Statistics 400 Lecture 1

Introduction zWhat is statistics? zDiscipline of deals with the collection, organization and interpretation of data. zDone to answer questions of interest.

Example (Pain Reduction and Reiki) zIs Reiki an effective pain management tool? zReiki treatment is touch therapy used as an alternative to pain medication. zA pilot study involving 20 volunteers experiencing pain was conducted. zAll treatments were provided by a certified Reiki therapist. zPain was measured using before and after the Reiki treatment.

Example (Saving for Retirement) zWhat are the attitudes of low wage earners about saving for retirement? zAmericans earning $35,000 or less were asked how they are likely to accumulate enough money to retire. zWhat are the data?

Some Definitions zInterested in something about a population. zPopulation is a collection of individuals. zDescribe individuals with data. zData sets contain information on individuals. zA variables are attributes of an individual (e.g., hair color, pain severity,...). zDistribution of a variable gives the values the variable can take and how often it takes on each value

Types of Variables zTwo types of variable: yQuantitative Variables take on numeric values for which addition and averaging make sense (height, weight, income,…). yQualitative Variables: each individual falls into a category (ethnicity, machine works or does not, …). zHair color: zColor preference (red=1, blue=3, green=3): zLength of time slept:

zWill first focus on descriptive statistics (graphical and numeric). zWill move on to inferential statistics (test hypotheses). zIn either case, statistical tools are used to describe data and help answer scientific questions.

Descriptive Statistics zWant to describe or summarize data in a clear and concise way. zTwo basic methods: graphical and numerical.

Graphical Descriptions of Data zOften, pictures tells entire story of data. zHave different plots for the different sorts of variables. zFor Qualitative variables, will use bar-plots and pie charts.

Bar Charts zVariable values are the category labels (typically placed along the x- axis) zHeights of bar is the count (percentage) of values faling in that category. zNote bars are the same width!

Example(retirement savings) zA USA Today (Jan. 4, 2000) poll asked Americans who earn $35,000 or less how they expected to accumulate a $500,000 retirement nest- egg. zThe results are summarized in the frequency table below:

Pie Charts zVariable values are the category labels zEach category must appear on the plot zPercentage of area of pie covered by pie is relative frequency or percent) of values falling in that category. zCan easily see percentage for each category zNote Less flexible than bar chart