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by: Taylor Hall

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# Statistics Week One Notes MGSC 291 001

Marketplace > University of South Carolina > MGSC 291 001 > Statistics Week One Notes
Taylor Hall
USC
GPA 3.94

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This covers the beginning notes about the importance of Statistics and covers Chapter One and the beginning of Chapter Two. Chapter One is the introduction to Statistics, including many definitions...
COURSE
PROF.
Stacey Mumbower
TYPE
Class Notes
PAGES
3
WORDS
CONCEPTS
Statistics
KARMA
Free

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This 3 page Class Notes was uploaded by Taylor Hall on Saturday August 27, 2016. The Class Notes belongs to MGSC 291 001 at University of South Carolina taught by Stacey Mumbower in Fall 2016. Since its upload, it has received 15 views.

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Date Created: 08/27/16
Statistics: Intro information  Data: What is it? o information we collect and organize o facts and figures o numbers and text  What is the point of Statistics? o To process data so that it is useful o Provide meaningful information in an easily accessible way o Answer questions o Tell a story  Help business leaders o Improved insight about operations o Make decisions that are: (prove your opinion is right)  Data-driven  Fact-based o Not based on speculation o EX: UPS, to increase productivity  Benefits of Data and Statistics o Reduce cost o Increase profit o Increase productivity o Better manage risk o Increase customer satisfaction  Companies need: o Translate data o Skills in high demand  Statistics  Data analysis  Business analysis  Ability to communicate Chapter 1: Intro to Statistics and Data  Population: consists of all items with the characteristics we want to understand  Sample: subset of the population  Important because we don’t have enough money, time and if it’s impossible  There is always a trade-off between accuracy and costs  Sample Statistics: the percentage from the sample  Population parameter: the unknown percent of voters  we use the sample statistic to make inferences about the unknown population parameter  inferences: act of deriving logical conclusion from premises known or assumed to be true  Types of Statistics:  Descriptive Statistics: summarizing important aspects of a data set. Includes collecting, organizing and presenting data (Chapter 1-3)  Inferential Statistics: Drawing conclusions about a population sample data from a population (Chapter 4-9, 12)  Data: facts and figures, numbers or text, collected through some type of measurement process  Data set: collection of data  Observations: rows of data  Variables: columns of data  Subject: individual IDs that represent who is surveyed  Ways data can be collected:  Times series data: Records a characteristic of a subject over several time periods (daily, weekly, monthly…) often displayed as a line chart  Value: response to a survey  Types of variables you can select  Qualitative: variables that use labels or names to identify distinguishing characteristics of observations  Quantitative: Meaningful numerical values  Discrete variable: a countable number of values  Continuous: infinites values in an interval  Measurement Scales:  Qualitative:  Nominal: categories for grouping the data  Ordinal:  Categorized and ranked by characteristics and trait  Magnitudes are meaningless and unknown (poor, fair, good, ect.)  Quantitative:  Interval:  Categorized and ranked by characteristics and trait  Magnitudes are meaningful  No absolute zero or starting point defined o Cannot take a ratio  Ratio:  Categorized and ranked by characteristics and trait  Magnitudes are meaningful  There is an absolute zero, and can make a ratio Chapter 2: Representing Data Through Tabular and Graphical Methods  Summarizing Data o Qualitative Data:  Frequency Distribution: Nominal information  Group data into categories and record how many observations fall into each category  Bar Chart: x axis has qualitative variable  X axis: categories  Y axis: frequencies  Relative Frequency Distribution  Frequency distribution (divided by) total observations o Quantitative Data  Frequency Distribution: groups data into intervals called classes and records the number of observations that fall into each class  Rules for classes: o Must be mutually exclusive: no overlapping o Must be exhaustive: no observations excluded form classes  Greater than, or equal to 500 but less than 600  Histogram: A visual representation of a frequency or a relative frequency distribution for a quantitative variable  Bar height represents frequency  Bar width represents class width  Will learn the different types in the future  Cumulative Relative Frequency:  Cumulative Frequency (divided by) total observations  Ogive: is a visual representation of a cumulative frequency or cumulative relative frequency distribution o Plot the cumulative frequency or CRF of each class using the upper limit of the class o Connect the neighboring points  Stem and Leaf Diagram  Stem: left most digits  Leaf: last digits  Scatter Plot: used to depict two potentially related variables  Each point is a pairing  Positive Linear  Positive curvilinear  Negative linear  Negative Curvilinear

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