Introduction to Business Statistics: Lesson #1

Introduction to Business Statistics: Lesson #1

when we answer the question what is
statistics it’s important to understand that statistics can either be a nown or a verb. For right now we’re going to use it as a verb. Statistics is the process of collecting organizing and interpreting data to make
decisions. Statistics help us make sense of numbers Statistics is a way
to get information from data Data is just the raw numbers and doesn’t
make much sense. We use statistics to
give us information. Information then, is dat that makes sense. Here is some data data, just some raw numbers we don’t know anything about. Statistics will help us make
information from this data. Now we have used statistics to
summarize the data Even these value may not make sense right now if you haven’t had a statistics
course before yet. But, some of these are familiar We have a mean, or average, the median, the maximum and minimum, the count and sum. Statistics helped us summarize the
data and describe it. This is an example of descriptive
statistics. This diagram explains where we’re
headed in this course. We will start
with descriptive statistics as we just saw. We’ll begin with with mean, mode, max, and other descriptive statistics. Later we will progress into
inferential statistics. Inferential statistics will help us make
decisions Let’s define descriptive statistics and inferential statistics
in more detail Descriptive statistics is the process of
organizing summarizing and presenting data in an informative way. An example might be the average age
of a student in this colleges 22.54 Descriptive statistics includes the mean, mode, median, count, sum, and others. Inferential statistics is covered in the second half of the class. It is the process of making
predictions and decisions about population based on a sample of that
population. For example if I were to survey a hundred college
students and I might then decide things about the populations based on my sample I might infer or decide that the the average age of the students in general it the whole college might be
23.5 but I’ve made that inference based on the sample that I’ve
taken not by surveying every member in the population. Just to clarify those terms, the population is all the members of the group. My
population would be all the students at the college. The sample
would be just some of the members selected from the population, either taken at random or another sampling method you will learn
as this class progresses. I mentioned the word survey. A survey is going to have variables. Variables are the
characteristics that we’re looking for such as ages, number of siblings, number of credits taken
class for those are all variables that will be tracking when we
survey a sample This class will get a little more complex as we go so now we’re going to introduce
different types variables. On this chart you can see two broad categories: qualitative
and quantitative and some sub-categories within those. Now we’ll discuss these different categories Qualitative variables are variables that
not readily turn into numbers for instance
gender would be qualitiative. Another example would be “how do you feel about the college that you attend?” We can do some limited statistical
analysis with some qualitative variables such as those we can turn into numeric values such as gender. We can
assign a zero for male one for female so in that case
we can do some statistical analysis for some qualitative variables but a qualitative variables such as how
do you feel it’s hard to do any kind of statistical analysis with that most to the time in statistics will be
dealing with quantitative variables and quantitative variables are numerical. There are different categories
for quantitative variables as you see on the slide. We”ll talk about that
next. Quantitative variables include things like the balance in your checking account, how many credits you are taking how many books you bought this semester. Those are quantitative variable examples As you can see quantitative variables to
be broken down into two different categories discrete and
continuous. Discrete variables are essentially whole
numbers like children in a family number of TV sets. You can’t have one and a half kids Continuous variables can take on any value such as the weight of an individual item. It could be 1.2 pounds,1.3 pounds, etc.. The amoung of air in a tire, the amount
you pay in income tax are example of continuous
variables. It’s important to learn and understand the difference between
these. Discrete in a whole number continuous can be any value within the range There are also four different levels of measurement for quantitative
variables . They are nominal interval, ordinal, and ratio. We’ll explain these in detail Okay let’s give this a shot. as we see there are four different
levels of measurement and the first is nominal. Nominal is when the data could simply be
classified. There isn’t an inherent value in the order. For instance if we had a list of 4 makes of cars, Ford
Dodge Toyota, and Honda and we just assign Ford number one and Honda number four I’ll there isn’t
an inherent rank in these numbers. With an ordinal value there is an
inherent rank and so we might say pick your favorite teams in order 1 to 10 for the BCS standings. Interval values have meaningful differences between the values such as temperature There is a meaningful difference
between 20 degrees and 30 degrees In dress size 2 and dress size 6 there is a meaningful difference. Ratio is very similar to interval. The main difference is that the ratio scale has a zero-point that really means zerio. With temperature using the interval zero degrees
really doesn’t mean the absence of temperature but with the ratio measurement 0 with mean zero such as the number of
patients seen 0 means Those are the four levels of
measurement for quantitative variables: nominal,
ordinal, interval, and radio. Spend some time in the textbook and make sure you understand the difference between these variable types and measurement scales.

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