BSB123 - Data Analysis Mid-Semester Exam Notes (Summary of Lectures 1 - 6)

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Department
Management and Human Resources
Course
BSB123
Professor
All Professors
Semester
Spring

Description
Data Analysis Mid-Semester Notes Lecture 1: Introduction to Statistics Statistics: processing and analysing data Descriptive: collecting, presenting and characterising Inferential: use sample to draw conclusions about the population Population: whole dataset Sample: subset Parameter: numerical measure that describes a characteristic of a population Statistic: numerical measure that describes a characteristic of a sample Primary source: collect yourself or internally Secondary source: buy data/external source Collecting Data Important Sources 1. Data distributed by organisation or individual 2. Designed experiment 3. Survey 4. Observational study Data  Categorical  Nominal: no order  Ordinal: order to categories  Numerical  Discrete (finite e.g 1,2,3) OR continuous (infinite 1.2713…)  Interval (no ratio proportions) OR ratio (ration comparisons)  Time series (time element) OR cross sectional (one point in time) Graphing/tables  Categorical Data  Summary table  Bar graph (good for frequency comparisons)  Pie graph (good for proportions) Christina Meyers BSB 123 Data Analysis 1 Lecture 2: Presenting data in tables and charts & Introduction to descriptive measures Numerical Data Ordered array: ordered from smallest to largest Frequency distribution: summary table in which data is arranged into numerically ordered classes Histogram: graph of continuous data in a frequency distribution (no gaps) Class intervals and boundaries Range  max min range Classwidth no. of classes Lowerboundary Upperboundeary Classmid-point 2 Rule of thumb: Usually at least 5 classes, but no more than 15 Two Variables: Bivariate Data x: Independent variable y: dependent variable Does (dependent variable, y) depend on (independent, x)?  Categorical vs categorical  Contingency table (allows for cross-tabulation of data)  Clustered bar chart/stacked bar chart (converts contingency table into graphical form)  Numerical vs numerical  Scatter plot (x,y pairs of data)  Line chart (time series – time is the independent variable, against a dependent variable)  Numerical vs categorical  Pivot table Positive relationship: as x increases, y increases Negative relationship: as x increase, y decreases No relationship: random movements of x and y Christina Meyers BSB 123 Data Analysis 2 Central tendency Mean: average (sum of values divided by the no. of values)  X i x  n Median: middle number of an ordered array Medianposition n 1 3 Rule 1: If Data set is even, median is the average of the two middle ranked values Rule 2: If Data set is odd, median is the middle ranked value Mode: most frequently observed value [may be no mode or several modes (bimodal)] Quartiles Split the ranked data into 4 segments, with an equal number of values per segment Q1 Q2 Q3 Q4 25% 25% 25% 25% n1  n1 3(n1)  4(n1) 4 2 4 4 (Median) Rules Rule 1: If the result is an integer, then the quartile is equal to the ranked value Rule 2: If the result is a fractional half, then the quartile is equal to the mean of the corresponding ranked values Rule 3: If the result is neither an integer nor a fractional half, round the result to the nearest integer and select that ranked value Box and whisker plot Christina Meyers BSB 123 Data Analysis 3 Lecture 3: Numerical descriptive measures Variation Range  max min Outliers can lead to an untrue indication of the range Interquartile range IQR Q3Q1 Ignores extreme values Variance: Measure of variation based on squared deviations from the mean Population Variance 2 2  (X  )   N Sample Variance 2 S   (x  x) n 1 Standard Deviation Only measures 1 variable Population Standard Deviation    2 Sample Standard Deviation 2 S  S Coefficient of Variation: Relative measure of variation, the standard deviation divided by the mean, multiplied by 100% CV  S x Covariance Measures direction of linear relationship between two numerical variables i.e positive or negative (x  x)(y  y) Cov(X,Y)   n1 Christina Meyers BSB 123 Data Analysis 4 Correlation Measures the direction and strength of the relationship Cov(X,Y) rXY  S x y Z Score The difference between a given observation and the mean, divided by the standard deviation Z  x  x S A Z Score above 3 below -3 is considered an outlier. An outlier can cause numerical measures to be distorted, resulting in misleading overall trends Shape Longer left tail Longer right tail Christina Meyers BSB 123 Data Analysis 5 Lecture 4: Simple Linear Regression and Introduction to Probabili
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