Welcome to this Statistics-1 Week 4 Summary post. It’s not an explanatory article, hope you keep that in mind. Once you are done with your lessons, this post should come in very handy though. Although the best notes are the ones you make yourself, if you are short of time, you can use this. Or you can use this as template to prepare your own summary- that would be the best thing. So go on, and if you find any errors, do let me know in the comment below.
Week 4
Statistics 1
Summary
- Association is not always Causality
- Association between categorical variables can be described in two ways.
- First is through a two-way contingency table.
- Another method is relative row frequency or relative column frequency.
- Binary response includes yes or no type of response. This is Bivariate categorical data.
- In contingency tables, the order of ordinal variables should be maintained.
- Google Sheet command for contingency table is : Pivot Table.
- If row relative frequencies are same, that means the variables are not associated.
- If row relative frequencies are different, that implies that the variables are associated.
- Stacked Bar chart or Segmented bar chart is used to represent count for a particular category.
- Regular Bar chart is generally used for a single categorical variable.
- However, if there are two categorical variables, then segmented bar chart is used.
- 100% Stacked bar chart or 100% Segmented bar chart implies proportional or Part-To-Whole relationships.
- In a scatter plot, the X axis contains the Explanatory variable. Example size of house and age.
- And the Y axis of the scatterplot contains the Response variable. Example, price of house and height.
- Studying scatterplot involves study of direction, curvature, variation, and outliers.
- Direction could be up or down, or both.
- Curvature could be linear or curved.
- Radiation could be tight or variable.
- Outliers may be present.
- Covariance and Correlation are two measures of linear association.
| Value of x | Value of y | Sign of deviation |
| Large | Large | Same |
| Small | Small | Same |
| Large | Small | Different |
| Small | Large | Different |