Variables

1. Numerical
   1. Continuous (Value)
   2. Discrete (Counts)
2. Categorical
   1. Regular (Gender)
   2. Ordinal (Rating)
3. Relationship
   1. Associated
   2. Independent

Charts

• Histograms denote the frequency distribution of a single binned numerical variable
• Relationship between 2 numerical variables will be a scatterplot
• Relationship between 2 categorical variables will be a bar chart
• Relationship between a categorical and numerical variable can be side-by-side box plots, that explore spread of the numerical variable per category but show them side-by-side for categorical comparison

Measures of center - Mean, Median Measures of spread - Standard Deviation, Interquartile Range

Ideal Distributions we want to work with

• Normal & T for numerical variables
• Chi-Square for categorical variables
• Binomial

https://www.youtube.com/watch?v=rzFX5NWojp0&

Working with a normal curve

• Z scores - normalized score defined in units of standard deviation. Z score of an observation in the normal curve is the difference of that observation and the mean divided by standard deviation. Probability of getting an observation above/below the point at which z score was calculated can then be calculated. https://www.youtube.com/watch?v=2tuBREK_mgE&

When we don’t get these distributions naturally, then we can get them by taking multiple samples and then preparing a distribution from the mean and deviation of those samples, this uses central limit theorem. It is recommended to have atleast 30 independent observations in each sample.