Basic Business Statistics

Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions. µ Σ λ σ C O P A I D β What is Meant by Statistics? Types of Statistics Descriptive Statistics Inferential Statistics Descriptive Statistics deals with methods of Organizing, Summarizing, & Presenting data* in an informative way. Typically, descriptive statistics include Mean, Mode, Median, Variance, Deviation, Skewness, Charts – histogram, bar, pie (we will see these in later chapters) * past/current data but not estimated future data Excel Example Output of Descriptive Statistics Inferential Statistics: methods used to determine something about a population on the basis of a sample. Population – all possible individuals, objects. Sample – part of the population of interest Why sample? •Time & cost are prohibitive •Physical impossibility of checking all items in population (eg. Checking quality of product if they are made in the millions) •Destructive nature of some tests •Sample results are adequate for decision-making Types of Variables Types of Variables For a Qualitative or Attribute Variable the characteristic being studied is non-numeric. It can only be labeled. (sometimes also called Categorical variable) Type of car Gender Eye Color Types of Variables In a Quantitative Variable information is reported numerically. Balance in your checking account Minutes remaining in class Number of children in a family Types of Quantitative Variables Quantitative variables can be classified as either Discrete or Continuous. Discrete Variables: can only assume certain values -there are usually “gaps” between values - usually “counted” Example: the number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1,2,3,…,etc). A Continuous Variable can assume any value within a specified range (“no gaps”). The pressure in a tire The height or weight of students in a class. Chart to remember Nominal Ordinal Interval Ratio The level of measurement dictates the kind of calculations you can do on the data. Eg. If one student’s major is Accounting and another’s IS, we cannot calculate the average major. On the other hand, we can average their heights, weights, etc. Nominal level Gender Data that is classified into categories. Can be arranged in any order. Measurement consists only of counts. Eye Color In this example, Country or Region is Nominal Level data Other examples: religion, major, gender, ethnicity, … Nominal level data must be: Mutually exclusive An individual, object, or measurement is included in only one category. Exhaustive Each individual, object, or measurement must appear in one of the categories. Ordinal level - involves data arranged in some order - magnitude of differences between data values cannot be determined. During a taste test of 4 soft drinks, Coca Cola was ranked number 1, Dr. Pepper number 2, Pepsi number 3, and Root Beer number 4. 4 2 1 3 Example of an Ordinal level variable Interval level - similar to the ordinal level - amounts of differences between data values is of equal size - there is no natural zero point. Eg. •Temperature on the Fahrenheit scale. •Difference between 10°F - 15°F is same as between 50°F& - 55°F •0° does not represent absence of temperature Ratio level (“highest” level of measurement) - zero value means “absence” - differences and ratios are meaningful for this level of measurement (A person with $2Million is twice as rich as another with $1Million) Miles traveled by sales representative in a month Traveled ‘0’ miles means did not travel at all Monthly income of surgeons Monthly income ‘0’ means did not make any money N-O-I-R ( Nerd Of India Rocks! ) Ethical Considerations Statistics can be used to mislead decision makers Don’t do it! •Keep taking different samples until you get the result you want •Quote ‘average’ to hide wide range of data values •Misleading graphical outputs •Make unwarranted conclusions on variable relationships Ethical Considerations The cost/year doubled in 5 years. But the graph appears to depict more than that. Ethical Considerations By changing the x-y scale, the rate of change in unemployment appears different. Ethical Considerations Also, a statistical association between two variables does not automatically imply ‘causation’. Eg. •Consumption of peanuts is correlated with aspirin consumption (eating peanuts gives headaches)