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Data and Statistics for Business Analytics Fundamentals

by: Evy

Data and Statistics for Business Analytics Fundamentals ISDS 361A

Marketplace > California State University - Fullerton > INFORMATION SYSTEMS > ISDS 361A > Data and Statistics for Business Analytics Fundamentals
Cal State Fullerton
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Ch. 1 (Week 1 Notes) Lecture and Reading, Includes Key Terms for the fundamentals of Data and Statistics for use in Business Analytics. (ISDS 361A Cal State Fullerton)
Business Analytics I
Mike F. Huang
Class Notes
#BusinessAnalytics #Business #Analytics #Stats #Statistics #Tutor #Notes, isds361a, Statistics, business, Analytics, key, terms, ch1, week1




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This 4 page Class Notes was uploaded by Evy on Tuesday August 30, 2016. The Class Notes belongs to ISDS 361A at California State University - Fullerton taught by Mike F. Huang in Fall 2016. Since its upload, it has received 33 views. For similar materials see Business Analytics I in INFORMATION SYSTEMS at California State University - Fullerton.

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Date Created: 08/30/16
DATA AND SATATISTICS Tuesday, August 23, 2016 5:42 PM Chapter One - ISDS 361 A - Week One 1.1: Applications in Business and Economics 1. Accounting: Uses in Auditing, and ProjectingAccounts Receivable 2. Finance: Used in Stock Analysis, Review of Financial Data, Earning Ratios, Determining Dividend Payouts, and Potentialsuccess of Investments. 3. Marketing: Scanner Data, What is being sold, what has sold, what might sell, Success of promotionsand marketing campaigns, relationship between Promotionactivities and sales. 4. Production: Quality control, X-bar charts for output and production process 5. Information Systems: Operations, Computer networks,asses performance of LANS, WANS and other network programs and uses. 1.2 Data AnalysisIntro: DATA: Facts and Figures, Often Very Large, Up for interpretation, Stored in Databases. Surveys: Random Samples, Used to collect DATA. Information: How do we extract useful information from this data? This is here Statistics Comes In. Why in Business? Affect Advertising has on sales…. Relationships and correlations between business practices and sales. Sales as to Revenue Determineeffects on diversity and cross-cultural relations. Reliability of Analysis What is Business Analytics? Use of Statistics in Business Decision Making. Scientific process of transforming data into insights for making better business decisions. SCALES OF MEASUREMENT: Nominal, Ordinal and Interval Nominal: Qualitative or Quantitative LABELS are used to distinguish categories (Full-Time, Part-Time,Seasonal or Volunteer Work) Ordinal: Ranking that is meaningful amongst categories. (Class Rank for graduating Seniors, broken into 20th percentiles) Interval: Numerical in Nature (SAT Scores, Point system) Cross Sectional Data vs. Time Series Data: Cross Sectional:collected around the same time. - current test scores Time Series Data: Data Collected over longer period of time - Yearly profit Observation: Set of Measurementsobtained for EACH ELEMENT of a data set. THIS MAKES the # of Observations= the # of Elements. AKA: # of Measurements= # of Variables Quantitative Data: Numerical Data CategoricalData: Data denoted by labels or categories (can be numbers, but arithmetic evaluations won't work). Quantitative Data is EITHERDISCRETE or CONTINUOUS Discrete:Measure of HOW MANY Continuous: Measures HOW MUCH Types of Statistics: Descriptive-Summary of Statistics (Important componentsof the data Set.) Collecting, Organizing, and presenting in Charts. Inferential: Drawing Larger Conclusions based on data set. KEY TERMS (Inferential Statistics): Population: Set of Items Being Studied Parameter (Variable) What is being studied? What componentis of interest? Sample: (Random subset chosen from the larger population Statistic: Calculated from the sample. Sampling: Examining part of the whole of a population. (costly) Random Sample - Everyoneor thing in population has equal chance of being selected. (Equal Probability) Sample size matters - too small of a sample yields poor statistical results. Random Samples are considered one of the best ways to obtain a representativeset of data (best representationof The population being studied.) BIASES: Selection: When subsets of the population have little to no chance of being selective."Selecting the Selected" Non-Response: Answers not truthful, answers forfeited, answers not obtained from groups of the population being studied. Measurement Errors: Not asking right questions, ambiguity, inaccurate recording. Voluntary Responses are often Biased. (Phone-Ins) Experimental vs. Observational. Experimental - Controlled groups, variables clearly identified for study. Observational:No attempt to control the population being studied. Non-Random samples lead to inaccuracies, and possible ethics problems. Non-Random samples lead to inaccuracies, and possible ethics problems. OVERVIEW: Statistic is the science of collecting, analyzing, presenting, and interpretationof data. Facts and figures collected and analyzed. Four Scales of Measurement:Nominal, Ordinal, Interval, and Ratio. Data can be classified as Quantitative or Categorical KEY TERMS: Statistics:science of collecting, analyzing, presenting, and interpreting data. Data: Facts and figures Collectedanalyzed and summarized for interpretation. Data Set: All the data collectedfor a study Elements: Entities on which data is collected (students in college) Variable: A characteristic of interest for the elements. (Working and Non-Working students) Observation: Set of measurementsobtained for an element Nominal Scale: when the data are LABELS OR NAMES used to identify an element. Ordinal Scale: The scale of measurementfor a variable if the data exhibit properties of nominal variables AND a RANKING order. (numeric or nonnumeric) Interval Scale: The scale of measurementfor a variable if the data demonstratethe same properties of ordinal data, but is expressed in fixed units of measure. (Always Numeric) Ratio Scale: Scale of measurementfor a variable if the data demonstrateall the properties of interval data, and the relationship or ratio between two value is meaningful. (Always numeric) CategoricalData: The names or labels used to identify an attribute of elements. (Can be numerical, but the relationship between the numbers are meaningless - Floors of a parking Garage Floor 1, 2,3 or 4, cannot add or subtract, only labels) Used on Nominal and Ordinal Scales. Quantitative Data: Numeric values that indicate how much or how many of something. (Interval or Ratio Scale) Relationship between numbers IS meaningful. CategoricalVariable: variable with categorical data (duh) Quantitative Variable: Variable with quantitative data (duh) Cross Sectional Data: Collected at the same or approx same time. Time Series Data: Data collected over several time period (to see growth or progression) DescriptiveStatistics: Tabular, graphical, and numerical summariesof data. Population: Set of all elementsof interest in a particular study. Census: survey for ENTIRE population Sample Survey: survey to collect data on a sample. StatisticalInference: process of using data to make hypothesis and estimates about characteristicsof a population. - For business this is used to judge organization growth changes, diversity, organizational trends, productivity,factors affecting growth, productivity, job satisfaction, etc. Data Mining: process of using procedures from stats and comp science to extract info from large databases. -Used for determining what does and doesn’t sell, who buys, spending trends, buying trends, product evaluation, success for promotionalcampaigns, etc. Business Analytics: scientific process of transforming data into insights for making better business decisions.


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