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## BNAD 276 Week 3 Notes

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Marketplace > University of Arizona > BNAD 276 001 > BNAD 276 Week 3 Notes
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These are the notes for week 3 of BNAD 276.
COURSE
Statistic Inference in Management
PROF.
Dr. Suzanne Delaney
TYPE
Class Notes
PAGES
3
WORDS
KARMA
25 ?

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This 3 page Class Notes was uploaded by madelinef on Thursday September 8, 2016. The Class Notes belongs to BNAD 276 001 at University of Arizona taught by Dr. Suzanne Delaney in Fall 2016. Since its upload, it has received 29 views.

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Date Created: 09/08/16
BNAD 276 Week 3 Notes  Correlation does not imply causation. o Is it possible that they are causally related? Yes, but the analysis does not answer that question o Even if it is a perfect correlation it still doesn’t cause causation o Ex: birthday cake doesn’t make you older  Linear Vs. Curvilinear Relationships o Linear relationship- a relationship that is described with a straight line o Curvilinear relationship- a relationship that can be described best with a curved line.  Not appropriate for correlation o When talking about correlation you need to say…  Positive or negative (direction)  Established values (actual number)  Variables  Strength (weak, moderate, strong)  Ex: This shows a strong negative relationship (r=-0.9) between a and b.  Frequency distribution- an organized list of observations and their frequency of occurrence o Guidelines for creation: o Classes should be mutually exclusive: each observation should be represented only once. No overlap between classes. Ex: political affiliation, gender, number of kids in family. o Set of classes should be exhaustive: Should include all data value possibilities! No data falls out of range. Ex: 0-3, 6-8, 9-11 is WRONG! But 0-3, 4-7, 8-11, 12-15= Right! o All classes share equal sized intervals (even if frequency for class is 0).  Wrong: 0-9, 10-12, 13-19  Right- 0-4, 5-9, 10-14 o Selecting number of classes is subjective  Generally, 5-15 will often work o Example Score Frequency Cumulative Relative Relative Frequency frequency cumulative frequency 95-99 2 28 Divide total Same as number by relative frequency to frequency get a ratio. Multiply by % to get a percent. 90-94 3 26 85-89 5 23 80-85 5 18 75-79 4 13 70-74 3 9 65-69 1 6 60-64 3 5 55-59 1 2 50-55 1 1  On a frequency histogram, frequency is on the y axis  Pareto chart- categories are displayed in descending order of frequency  Stacked bar chart- bar height is the sum of several subtotals  Simple line chart- often used for time series data (continuous data) o Space between dots implies a continuous flow  Pie chart- general idea of data that must sum to a total. o Use with caution! Often overused. o 2D is better than 3D usually because the third dimension doesn’t add much Frequency Distributions  The normal curve- bell curve shaped  Central tendency- where’s data values concentrated? What are the typical or middle data values? o Mean- the balance point of distribution. Found by adding up all observations and then dividing by the number of observations.  For a sample x/n= mean  For a population X/N= mean  = add up, x or X= scores, n or N= number of scores o Median- middle value when observations are ordered from least to most (or most to least).  Numbers must be in numerical order to start  If there is 2 medians, take the average of them  1 quartile- middle number of the lower half  second quartile- median  3 quartile- middle number of upper half o mode- the value of the most common observation  bimodal distribution- if there are 2 most frequent observations o trimmed mean- trim off a bit at the top and bottom to get rid of outliers.  Dispersion- how much variation is there in data? How spread out are the data values? How unusual are they? Standard deviation, variance, range, absolute deviation  Shape- are the data values distributed symmetrically? Skewed? Sharply peaked? Bimodal? Central Tendencies for Qualitative Data  Mode Is good for nominal or ordinal data  Median can be used for ordinal data  Mean can be used with interval or ratio data  Measure of central tendency- describes how scores tend to cluster to the center o In all distributions  Mode=tallest part  Median=middle score  Mean= balanced part  Positively skewed distribution: mode<median<mean  Negatively skewed distribution: mean<median<mode  Means most affected by outliers or skewed distribution!  Variability- some distributions are more valuable than others. o The larger the variability, the wider the curve and vice versa o Range- difference between largest and smallest number

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