Z- Statistics, T-Statistics, P-Statistics are Still Confusing you?
By: Amit Chauhan
Tags: Machine Learning, Artificial Intelligence, Data Visualization, Data Science, Programming

Z- Statistics, T-Statistics, P-Statistics are Still Confusing you?

Definitions and concepts in Statistics for machine learning

Photo by Ruthson Zimmerman on Unsplash

Understanding statistics look like a side parallel road for data science and machine learning people. But learning statistics is worth making the inferences and solutions from the data. The road not to be taken by many people should have to accept the statistics with their daily dose. Well, in this article we will discuss the Z, T and P statistics distribution and will try to learn why we use them in data science. Before diving into this concept we will discuss some basic definition and terms as shown below:

Topics to be covered:

Section 1: Types of Data, Histogram and Scatter plot

Section 2: Central Measures values and Measures of Spread

Section 3: Covariance and Correlation

Section 4: Z, T — Distributions and confidence intervals

Section 5: Hypothesis and P — Distribution

Section 1:

Types of Data

Acquiring knowledge about statistics is a need in a data science career. Before jumping we should know with whom we are dealing with oh! that is obvious “DATA”. It is not like that data will come to you and tell all the inferences, we need to find a way to deal with different types of data.

Data is comprised of numbers and words that can be in the measurable or observational form. We cannot do the same operation with all different types of data, for this we need to identify and according to that, we need to test and visualize.

Numerical Data: This type of data deals with numbers which can be in a quantitative form either discrete or in continuous.

Discrete data is like a whole number ( 2, 10, 20, 15, etc) which tells a direct specification of the quantity that we count easily.

Continuous data is like a range means the value which falls in some particular measuring range ( Kg, Km, cm, etc ).

Categorical Data: This type deals with qualitative data that we can describe. It comes in a group of two or more types of a different description.

Example: Binary values ( 0 and 1 ),

Nominal Data: They are not in order but shows some groups or category like seasons, brand names, flower names, etc.

Ordinal Data: Well these types of data are those where we give them some rating or in some order.

Histogram and Scatter plot

If data is very big then we cannot sit all day and check every line of data records to take out some information. That’s where graphs and plots of data come into the picture.

Different types of plots are Bar, Line and Pie Charts, Histogram and Scatter plots, etc.

Histogram

Histograms although similar to bar charts but in the histogram, the bar falls in some range i.e. continuous type. The bar charts can have a gap between two bars but histogram doesn’t.

Difference between Bar and Histogram Chart. A photo from mathisfun

Scatter Plot

The scatter plot shows the relationship between two variable points in the record.

Scatter plot between Time and Marks. A photo by Author

Section 2:

Central Measures value

When we want to know about something from a big record we choose one thing which is similar to all others. So, in numbers, we choose a common and around value through Mean, Median and Mode.

Measures of Spread

The Range

When we arrange the data in ascending order and make a difference from the big number to a small number is called range.

Mean Deviation

This spread tells us how far the values from the measured central value.

First, take a mean of the values.

Then make a difference from the mean value to all values.

Then calculates the spread distance from the mean.

Example: