Excel Lab 1  This lab session is designed to introduce you to the statistical aspects of Microsoft Excel. During this session you will learn how to enter and exit Excel, how to enter data and commands, how to print information, and how to save your work for use in subsequent sessions. As with any new skill, using this software will require practice and patience. Excel is a spreadsheet used for organizing data in columns and rows. It is an integrated part of Microsoft Office, and so data can be easily imported and exported into word processing documents, databases, graphics programs, etc. It offers a wide range of statistical functions and graphs and so is a an alternative to specific statistical software. 
Excel Lab 2  Graphically representing data is one of the most helpful ways to become acquainted with the sample data. In this lab you will use Excel to present data graphically. You will be analyzing data using four types of graphs: Circle graphs, Bar graphs, histograms, and cumulative (relative) frequency plots (ogives). 
Excel Lab 3  The basic idea of descriptive statistics is to describe a set of data in a variety of abbreviated ways. In this lab you will investigate measures of central tendency and dispersion. The boxandwhiskers display, a graphical display of the 5number summary of a set of data, will also be introduced. 
Excel Lab 4  It is frequently interesting
to view the relationship of two variables. In this lab we will see how Excel can help us plot bivariate data and
discover some trends in the relationship. We can set up the data as ordered pairs, with the independent variable
as the x and the dependent variable as the y.

Excel Lab 5  Not only is it important to analyze single variables, but frequently one needs to determine if and how two variables are related. The correlation coefficient is a measure of the strength of the linear relationship between two variables. In these exercises you will use Excel to analyze this statistic, and these exercises will also give you a very brief introduction to linear regression. 
Excel Lab 6  This lab session is designed
to introduce you to random numbers and their use in simulating
experiments. The outcomes of events in
normal life cannot be predicted, but it is possible to have an idea of what
outcomes are possible. The theory of
probability was developed to help analyze experiments whose outcomes are
uncertain. We can use Excel to simulate
certain experiments such as flipping a coin or rolling a die.

Excel Lab 7  The normal distribution is one of the most important
distribution functions in statistics. We will now see how the binomial
probabilities can be reasonably estimated by using the normal probability
distribution. Later we will need to
determine whether normality is a reasonable assumption. We will start our investigation with a few
specific binomial distributions.

Excel Lab 8  In an effort to predict population parameters, we need to investigate the variability in the sample means obtained from repeated sampling. The Central Limit Theorem tells us that the sampling distribution of sample means, _{ } , is approximately normally distributed. In the following lab you will test the results of the Central Limit Theorem. 
Excel Lab 9  Two indispensable
statistical decisionmaking tools for a single parameter are (i)confidence
intervals, and (ii) hypothesis tests to investigate theories about
parameters. In this lab you will learn
how to calculate confidence intervals and perform hypothesis tests (assuming we
know sigma) using Excel.

Excel Lab 10  The tstatistic is used when making inferences concerning the population mean when sigma is an unknown quantity. We will introduce the ttest and compare the z and t distributions. 
Excel Lab 11  In this lab we will investigate the inferences that can be made about the binomial parameter p. Inferences concerning the population binomial parameter p are made using procedures that closely parallel the inference procedures for the population mean m (see lab 10). 
Excel Lab 12  In this lab we will present the hypothesis test for the standard deviation for a normal population. When sample data are skewed, just one outlier can greatly affect the standard deviation. It is very important, especially when using small samples, that the sampled population be normal; otherwise the procedures are not reliable. However, unlike the analysis for the mean you will not have convenient computer commands to help you. 
Excel Lab 13  When comparing two populations we need
two samples, one from each population. Two kinds of samples can be used: dependent or independent, determined
by the source of the data. The methods
of comparison are quite different.

Excel Lab 14  The data used in this lab is enumerative
 that is, the data is placed in categories and counted. The observed frequencies list exactly what
happened in the sample. The expected
frequencies represent the theoretical expected outcomes (what is expected to
happen “on the average”). These
expected values must always add up to n.
When
we perform a hypothesis test on these two sets of values, we are really asking,
“how different are they”? If the difference
is small, we may attribute it to the chance variation in the samples. However, if the difference is large there
may be a difference in the proportions in the population and we may reject the
null hypothesis. We can use the c^{2} distribution in our test. We will first make inferences concerning multinomial experiments and
then extend that to contingency tables.

Excel Lab 15  In earlier sessions you have examined and compared means from

Excel Lab 16  In an earlier lab, we looked at bivariate
data, and used the linear correlation coefficient to see if there was a
relationship between the two variables. You also looked at a method of developing a line of best fit. In this lab we will look at a method of
deciding whether the equation of that line is of any use to us in making point
predictions and developing confidence intervals.

Excel Lab 17  All the previous methods we have studied
are parametric statistics  based on a population that has a certain
distribution and can be applied only when
