CD-ROM

• Explore folder
• somewhat interactive exercises regarding
• box plots
• central limit theorem
• confidence intervals
• distributions
• exponential smoothing
• hypothesis testing
• population parameters
• probability
• random samples
• StatPlusV2 folder
• MS Excel Add-in
• Student
• a lot of data files to be used in exercises in the book
• Webhelp
• helps with finding help for StatPlus functions

content

Chapter 1 Getting started with Excel

• WIndows operating system (targeted at W2000)
• File installation
• Excel files/worksheets/saving/printing
• Excel Add-ins and installation of those
• Should install the software in class
• then have students work through the chapter themselves, install software on their machines at home (still need to figure out if this can be done in the labs as well, can the add-in run from the CD Rom?)
• assign as homework to print spreadsheet and hand it in (see gif file)
• should do all this before dealing with science
• no excercises

Chapter 2 Working with data

• data entry, formatting
• formulas and functions (relative/absolute)
• importing data from textfiles (wheat.xls)
• (importing data from databases)
• show how to import data in class (wheat.txt)

Chapter 3 Working with charts

• a picture is worth a thousand words
• Excel chart types
• how to make/edit a scatter plot
• bubble plots
• breaking a scatter plot into categories
• deal with Excel for a while before going into science?

Chapter 4 Describing your data

• variables and descriptive statistics
• frequency tables
• histograms
• distributions
• (stem leaf plots)
• means,.....
• variability
• boxplots (concept tutorial)

Chapter 5 Probability distributions

• probability
• discrete probability distributions (Poisson)
• continuous probability distributions
• PDF concept tutorial
• random variables and samples tutorial
• Normal distribution tutorial
• normal probability plots
• the sampling distribution tutorial
• central limit theorem tutorial

2 ways to quantify the probability of an event

• relative  frequency = Number of times an event occurs/number of replications
• probability = relative frequency on an event after an indefinitely long number of trials

Probability distributions

• pattern of probabilities for a set of events is called  a probability distribution
• two important elements:
• the probability of each event or combination of events must range from 0 to 1
• the sum of the probabilities of all possible events must be equal to 1
• discrete probability distribution => probabilities associated with series of discrete outcomes
• continuous probability distribution => probabilities associated with continuous values
• we cannot assign a positive probability to a specific value
• continuous probability distributions are calculated using a probability density function
• the probability associated with a range of values is equal to the area under the PDF curve
• Distributions  tutorial

Random Variables and random samples

• a random variable is a variable whose values occur at random, following a probability distribution
• when a random variable attains a value that value is called an observation
• a collection of observations is called a random sample
• Random sample tutorial (could go to the last page and demonstrate this. example: Marksman/target)

The Normal Distribution

• the distribution used in the above example is the Normal distribution:


• Probability tutorial  => normal distribution
• last page is useful: find the probability between +/- 1s and +/- 2s. (~0.68 and 0.95)
• relevant EXCEL functions
• NORMDIST
• NORMINV
• NORMBETW
• how to find out if data are normally distributed?
• use StatPlus to generate random normal data
• display data in two ways
1. as histogram with normal distribution overlay
2. as normal probability plot (normal score is the value you would expect if your sample came from a normal distribution, think of an infinite number of samples, each consisting of 5 observations, and average them)
• represent your 10 counts of PM data as histogram and normal probability plot

Parameters and estimators

• the normal distribution is described by two parameters, m and s
• these parameters can be estimated by  and s
• how large must the sample be to properly estimate m?
• example: 9 samples with 100 observations each sampled from a normal distribution (m=0, s=s), the mean is close to 0, and the SD is 1/3 of the s of the original  normal distribution
• (Population parameters tutorial)
• the SD of  is also referred to as the Standard Error (SE) of 
• by increasing the sample size, we can reduce SE
• Central limit theorem
• the central limit theorem describes the sampling distribution of the sample average, taken from any distribution
• if you have a sample taken from a probability distribution with mean m and standard deviation s, the sampling distribution of  is approximately normal with a mean of m and a SD of s/n^1/2
• Central limit theorem tutorial

Chapter 6 Statistical interference

• confidence interval tutorial
• hypothesis testing tutorial
• t-distribution tutorial
• non-parametric tests (in case data are not normal distibuted)

Confidence intervals

• statistical interference is the act of reaching conclusions about the world based on a set of data, and then evaluating the reliability of those conclusion
• a rough confidence interval can be estimated by +/- 2SE, 95% of the averages fall into this range
• in a report you might read: 'the flux of meteroites is 7.2+/-1.2 per year at the 95% confidence level'
• Confidence level tutorial: explore the confidence interval

Hypothesis testing

• testing a theory flow chart
• 4 elements in a hypothesis test
1. a null hypothesis H_o
2. an alternate hypothesis, H_a
3. a test statistic
4. a rejection region
• you accept the null hypothesis unless you have convincing evidence to the contrary
• defective resistors example
• Ho: there is no change in the mean number of defective resistors
• Ha: the mean number of defective transistors has changed

Chapter 7 Tables

• pivot tables
• chi-sqaure test

Chapter 8 Regression and Correlation

• correlation
• P-values
• scatterplot matrix

Chapter 9 Multiple Regression

Chapter 10 Analysis of Variance

Chapter 11 Time Series

Chapter 12 Quality Control