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by: Jocelyn Peck

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# Business Statistics Chapter 1 MIS 24056-602

Jocelyn Peck
KSU
GPA 3.7

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Chapter 1
COURSE
PROF.
Rajagopal, Parthasarathy
TYPE
Class Notes
PAGES
6
WORDS
KARMA
25 ?

## Popular in Management Information Systems

This 6 page Class Notes was uploaded by Jocelyn Peck on Thursday September 8, 2016. The Class Notes belongs to MIS 24056-602 at Kent State University taught by Rajagopal, Parthasarathy in Fall 2016. Since its upload, it has received 19 views. For similar materials see Business Statistics in Management Information Systems at Kent State University.

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Date Created: 09/08/16
Chapter 1 Data and Statistics  Statistics:  Refers to numerical facts: o Averages o Medians o Percent’s o Index Numbers  Helps us understand a variety of business and economic situations.  Refers to the art and science of: o Collecting o Analyzing o Presenting o Interpreting Data  Accounting: Using statistical sampling procedures when conducting audits.  Economics: Using statistical information in making forecast about the future economy.  Finance: Using price-earning ratios and dividend yields to guide investment advice.  Marketing: Collecting data for a variety of marketing research.  Production: Monitoring the output of a production process.  Information Systems: Statistical information helps access the performance of computer networks.  Data and Data Stats:  Data: facts and figures o Collected o Analyzed o Summarized o Presented o Interpreted  Data Set: the data collected in a particular study.  Elements: The entities on which data is collected.  Variable: A characteristic of interest for the elements.  Observation: The set of measurements obtained for a particular element.  “n” elements contains “n” observations.  Total Number of Data Values in a Complete Data Set = The # of Elements (x) The # of Variables  Scales of Measurement:  Nominal  Ordinal  Interval  Ratio  The scale determines the amount of information contained in the data.  The scale indicates the data summarization and statistical analyses that are most appropriate.  Nominal:  Labels  Names  Nonnumeric  Numeric Code  Ordinal:  The data have the properties of nominal data.  Order  Rank  Nonnumeric Label  Numeric Code  Interval:  The data have the properties of ordinal data.  The interval between observations is expressed in terms of a fixed unit of measure.  Always Numeric  Ratio:  The data has all the properties of interval data.  The ratio of 2 values is meaningful.  Distance  Height  Weight  Time  Scale must contain a zero value.  Data can be classified:  Categorical  Quantitative  The statistical analysis that is appropriate depends on whether the data for the variable is either Categorical or Quantitative.  There are more alternatives for Statistical Analysis when the data is Quantitative.  Categorical Data:  Labels  Names  Qualitative Data  Nominal or Ordinal Scale  Numeric or Nonnumeric  Quantitative Data:  How much: o Discrete  How many: o Continuous  Always Numeric  Cross-sectional Data: The data is collected at the same or approximately the same point in time.  Time Series Data: The data is collected over several time periods. o What happened in the past? o Identify any trends over time o Project future levels for the time series  Data Sources:  Statistical Studies: Observational  In observational (nonexperimental) studies no attempt is made to control or influence the variables of interest. o A survey  Statistical Studies: Experimental  In experimental studies the variable of interest is identified first.  Then one or more variables are identified and controlled so that the data can be obtained about how they influence the variable of interest  Time Requirement:  Searching for information can be time consuming  Information may no longer be useful after a certain time period  Cost Acquisition:  Organizations often charge for their information  Data Errors:  Using data with little care can lead to misleading information  Descriptive Statistics:  Tabular  Graphical  Numerical  The most common numerical descriptive statistic is the average (or mean)  The average demonstrates a measure of the central tendency, or central location of the data for a variable  Statistical Inference:  Population: the set of all elements of interest  Sample: a subset of the population  Statistical Inference: the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population  Census: collecting data for the entire population  Sample Survey: collecting data for a sample

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