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## Business Statistics 2340 Week 1 & 2 Notes

by: Bergen Eskildsen

4

0

2

# Business Statistics 2340 Week 1 & 2 Notes OIS 2340

Marketplace > University of Utah > Statistics > OIS 2340 > Business Statistics 2340 Week 1 2 Notes
Bergen Eskildsen
The U

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These notes cover what was taught in class and should prepare you for the homework Chapter 1 & 2 HW assignment
COURSE
PROF.
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
Statistics, Graphs, Data Analysis
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## Popular in Statistics

This 2 page Class Notes was uploaded by Bergen Eskildsen on Thursday August 18, 2016. The Class Notes belongs to OIS 2340 at University of Utah taught by Mohammad Amin Morad in Fall 2016. Since its upload, it has received 4 views. For similar materials see Business Statistics in Statistics at University of Utah.

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Date Created: 08/18/16
Business Statistics Notes Week 1 & 2: August 23, August 30 Quantitative Data:  Discrete (e.g. number of children)  Continuous (e.g. weight, volume) Qualitative Data: information about qualities; information that can't actually be measured Nominal Data (nomial­names)  Lowest form of data (e.g. comparison)  Nonnumeric label/code  Equality (=) relationship  No meaningful data  (e.g. What is your gender?...male, female / What is your hair color?...blonde, brown,  black, other ) Ordinal Data (numerical)  All characteristics of interval data  Data labels with order  Equality (=) greater, less than ><  No meaningful interval distance  (e.g. How do you feel today?..1. very happy 2. Unhappy 3. Okay 4. Happy 5. Very  happy) Interval Data  Characteristics of ordinal data  No meaningful zero or ratio  Distance between 2 data can be measured Ratio Level Data:  All characteristics of interval data   True Zero Poin  (zero means “none”) o \$0 balance is no money in account Statistics: Corresponding measures computed for a sample 4 types of statistics: Simple random, stratified, systematic, cluster 1. Simple Random: equal chance 2. Stratified: Divide population into subgroups (strata) according to common  characteristics 3. Systematic:   decide sample size (n)   divide ordered frame of (N) individuals into groups of (k) individuals (K=N/n)  st  Randomly select one individual from 1  group 4. Cluster: Divide into clusters each representative of population, select simple random  samples of clusters Descriptive Statistics: uses data to provide descriptions of population either through numerical  calculations or graphs/tables Inferential Statistics: Makes inferences/predictions about population based on sample data  taken from population in question Parameters: descriptive numerical measures, such as average or proportion, computed from  entire population Time series: is a sequence taken at successive equally spaced points in time Cross­sectional data: collected by observing many subjects (such as individuals, firms,  countries, or regions) at the same point of time, or without regard to differences in time. Data Collection issues:  Data accuracy  Interview Bias  Nonresponsive Bias  Selection Bias  Observer Bias  Measurement Error  Interval Validity  External Validity  Histograms: only used to plot the frequency of score occurrences in a continuous data set that  has been divided into classes, called bins. Cannot have gaps between bars. QUANTITATIVE Frequency Distributions: should have between 5 and 20 classes. a mathematical function  showing the number of instances in which a variable takes each of its possible values.

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