Some vocab  Statistics is the art of solving problems and answering questions by collecting and analysing data.  Data are the facts or information we collect and analyze. (plural)  (note datum is the singular term)  Data set- a list of unorganized data. Often called the raw data

More vocab  Population- a collection of individuals about which we want to draw info/ conclusions  Sample- a subset of the population. (important for a sample to be random and to avoid bias!)  Survey- a collection of info from a sample  Parameter- a numerical quantity measuring some aspect of a population (i.e. mean (average) and usually have greek letters like αβγδμσρ etc)  Distribution- the “spread” of the data  Outliers- much larger or smaller than general body of data.

Raw Data .....just the numbers

Statistical Investigation  Step 1: Examining a problem which might be solved using data and asking questions (how many students ride bikes to school)  Step 2: Collecting the data  Step 3: Organising the data.  Step 4: Summarising and displaying the data.  Step 5: Analysing the data, and making a conclusion  Step 6: Writing a report (presenting your findings)

Census/Sample  A census is a method which involves collecting data about EVERY individual in a whole population.  A sample is a method which involves collecting data about a part of the population.  Not as detailed or accurate as census, but easier.

Problems with a sample A sample can be biased if the data has been unfairly influedned in the collection process. A biased sample won’t represent the whole population Question: Are you good at climbing trees?

Other problems  Question: Do Americans  like cheese burgers???  I am American.  I like cheese burgers.  There fore ALL Americans like cheeseburgers.  Valid argument?!?!  …..i think NOT!  A sample must be sufficiently large to represent the whole population

Variables in Statistics  Categorical variable – describes qualities or characteristics. Can be divided into categories.  The information is called categorical data.  Examples.  Getting to school: Bus, train, bike, car, walking.  Color of eyes:

Variables in statistics  Quantitative variable- has a numerical value, and is often called a numerical variable.  The information collected is called numerical data.  Can be discrete or continuous.  A quantitative discrete variable takes exact number values. (Think counting)  Examples. Number of people in a house hold  The score out of 30 on a test  The number of sunny days in Stavanger. 1,2,3,4,.....

Variables in Statistics  A quantitative continuous variable takes numerical values within a certian CONTINOUS range. (think measuring)  Examples.  The weights of new born babies  The heights of 9th grade students  Time

Bar Chart vs. Histogram Histogram DISCRETE DATA Continuous Data

Bar Chart and Histogram  Both have:  Frequency on vertical axis and scores on horizontal  Column widths are EQUAL  Modal class = highest bar

Presenting the Data  See text page 378/9

Presenting and Interpreting Data

The Distribution of Data  (going to lunch)

The “spread” of the Data

Outliers

Interesting Statistics   zations/ zations/  ms=false&exact=false ms=false&exact=false  stm stm

 See “big” data pdf