Unit 1 Review

1.1: representing data Types of data: 1. Quantitative – can be represented by a number Discrete Data Data where a fraction/decimal is not possible e.g., age, number of siblings Continuous Data Data where fractions/decimals are possible E.g., height, weight, academic average 2. Qualitative – cannot be measured numerically e.g., eye colour, surname, favourite band

Population - the entire group from which we can collect data / draw conclusions Census – data collected from every member of the pop’n Data is representative of the population Can be time-consuming and/or expensive Sample - data collected from a subset of the pop’n A well-chosen sample will be representative of the pop’n

Organizing data Frequency table Stem and leaf plot Bar graphs Typically used for qualitative/discrete data double bar graph Stacked bar graph Histograms Typically used for continuous data Pie graph Scatter plot Typically used to show relationship between two numeric variables Box and whisker plot Pictograph Timeline Heat Map

1.2: drawing conclusions from data Types of statistical relationships: 1. Correlation Two variables appear to be related A change in one variable is associated with a change in the other 2. Causation Change in one variable is PROVEN to cause a change in the other requires an in-depth study

Drawing conclusions A great amount of data is required before conclusions can be drawn safely If A and B are correlated, it does not necessarily mean A caused B, or B caused A They could both be caused by another variable C Or they could be correlated due to chance

1.3: trends in data Variable: a symbol that represents an unknown quantity. They can be: Discrete (a single value) Continuous (a range of values) Constant: a value known and unchanging

Types of variables Independent Variable placed on the horizontal axis Dependent Variable values depend on the independent variable placed on the vertical axis Usually the variable you want to study

Trends Trends are: a pattern of average behavior that occurs over time a general “direction” that something tends toward need two variables to exhibit a trend Trend could be: Strong or weak Positive or negative Linear or non-linear Line of best fit: A line that best represents the trend in data and is used for making predictions

Regression the correlation coefficient, r, indicates the strength and direction of a linear relationship r = 0no relationship r = 1perfect positive correlation r = -1perfect negative correlation r 2 is the coefficient of determination if r 2 = 0.42, that means that 42% of the variation in y is due to x

1.5: biased ways of representing data Data can be represented in misleading ways to change opinions or make profits