Basic Business Statistics – Chapter 1: INTRODUCTION
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What is Statistics?
Why does a manager need to know about statistics?
Some Important Definitions: Population vs. sample / Parameter vs. statistic / Frame
Descriptive Versus Inferential Statistics
a. Descriptive: Collecting (survey), presenting (tables and graphs), and characterizing data (mean or
standard deviation) data
b. Inferential: Drawing conclusions and/or making decisions concerning a population based only on
sample data (How do customers rate our products as compared with those from our competitors?)
i. Estimation - E.g. Estimate the population mean volume of soft drink in the 20 oz bottles
of a specific brand of coca-cola using the sample mean volume
ii. Hypothesis Testing - E.g. Test the claim that the population mean volume of bottles of a
specific brand of Coca-Cola is 20 oz
Why Data Are Needed: to measure performance or to formulate courses of action
Types of Data and Their Sources
a. Types of data
i. Categorical: marital status, political party, eye color, etc
ii. Numerical:
1. Discrete (be counted): number of children or defects per hour
2. Continuous (be measured): weight or voltage
b. Data Sources
i. Primary data through observations, survey, or experimentation
ii. Secondary data through some print or electronic media
Measurement Levels or Scales
a. Nominal data with distinct categories (no ordering or direction or ranking is implied) and is the
weakest form of measurement, such as marital status, type of car owned, type of stocks invested.
b. Ordinal data with distinct ranked categories (rankings, order, or scaling are implied), such as
service quality rating, student letter grade or Standard & Poor’s bond rating
c. Interval data with an ordered scale between measurements but no true zero, such as Fahrenheit
70°F or Celsius 21°C = (70° F – 32° F)*5/9, or Standardized exam score (SAT)
d. Ratio Data with the highest level: the differences between measurements and the true zero exists,
such as, Height, Age, Weekly Food Spending
Design of Survey Research pg. 242 – 249
a. Formulate Accurate Questions: Clear and unambiguous questions use clear operational
definitions – universally accepted definitions / Which questions on Student Survey need to be
revised?
b. Test the Survey: Pilot test on a small group of participants to assess clarity and length
c. Write a Cover Letter: State the goal and purpose of the survey / Explain the importance of a
response / Provide assurance of respondent anonymity
d. Offer incentive gift for respondent participation
Types of Survey Sampling Methods
a. Non – probability sampling: judgment, quota, chunk or convenience
b. Probability sampling: simple random, systematic, cluster or stratified
Evaluating Survey Worthiness: What is the purpose of the survey? / Is the survey based on a probability
sample?
Types of Survey Errors
a. Coverage error – significant differences between the intended population and the frame used to
draw samples
b. Nonresponse error – no way to follow up
c. Measurement error – bad or misleading questions
d. Sampling error – always exist