Introduction to Business Statistics: Lesson #1

This Post: Introduction to Business Statistics: Lesson #1

was put together by our team of editors who are Business Review Consultants that give their expert recommendations, on the latest business software & tools that have been launched onto the market.

When we publish the latest money making opportunity or business software review on how to start an online business,

 
we will give you our complete analysis on the most popular software product tools. We will expose the most profitable online systems that top internet marketers don't want to share with you.

 

We reveal some of the most Powerful web programs  That have recently been launched onto the market that will help those starting an online enterprise or setting up new revenue streams.
We will test these software apps, to examine every feature of the tool, making sure its delivering results.      

All the videos we post on our constantly updated blog,

just like Introduction to Business Statistics: Lesson #1 that your about to watch,

will give you the competitive edge in your online business ventures,

that are targeted towards those interested in using the best tools on the market, that will help you start an online enterprise or run a profitable Home Based Business Franchise, using the most powerful web tools.

when we answer the question what is statistics it’s important to understand that statistics can either be a noun or a verb for right now we’re going to use it as a verb and so we’re going to say that statistics is the process of collecting organizing and interpreting data to make decisions well basically statistics help us make sense of numbers as I just mentioned statistic is a way to get information from data data is just the raw numbers it doesn’t make a lot of sense so in this case we use statistics to give us information information is data that makes sense here you see some data data in this case is just some raw numbers we don’t know anything about these numbers and they don’t make a lot of sense there could be thousands of these numbers so student statistics will help us make information out of these numbers and we’ll see that in the next slide well now we have used statistics to summarize the data and some of the values and names that you see right now probably don’t make a lot of sense if you haven’t had a statistics course before yet but some of these do mean or sometimes we call it the average the median the maximum the minimum the count and the sum so we’ve used statistics the process to summarize the data and describe it in this case so we have an example here of descriptive statistics this little diagram kind of explains where we’re headed in this course we’re going to start by working with descriptive statistics and we just saw an example of that what we’re going to be working with things like mean and mode and maximum that sort of thing but eventually we’ll progress into inferential statistics and inferential statistics are going to help us make decisions let’s just take a moment here and define descriptive statistics and inferential statistics a little bit more in detail so descriptive stats is the process of organizing summarizing and presenting the data in an informative way and an example might be the average age of a student of this College is twenty two point five or as we saw in the previous slide things that will include you know the mean the mode and that sort of thing inferential statistics is what we’re headed towards the second half of the book but that’s the process of making predictions or decisions about a population based on a sample of that population for example if I were to survey a hundred college students and I could decide at that point based on my sample I would make a decision maybe on their age that the average age of the student in general at the whole college might be twenty three point five but I’ve made that inference based on the sample that I’ve taken not by surveying every member in the population all right just to make sure we understand the difference the population is all the members of the group if my population were all the students at this college that would be the population the sample would be just some of the members on either taken at random or there’s various ways to sample that we’ll learn as we go a little bit further in this class I’ve mentioned the word survey already a couple of times and a survey is going to have variables and variables are the characteristics that we’re looking for such as age number of siblings number of credits taken at a class so those are all variables that we’ll be tracking when we survey a sample well you probably understood we’re going to get a little bit more complex as we go and now we’re going to introduce you to the different types of variables you can see that there’s two broad categories qualitative and quantitative and then there’s some subcategories within that and we’re going to spend just a couple of minutes talking about these different categories qualitative variables are variables that are not readily turned into numbers for instance gender would be qualitative another example of a qualitative would be variable would be how do you feel about the college that you attend typically those for statistical analysis were you going to work with qualitative value variables that we can turn into numeric values such as gender okay we can assign a zero for male and a one for female so in that case we can do some statistical analysis for some qualitative variables but a qualitative variable such as how do you feel it’s hard to do any kind of statistical analysis with that most of the time in statistics we’ll be dealing with quantitative variables and quantitative variables are numerical now there are a couple of different categories of quantitative variables as you can see here on this slide and we’ll talk about that here in a second but a quantitative variable could be an example of such as the balance in your checking account how many credits you’re taking how many books you bought this semester so those would be quantitative variable examples as you can see quantitative variables can be broken down into two different categories discrete and continuous discrete essentially are going to be whole numbers children in a family TV sets own you can’t have one and a half kids we don’t count the ones in the oven at this case continuous on the other end can be any value the weight in that an individual has it could be one point two pounds one point three pounds or the amount of error and a tire the amount that you paid an income tax so those things would be continuous variables and it’s important to learn and understand the difference between these discrete thing and a whole number continuous any value within the range as you just learned there are discrete and continuous variables but there’s also four different levels of measurement for quantitative variables they are nominal interval ordinal and ratio and we’ll go ahead and explain those in detail on the next slide 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 can simply be classified there isn’t an inherent value in there for instance if we had a list of four makes of you know Ford dodge Toyota and Honda and we just assigned Ford number one and Honda number four there isn’t an inherent rank in there with an ordinal value there is an inherent rank and so we might say okay pick your favorite teams in order of one to ten or the BCS standings that sort of thing antipholus are meaningful where there are meaningful differences between the values such as temperature there is a meaningful difference between 20 degrees and 30 degrees address size two and a dress size six there’s a meaningful difference in their now ratio is very similar to interval the main difference is that a ratio value that the zero point really means zero temperature you know in using the interval zero degrees really doesn’t mean the absence of temperature but with a ratio measurement zero would mean zero such as the number of patients seen zero means zero okay so those are the four levels of measurement for quantitative variables nominal ordinal interval and ratio spend some time look at the book and make sure you do understand the difference between these