BDA3053_Lectures01_02.pptx - BDA 3053 Business Data...

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BDA 3053: Business Data Analysis Lectures 1 & 2 David Pumphrey
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Course Overview Course meetings Tuesdays and Thursdays, 1:00 – 2:20 PM, RTH 313If necessary, I can schedule help sessions or lab sessions Course description This is a second course in business data analysis, with brief coverage of analytics programming, regression analysis methods, ANOVA, selection techniques and path analysis. Prerequisites An introductory statistics course at the undergraduate-level, which covered topics such as: descriptive statistics, applications of basic probability theory, hypothesis testing, confidence intervals, correlation and simple linear regression, and introduction to one-way analysis of variance. Although helpful, no specific knowledge of R, Python, or other analytics data/analysis toolset is required. Knowledge of core programming control structures (sequence, branching, & iteration) is helpful. Adapted from Lectures by Dr. John Bentley, Univ. of Mississippi, Fall 2010
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Brief Review of Statistics Classification of Variables Variable classification by values Discrete vs. continuous A discrete variable is characterized by gaps or interruptions in the values that it can assume. Examples include sex, religious affiliation, treatment group (i.e., active drug vs. placebo), the number of visits to an emergency room (sometimes called a count variable), and response to treatment measured as excellent, good, fair, poor (sometimes called an ordinal variable). Some researchers use the terms “ qualitative ” or “ categorical ” for a discrete variable. A discrete variable with two categories is called dichotomous (or binary) A continuous variable does not possess the gaps or interruptions characteristic of a discrete variable; in other words, a continuous variable can take on any value within a defined range. Examples include blood pressure, temperature, and height. Quantitative ” variable can be used to describe a continuous variable. Adapted from Lecture by Dr. John Bentley, Univ. of Mississippi, Fall 2010
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Brief Review of Statistics Classification of Variables Variable classification by values Discrete vs. continuous Discrete variables can sometimes be treated as continuous variables when analyzing data (e.g., a variable representing age categories, 0 to 4, 5 to 9, 10 to 14, etc. can be treated as a continuous variable). Another example: Pain measured on a 7-point scale. In addition, continuous variables can be collapsed into discrete variables by specifying cutoff values on the continuous scale. This can sometimes be useful when there are meaningful cutoff values (e.g., when a score above a certain value is indicative of pathology on a diagnostic test) but in other situations it can lead to substantial loss of information.
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  • Spring '12
  • Dr.Campbell
  • Normal Distribution, Statistical hypothesis testing, Dr. John Bentley

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