Chapter 2 - Data collection and analysis

# Chapter 2 - Data collection and analysis - Chapter 2 Data...

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Chapter 2 Data Collection and Analysis

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Data collection Cross-sectional data versus time-series data Categorical data Nominal versus ordinal Numerical data “a snapshot” “series of observations over time”
Data collection Example: I survey the class and collect the following data Gender Age Number of siblings Letter grade you think you will receive in ITIS 1P97 What type of data is each of these

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Summarizing data Descriptive statistics Measures of central tendency Mean, µ = AVERAGE(range of values) Median - has just as many values above it as below it MEDIAN(range of values) Mode – the most frequent value of the variable MODE(range of values)
Summarizing data Descriptive statistics Measures of variance Range – subtract the largest value observation from the smallest value observation Variance of a population VARP(range of values) Variance of a sample VAR(range of values)

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Summarizing data Descriptive statistics Measures of variance Standard deviation of a population = σ STDEV.P(range) Standard deviation of a sample = s STDEV.S(range)
Summarizing data Statistical displays For categorical data Ordinal data – bar chart For nominal data, can also create “Pareto chart” 0 2 4 6 8 10 12 Weekly Customer Survey frequency 0 2 4 6 8 10 12 Percentage of Sales

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Histograms Graphical representation of a frequency distribution Create “bins” – a set of ranges of values of the variable Assign each observation to its appropriate bin Plot the number of observations in each bin To distinguish them from bar graphs, histograms are shown with no gaps between the bars.
Probability and Random Variables Probability Measures uncertainty The likelihood of an event occurring A number between 0 (no chance of occurring) and 1 (absolutely sure to occur)

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Probability and Random Variables Subjective probability Logical probability Experimental probability
Probability and Random Variables Basic terms experiment (or sample) - activity that results in outcome. outcome - result of experiment. sample space - all possible outcomes of experiment. event - a collection of outcomes

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Probability and Random Variables Event relationships Independent events Dependent events Mutually exclusive events Collectively exhaustive events Complements law
Probability and Random Variables Union of events – A or B Intersection of events – A and B

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## This note was uploaded on 11/04/2011 for the course ITIS ITIS 1P97 taught by Professor Dr.susansproule during the Fall '11 term at Brock University.

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Chapter 2 - Data collection and analysis - Chapter 2 Data...

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