Stats Flashcards
One sample, parametric data
One sample t test
Two samples, parametric data
Two sample t test
Two related samples, parametric data
Paired sample t test
Three + samples, parametric data
One-way ANOVA
Relational, parametric data
Pearson correlation coefficient or simple linear regression
Explanatory, parametric data
Multiple Regression
One sample, non-parametric data
Chi square
Two samples, non-parametric data
Chi-square (for nominal and ordinal). Mann-Whitney (for ordinal, interval and ratio data).
Two related samples, non-parametric data
Friedman test. Wilcoxon test (for ordinal, interval and ratio data.)
Three + samples, non-parametric data
Chi-Square (for nominal and ordinal data). Kruskal-Wallis (for ordinal, interval and ratio data).
Relational, non-parametric data
Spearman rank correlation coefficient. Logistic regression.
Explanatory, non-parametric data
Multiple logistic regression.
Normal distribution, known as
Gaussian distribution
Parametric data is data
that are measure on interval/ratio scales and data that are not normally distributed.
Non-parametric data is data
Nominal/ordinal data or interval/ratio data that are not normally distributed.
Test for normal distribution
Kolmogorov-Smirnov test.
Devon vs UK incomes
One sample t
Devon vs Dorset incomes
Two sample t test
Devon incomes 2000 vs Devon incomes 2002
Paired sample t test
Devon vs Dorset vs Somerset incomes
One-way ANOVA.
Income correlated with spending on holidays
Pearson correlation coefficient or simple linear regression
Income and age predicting holiday spending
Multiple regression
Devon sites vs UK sites visited
Chi-square
Devon vs Dorset sites visited
Chi-Square (for nominal and ordinal data). Kruskal-Wallis (for ordinal, interval and ratio data).
Devon sites visited 2000 and 2001
Friedman test. Wilcoxon test (for ordinal, interval and ratio data.)
Devon vs Dorset vs Somerset sites visited
Chi-Square (for nominal and ordinal data). Kruskal-Wallis (for ordinal, interval and ratio data).
Income correlated with number of sites visited
Spearman rank correlation coefficient. Logistic regression.
Income and age predicting sites visited.
Multiple logistic regression.
Nominal Data
Refers to categorically discrete data such as name of your school, type of car you drive or name of a book
Ordinal Data
Refers to quantities that have a natural ordering. The ranking of favorite sports, the order of people’s place in a line, the order of runners finishing a race or more often the choice on a rating scale from 1 to 5.
Interval data
Is like ordinal except we can say the intervals between each value are equally split. The most common example is temperature in degrees Fahrenheit.
Ratio data
is interval data with a natural zero point. For example, time is ratio since 0 time is meaningful. Degrees Kelvin has a 0 point (absolute 0) and the steps in both these scales have the same degree of magnitude.
Discontiuous/discrete data
Whole number values, such as the number of students attending a course. Nominal and ordinal data often reflects discrete data
Continuous variables
These are variables which have an infinite number of fractional points e.g. height
Categorical data
Data subdivided in categories. Nominal data is subdivided in unordered categories, while categories of ordinal data have an internal order.
R squared
the ‘goodness of fit’ that the model offers, expressed in per cent.
B
The regression Coefficient
t
The ‘significance’ of the coefficient in explaining the variance.
F
The significance of the overall model in explaining the variance.
Equation for a straight line
y= a + bx
y =
dependent variable
b =
slope gradient
a =
intercept
x =
independent variable
