Chapter 1

* Population – consists of members of a group which you want to draw a conclusion * Sample – portion of population

* Parameter – numerical measure that describes a characteristic of a population * Statistic – numerical measure that describes a characteristic of a sample * Descriptive statistics – collecting, summarizing and presenting data e.g. survey * Inferential statistics – drawing conclusions about a population based on sample data * Primary sources – you need to collect (start from scratch) * Secondary sources – other sources e.g. marketing/websites * Types of Data – Categorical, Numerical, Discrete, Continuous * Categorical Data – in words, yes or no, gender, colour * Numerical – numbers, how many times, income

* Discrete – counting, whole numbers

* Continuous – measuring, height, weight. Can have partial numbers e.g. 4.73 * Levels of Measurement and Measurement Scales – Ratio Data, Interval Data, Ordinal Data, Nominal Data * Ratio Data – true zero exists, only positive values e.g. Height, weight, age (numerical) * Interval – no true zero, negative and positive values e.g. temp and dates (numerical data) * Ordinal – ordered categories e.g. rankings, student letter grades (categorical) * Nominal – categories, no ordering or direction e.g. martial status, type of car, gender (categorical) * Summary

* Population vs. sample

* Parameter vs. statistics

* Data collection sources

* Categorical vs. numerical data

* Discrete vs. continuous data

* Nominal vs. ordinal data

* Interval and ratio scales

Chapter 2

* Table and Charts for Categorical Data – Graphing (Bar charts and pie charts), Summary table * Bar and Pie Charts – used for qualitative data

* Tables and Charts for Numerical Data – Ordered Array (Stem-and-leaf plot), Frequency Distributions Cumulative Distributions (Histogram, Polygon, Ogive) * Ordered Array – sequence of data in rank order

* Frequency Distribution – summary table in which data are arranged into numerically ordered classes or intervals. The number of observations in each ordered class or interval becomes the corresponding frequency of that class or interval. * Scatter Diagrams – scatter diagrams are used to examine possible relationships between two numerical variables * Summary

* Summary tables, bar charts, pie charts

* Ordered array and stem-and-leaf display

* Frequency distributions, histograms and polygons

* Cumulative distributions and ogives

* Contingency tables and side-by-side bar charts

* Scatter diagrams and time-series plots

Chapter 3

* Central Tendency – Average and typical number

* Describing data by its central tendency, variation and shape – Central Tendency (Arithmetic mean, median, mode, geometric mean), Quartiles, Variation (range, interquartile range, variance, standard deviation, coefficient of variation), Shape (Skewness). * Measures of central tendency – arithmetic mean, median, mode * Median – 2/n+1 (ranked value)

* Mode – frequent occurring number

* Median – not affected by extreme value

* Quartiles – Q1, Q2 (median), Q3, Q4

* Q1 = (n+1)/4

* Q2 = (n+1)/2

* Q3 = 3(n+1)/4

* Variation - give information on the spread of the data values e.g. range, interquartile range, variance, standard deviation, co-efficient of variation. * Range – (largest – smallest)

* Interquartile range – IQR = Q3 – Q1 (box n whisker plot) * Variance – measures average scatter around the mean.

* Standard Deviation – shows variation about the mean.

* Coefficient of variation – shows variation relative to mean, always expressed as a %. * CV = Standard deviation/ mean x 100%

* Z score – difference between a given observation and the mean, divided by the standard deviation, e.g. a Z score of 2.0 means that a value is 2.0 standard deviations from the mean and a Z score...