First year

Question 1

Normal distribution

Answer 1

95% of values in a population will lie between 1.96 standard deviations of the mean (mean + 1.96sd)

If a variable X follows a normal distribution we say that X is N(μ, σ2) or X~ N(μ, σ2)

The mean (μ)
The standard deviation (σ)

Tabla

Question 2

Data

Answer 2

data types: numerical (discrete, a count or continuous, a measurement) or categorical (nominal, data can be ordered or nominal, data cannot be ordered)

Central tendency: the mode (the most frequently occurring value in a data set), the median (middle value in an ordered dataset) and the mean (la media)

Measures of position: the quartiles

Measures of spread: The ranges (maximum value – minimum value), the interquartile range (the difference between the first and third quartiles) standard deviation (how closely the data values in a dataset cluster around the mean)

Shape of data set

Question 3

Outliers

Answer 3

An observation that is numerically distant from the rest of the data. Lower inner fence

LIF = Q1-(1.5 x IQR)
Upper inner fence UIF = Q3+(1.5 x IQR)

Question 4

Tables

Answer 4

Percentage = Proportion = Probability

Question 5

Correlation 1

Answer 5

Response (observation making, dependent variable) and factor (independent variable) in associations between two variables.

Depends on the nature of the variables,

• Numerical vs numerical: scatter plot
• Categorical vs categorical: Chi-Square test
• Categorical (factor) vs numerical (response):
  • Comparing histograms
  • Comparing boxplots
  • Compare descriptive statistics
  • Formal methods
  • Correlation Coefficients
• Ordinal variables: Spearman’s rank correlation coefficient (Rs)

Question 6

Correlation 2

Answer 6

Value of r Correlation (linear relationship)
-1 to –0.7 Strong negative correlation
-0.7 to –0.3 Moderate negative correlation
-0.3 to 0 Weak negative correlation
0 to 0.3 Weak positive correlation
0.3 to 0.7 Moderate positive correlation
0.7 to 1 Strong positive correlation
very close to 0 No correlation

Correlation is NOT causation.

Correlation may support the argument for causation but it does not prove it.

Question 7

Simple linear regression

Answer 7

y=mx+c

2 variables