Mean, median, mode, kurtosis, standard deviation, skewness, and variance.
How to visually display the descriptive profile of the data
a). Frequency Analysis One Way Frequency Tables/Graphs A table showing the number (n) or percentage (%) of respondents choosing each answer to a survey question.
b). Cross Tabulations Examination of the responses to one question relative to the responses to one or more questions in a survey set.
Cross tabulation two items - “Business Category” and “Gender”
Additional filtering criteria - “Veteran Status” - Now filtering three items.
c). Descriptive Statistics Effective means of summarizing large sets of data. Key measures include: mean, median, mode, kurtosis, standard deviation, skewness, and variance. Measures of Central Tendency! Mean Median Mode Measures of Dispersion! Variance Range Standard Deviation Skewness
d). Graphical Representation Line, Pie, and Bar Charts Line Charts: Good for demonstrating linear relationships. Pie Charts: Good for special relationships among data points. Bar Charts: Good for side by side relationships / comparisons
2. I want to test the differences between Groups of people or things!
Differences between two groups (e.g., males and females)
Measure of difference: T-statistic
Differences between two or more groups (e.g., age groups)
Measure of difference: F-statistic
Measures of Difference
Significance of Difference (p>0.01)
Needs to look at means or run additional statistic to identify ‘where’ the difference are!
No Apparent Relationship Between X and Y X Y Perfect Positive Relationship Between X and Y X Y Perfect Negative Relationship Between X and Y Parabolic Relationship Between X and Y X Y
General Positive Relationship Between X and Y X Y No Apparent Relationship Between X and Y X Y Y X Negative Curvilinear Relationship Between X and Y General Negative Relationship Between X and Y X Y
Group people or objects based differences between and similarities within (segmentation)
Group data to most important related to criterion (11 items = 2 dimensions of satisfaction)
Visual representation of perceptions by groups (brand associations)
Value of people’s rankings of important product attributes (consumer choice > price, quality, location)
Cluster Analysis The general term for statistical procedures that classify objects or people into some number of mutually exclusive and exhaustive groups on the basis of two or more classification variables. Cluster 1: Men Cluster 2: Women Cluster 3: People with Green Cars
Factor Analysis Procedure for grouping & simplifying data by reducing a large set of values/items to a smaller set of factors/dimension of a variable by identifying dimensions in the data . Factor Loading: Correlation between factor scores and the original variables.
Ratio, Interval, Ordinal, Nominal = Statistical power
What do you want to find out?
Describe how data is distributed
Group differences between two or more groups
Relationships between two or more variables
Who falls into which grouping
Customer preference criterion
How much missing data?
How big is sample size?
How was data collected – random or non-random?
The content of this work is of shared interest between the author, Kelly Page and other parties who have contributed and/or provided support for the generation of the content detailed within. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 2.0 UK: England & Wales. http://creativecommons.org/ Kelly Page (cc)