St-260 Exam 1 Study Guide
St-260 Exam 1 Study Guide ST 260
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This 6 page Study Guide was uploaded by Carter Cox on Tuesday September 20, 2016. The Study Guide belongs to ST 260 at University of Alabama - Tuscaloosa taught by Dr. Marcus Perry in Fall 2016. Since its upload, it has received 182 views. For similar materials see Statistical Data Analysis in Business at University of Alabama - Tuscaloosa.
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Date Created: 09/20/16
ST-260 Study Guide 1 Statistics - Science of … Data o Collecting o Organizing o Summarizing o Interpreting - Purpose of Decision making Types of Statistics - Descriptive o Summarizes data - Inferential o Summarizes and allows inference to a population o Specify Parameters Data Types Variable - Any measurable characteristic of a person or thing - Always observe… when reviewing data set o The number of variables o The type of variable Types of Variables - Quantitative o If averages or differences have meaning Length, width, time, income o Discrete Countable possible outcomes o Continuous Any value in an interval can occur - Qualitative (categorical) o Classify people or things Color, nationality, gender o Ordinal Natural ordering Good, better, best o Nominal By name only, no natural ordinal Male, female, red blue Interval Vs Ratio Data - Difference is that interval data does not have an established non arbitrary zero point - Example: temperature interval data Graphic Summaries Graphs - Single Quantitative Variable o Histogram Best for large data series o Stem and Leaf Plots Best for small data set Preserves actual data values o Dot Plot Small data plots Quick and easy - Single Categorical Variable o Bar Chart Similar to Histogram Order of bars lacks meaning o Circle Graph/ Pie Chart Small slices are problematic - Two Quantitative Variables o Scatter Plot o Curvilinear, typical value, spread, outlier o Time Series Plot - Two Categorical Variables o Two Way Table Organizational tool Joint frequencies and marginal counts o Stacked Bar Chart Bar chart with segmented bars - 3D Graph o Often hard to read o Messy and uninformative Key Features of a Graph - Shape - Typical Value - Spread - Outlier Shapes of Data Distributions - Symmetric o Left and right halves are “mirror images” - Skewed Left o Extreme values extend to left or negative direction - Skewed Right o Extreme value extend right in positive direction Numerical Summaries Population of Interest - things we wish to learn about Key Characteristics - Typical values or variation Parameters - True value for a population First Principle - Numerical summaries should quantify key characteristics of a data set o Location and variation Measure of Location/ Center - Mean - Symmetric Distributions - Median – Distribution with outliers - Mode – Categorical Variable - Trimmed Mean – Distribution with Outliers Probability Probability - Numerical value expressing the degree of uncertainty regarding the occurrence of an event - Two building blocks o Random experiment giving rise to uncertain outcomes o Random experiment outcomes Sample Space - Set of all possible outcomes of a random experiment Event - Is any subset of the sample space Relationships among events - compliment o set of all possible outcomes in a sample space that do not belong to the event - Union (or) o Set of all possible outcomes that belong to at least one of the two events (A u B) - Intersection o Set of all possible outcomes that belong to both events (A n B) - Disjoint or Mutually Exclusive o Events that have no outcome in common Important Formulas Mean - Average of the data - Add the numbers up then divide by how many number - Example: o 1,3,5,6,7 o 22/5…. Median - Put numbers in order least to greatest - The number in the middle is the median Mode - Value that occurs most often Range - Difference between highest and lowest value in a data set 1 Quartile (Q1) - Median of the lower half of the data set rd 3 Quartile (Q3) - Median of the upper half of the data set Interquartile Range (IQR) - (Q3 – Q1) Upper Limit - (Q3 + 1.5 x IQR) Lower Limit - (Q1 – 1.5 x IQR) Union - A u B - The (u) is an or Intersection - A n B - The (n) is an and P(A given B) - P(A n B)/ P(B) Additive Rule - P(A u B)= P(A) + P(B) – P(A n B) - If mutually exclusive o P(A u B)= P(A) + P(B) Multiplicative rule - Any two events o P(A n B)= P(A)P(B given A)= P(B)P(A given B) - Independent o P(A n B)= P(A)P(B) Bayes Theorem Steps - Define the events - Assign Probabilities - Plug the probabilities in
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