of measurement is like the ordinal level, with additional property that the difference between any two data values is meaningful. However, data at this level do not have a natural zero starting point (where none of the quantity is present). The ratio level of measurement is the interval level with the additional property that there is also a natural zero starting point (where zero indicates none of the quantity is present). For values at this level, differences and ratios are both meaningful.
Levels of Measurement Ratio:There is a natural zero starting point and ratios are meaningful. ex: Distances, Time, Salary, Age Interval:Differences are meaningful, but there is no natural zero starting point and ratios are meaningless. ex: Body temperatures in degrees Fahrenheit, IQ, SAT score. Ordinal:Categories are ordered, but differences can't be found or are meaningless. ex: Ranks of colleges in U.S. News and World Report, Grades Nominal:Categories only. Data cannot be arranged in an ordering scheme. ex: Eye Colors, ethnicity, party affiliation