BUSA 3101 Chapter 1
nominal level of measurement (qualitative)
-data that consist of labels, names, or categories -data that has no order -data that can only be classified or counted
3 reasons to study statistics
-data's collected everywhere and we need statistics to make the information useful -statistics is used to make professional and personal decisions -statistics is needed in your career
2 types of statistics
-descriptive -inferential
2 types of quantitative variables
-discrete -continuous
ordinal level of measurement (qualitative)
-level that ranks or rates data based on a relatively defined trait or qualitative variable -data only ranked and counted
interval level of measurement
-level that ranks or rates data based on a relatively defined trait or qualitative variable -data only ranked and counted - similar to ordinal but interval/distance between values is meaningful -based on a scale with a known unit of measurement -there is no natural 0 point
Ordinal Level Example
-list of top ten states for climate -list of top 50 countries for business -list of student rankings of professors -your rank in class -team standing in athletic conference
why is the level of measurement important?
- The level of measurement dictates the calculations that can be done to summarize and present the data. - It is used to determine the statistical tests that should be performed on the data.
internal level of measurement example
-Fahrenheit temperature scale -dress sizes -temperature
Discrete Variable (Quantitative)
-are typically the result of counting -have gaps between the values
nominal level example
-classifying candies by color -identifying students by gender -make of a car -jersey numbers
A poll solicits a large number of college undergrads for info on the following variables: A. the name of their cell phone provider (AT& T, Verizon, and so on) B. the number of minutes used last month C. their satisfaction with the service ( terrible, adequate, excellent, etc.) What is the level of measurement for each of these three variables?
-nominal -ordinal -ordinal
what are the 2 types of variables?
-qualitative -quantitative
Ratio Level of Measurement (Quantitative)
-the 0 point represents the absence of the characteristic -has all the traits of the interval level but the ratios between numbers are meaningful: -level that ranks or rates data based on a relatively defined trait or qualitative variable -data only ranked and counted -based on a scale with a known unit of measure
continuous variable (quantitative)
-usually the result of measuring -can assume any value within a specific range
ratio level of measurement examples
-wages -changes in stock prices -weight -number of patients seen -number of calls made -distance to class
4 levels of measurement
NOIR -nominal -ordinal -interval -ratio
qualitative variable
object or individual is observed and recorded as a non-numeric characteristic or attribute
sample
a portion, or part, of the population of interest
quantitative variable
a variable that is reported numerically
Population
entire set of individuals or objects of interest or the measurements obtained from all individuals or objects of interest
descriptive statistics
methods of organizing, summarizing, and presenting data in an informative and meaningful way
level of measurement that can only be classified or counted
nominal
what is the lowest level of measurement?
nominal
You are looking forward to graduation and your first job as a salesperson for one of ten large pharmaceutical corporations. Planning for your interviews, you will need to know about each company's mission, profitability, products, and markets. Would you collect information using a sample or a population? Why?
population bc each company is different and you need info on each specifically for the interview, estimation is helpful but not realistically applicable to each
the highest level of measurement?
ratio
inferential statistics
the methods used to estimate a property of a population on the basis of a sample
What is statistics?
the science of collecting, organizing, analyzing, and interpreting data in order to make decisions