Transcript Essential Statistics: (what I should know about stats)

(a.k.a: The statistical bare minimum I should take
along from STAT 101)
 Definitions and relationships as presented on the sheet Anatomy of the
Basics: Statistical Terms and Relationships
 Identification of variables and their characteristics
 Careful review of data and their presentation
 Providing a context for the data
 Why percentages and not numeric counts when making comparisons
Essentials: Sampling
(stuff I should know)
• General types of data collection
• Importance of randomization in obtaining samples
• Sampling Error
• Difference between non-probability sampling and
probability sampling
• Different types of random samples and how each is
obtained
• Ability to obtain samples using probability sampling
approaches
 Definitions: Permutation; Factorial; Combination.
 What a Factorial is and how to use it.
 Ability to determine the number of permutations or
combinations resulting from a stated situation.
 Extras here: Tree diagrams & the multiplication rule.
 Characteristics of qualitative variables.
 Building a qualitative frequency table.
 Appropriate charts/graphs for qualitative data (and how to
make them).
 Characteristics of quantitative variables.
 Building a quantitative frequency table.
 From within a quantitative frequency table, be able to identify:
classes, class widths, class midpoints, class limits, boundaries
(cutpoints)
 Identify and construct appropriate charts/graphs for
quantitative data.
 Understand what Sigma (S) means and how it is used.
 Be able to interpret what S is telling you to do in a given
formula.
 When you think you’ve got it, practice some more.
 Be able to identify the characteristics of the median, mean
and mode, and to which types of data each applies.
 Be able to calculate the median, mean and mode, as
appropriate, for a set of data.
 Affected by vs. resistant to extreme values. What are the
implications for the mean and median?.
 Be able to explain what constitutes a distribution.
 Be able to identify Left, Right and Normal distributions
(and a Uniform distribution).
 Be able to determine if a distribution is normally distributed
or skewed through use of a formula or computer software
and, be able to interpret the results of this process.
 Know the types of measures used to look at variation and the type data
to which they apply.
 Be able to calculate the range, standard deviation and inter-quartile
range.
 Be able to determine the distance away from the mean a given value lies
in terms of standard deviations (think z-score).
 Be able to apply the Empirical Rule and Chebychev’s Theorem to
specific situations.
 Know the types of measures used to look at specific positions within a
data distribution.
 Be able to calculate the inter-quartile range, three quartiles, Pearson’s
Index of Skewness, z-score, Coefficient of Variation.
 Be familiar with symmetry vs. skewness and distribution shapes.
 Be able to build both traditional and modified box plots (aka: box-and-
whiskers plot).
 Correlation – potential relationships, not causality.
 Know the steps one might employ before obtaining a correlation.
 Know the characteristics of the Pearson Product Moment Correlation
Coefficient (for us the correlation).
 Be able to calculate a correlation and determine if it is statistically significant.
 Be able to create a scatter plot of the paired data being studied.
 Be able to determine the directionality of a correlation and its strength via
formula and observation of plotted data.
 Understand what the regression process does - prediction.
 Be able to state the steps we use leading up to the decision to conduct
regression.
 Be able to calculate the slope of a line and the y-intercept.
 Be able to calculate a regression equation and apply it to the prediction of other
values. Know that these are estimates, not necessarily the actual values that
might occur.
 Know what the Least Squares Property and Line of Best Fit. Residual –
what’s that?