Chapter 2 Review (Pages 95 – 97)
2.1 Hypotheses and Sources of Data (pages 42-47).
Exercises:
1 – Forming hypotheses and stating opposite hypotheses.
2 – Choosing data sources – primary and secondary data.
Definitions:
hypothesis – a theory or statement that is either true or false. It can be tested to determine its validity.
statistics – numerical data, or the collection, organization and analysis of numerical data
primary data – original data that a researcher gathers specifically for a particular experiment or survey (new information)
secondary data – data that someone else has already gathered for some other purpose (existing data)
2.2 Sampling Principles (pages 48 – 55).
Exercises:
3 – Sampling types & populations.
4 – Sampling types & populations.
5 – Sampling types & populations.
Sampling methods: simple random, systematic random, stratified random, and non-random.
Definitions:
sample – any group of people or items selected from a population (any part of the population)
population – the whole group of people or items being studied
census – a survey of all members of a population
random sample – a sample in which all members of a population have an equal chance of being chosen. As a result, a random sample is likely to be representative of the whole population.
non-random sampling – using a method in which all members of a population do not have and equal chance of being chosen to choose a sample from a population
bias – error resulting from choosing a sample that does not represent the whole population. Bias can make the result of a survey inaccurate.
simple random sampling – choosing a specific number of members randomly from the entire population
systematic random sampling – choosing a specific number of members randomly from the entire population
stratefied random sampling – dividing a population into distinct groups and then choosing the same fraction of members from each group
2.3 Use Scatter Plots to Analyse Data (pages 56 – 67).
Exercises:
6 – Graphing points on a scatter plot (what goes on the horizontal axis, and the vertical axis). Finding a trend to describe the relationship between the variables. Outliers.
7 – Graphing points on a scatter plot (what goes on the horizontal axis, and the vertical axis). Finding a trend to describe the relationship between the variables. Outliers.
Scatter plots can help you see relationships between variables.
Definitions:
inference – conclusion based on reasoning and data
dependent variable – a variable that is affected by some other variable (graphed on the vertical axis)
independent variable – a variable that affects the value of another variable (graphed on the horizontal axis).
outliers – measurement that differs significantly from the rest of the data. You can discard an outlier only if you know that it is not representative of the relationship between the variables.
2.4 Trends, Interpolation and Extrapolation (pages 68 – 76).
Exercises:
8 – Graphing. Describing trends. Estimating (interpolation). Predicting (extrapolation).
9 – Graphing. Describing trends. Predicting (extrapolation).
Patterns in a graph often indiate a trend.
Definitions:
interpolate – estimate a value between two measurements in a set of data (between points on the graph).
extrapolate – estimate a value beyond the range of a set of data (past the points on the graph).
2.5 Linear and Non-Linear Relations (pages 77 – 87).
Exercises:
10 – Graphing. Line of best fit.
11 – Graphing. Describing relationships. Outliers. Estimation (interpolation).
Definitions:
linear relation – a relation between two variables that forms a straight line when graphed.
line of best fit – a straight line that comes closest to the points on a scatter plot. A line of best fit can model a linear relationship, but is usually a poor model for a non-linear relationship.
curve of best fit – a curve that comes closest to the points on a scatter plot of a non-linear relation.