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by: Morgan Owens

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# Study Guide for Exam 1 Bus 210

Marketplace > George Mason University > Bus 210 > Study Guide for Exam 1
Morgan Owens
Mason
GPA 3.5

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here is a study guide for exam 1. It combines the study guides she filled out and notes on how to complete things in Excel such as formulas. Good luck!
COURSE
PROF.
Toni C Garcia
TYPE
Study Guide
PAGES
6
WORDS
KARMA
50 ?

## Popular in Department

This 6 page Study Guide was uploaded by Morgan Owens on Wednesday October 5, 2016. The Study Guide belongs to Bus 210 at George Mason University taught by Toni C Garcia in Fall 2016. Since its upload, it has received 53 views.

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Date Created: 10/05/16
Exam 1 study guide Chapter 1 1) Business Analytics: analysis of very large data sets. 2) Data Analysis: data description, inference, and relationships in the data. Also includes decision making, which optimizes techniques for problems with no uncertainty, decision analysis for problems with uncertainty and structures sensitivity analysis. Dealing with uncertainty: measuring uncertainty and modeling uncertainty explicitly. 3) Excel Functions: a. Built in functions i. Financial Functions: b. Entering function i. Using formulas with arithmetic operators and parentheses ii. Colon notation iii. Relative and Absolute references 4) Modeling and Models: as abstraction of a real problem that tries to capture the essence and key features of the problem. Different types of models include: a. Graphical Models: making a picture b. Algebraic Models: uses algebraic equations and inequalities to specify a set of relationships in a very precise way. c. Spreadsheet Models: alternative to algebraic, relates various quantities in a spreadsheet with cell formulas. i. What-If Analysis: 1. Data Tables: one-way: single inputs impact on output two-way: 2 inputs at a time impact on output 2. Goal Seek: what input value will give you a desired output level, the goal. Changes two variables at a time. Chapter 2 Describing the Distributions of a Single Variable 1) Populations and Samples: population includes all the entities of interest in a study (people, household, machines), a sample is a subset of the population, often randomly chosen and pregerable representative of the population as a whole. 2) Data Sets: array of data, with variable in columns and observations in rows. Variables: is a characteristic of members of a population, such as height, gender and salary. Observations: is a list of all variable values for a single member of a population. 3) Types of Data: a. Numerical: a meaningful arithmetic can be performed on it. i. Discrete: numerical variable that results from a count, EX: number of children. Continuous: variable is the result of an essentially continuous measurement EX: weight/ height. ii. Binned: corresponds to a numerical variable that has been categorized into discrete categories called bins. iii. Excel functions: VLOOKUP, HLOOKUP, IF, COUNTIF b. Categorical: a meaningful arithmetic cannot be performed on it. i. Ordinal: natural order. ii. Nominal: no natural ordering. iii. Dummy Variable: a 0-1 coded variable for a specific category. c. Cross-sectional Data: data on a cross-section of a population at a distinct point in time. d. Time series Data: data collected over time. i. Time Series Graphs: graph of the values of one or more time series, using time on the horizontal axis. The place you start time series analysis. 4) Descriptive Measures for Categorical Variables a. Count (Frequency) and Percentage Distributions b. Column Chart: chart that displays data in columns c. Pie Chart: displays data in a circle( pie) for percentages. 5) Descriptive Measures for Numerical Variables b. Histograms: is the most common type of chart for showing the distribution of a numerical variable. Based off binning the variable, great way of showing the shape of distribution c. Mean: average of all values. Median: middle of the observation when the data are sorted from smallest to largest. Mode: value that appears most often. d. Minimum: highest value in data Maximum: smallest value in data Quartiles: divide the data into four groups, each with approximately a quarter of all observations. i. Boxplots: box-whisker plot, alternative chart for showing the distribution of a variable. Considered the “bigger picture” chart. e. Measures of Variability: i. Range: maximum value minus the minimum value. Interquartile Range: third quartile minus the first quartile. Variance: essentially the average of the squared deviations from the mean. Standard Deviation: square root of the variance. f. Difference for Measures between a Population and Sample g. Empirical Rules: mean they are based on commonly observed data, as opposed to theoretical mathematical arguments. h. Measures of Shape Skewness: occurs when there is a lack of symmetry. Positively (right) or negatively (left) skewed. 6) Excel Tables for Filtering, Sorting, and Summarizing Outlier: value or an entire observation that lies well outside the norm. best to run analysis of the data with and without outliers. Chapter 3 Finding Relationships among Variables 1) Relationships among Categorical Variables a. Cross tabulatio n:  is a two (or more) dimensional table that records the number  (frequency) of respondents that have the specific characteristics described in the cells of the  table.  Contingency Tables: table of counts for 2 variables at a time. b. Row percentages: for each row, you find the percentage distribution of the columns. Column percentages: for each column, you find the percentage distribution of the row. * different distributions in a row or column provides evidence of an association. 2) Relationships among Categorical Variables and a Numerical Variable a. Comparison Problem: is one of the most important problems in data analysis. It occurs whenever you want to compare a numerical measure across two or more sub populations. b. Stacked or Unstacked Data Formats stacked data: a bunch of data sets where the categories are “stacked”, mixed or combined together. unstacked data: categories and the data that belong to them are separated. 3) Relationships among Numerical Variables a. Scatterplots: a scatter of points, where each point denotes the values of an observation for two selected variables. Trend line: a line or curve that “fits” the scatter as well as possible. b. Correlation: unit less quantity that is unaffected by the measurement scale. Always between -1 and +1. Covariance: essentially an average of products of deviations from the mean. 4) Pivot Tables: tool that allows you to break down data by categories. Pivot Charts: adapt automatically to the underlying pivot table. Looking for relationship between categorical variable and numeric variable. EX: compared grades (%)(numeric) between midterm and final (categorical) - Report relative statistics for each category. - Graphs of numeric variable for each category (histograms, box plot) = month ( ) Check format to change how date is displayed. Excel (formulas and instructions) To find average- =average( ) Future value (FV)- calculated future value of regular periodic payments Payment (PMT)- calculated the periodic and level payments required to pay off a loan. (rate, nper, PV Rate=rate N per = number of payment PV= present value, loan amount. Absolute reference= enter formula with a \$ before row or column. Make the formula not update, locks it on the cell or row. Range = max(data set)- min(data set) IQR= quartile (data set),3)-quartile(data set, 1) Count If- if (blank) is true and if (blank) is true. (range1, condition 1, range 2, condition 2) If statements, - =IF(test, value if true, value if false) - Test = testing a condition Value if true= telling excel what to do if it’s true Value if false= telling excel what to do if its false EX: If grade is above 69, pass otherwise fail. OR statement - =OR(test1, test 2, test 3, ….) - Will return “true” if at least one of conditions (tests) is true. - Will return “false” if none of the conditions (tests) are met. AND statement - =AND(test 1, test 2, test 3,…) - AND returns “true” if all are true - AND returns “false” if at least 1 condition (test) is false. Example Exam question: What is a recoded month called? – dummy variable The material for this outline isfrom Business Analytics: Data Analysis and Decision Making, 6 th Edition, by S. Albright and W. Winston, 2017 Cengage Learning

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