# Statistics for Data Science (Descriptive & Inferential Statistics)

Today we are going to discuss **statistics for data science**.

Statistics plays a vital role in the life of a Data Scientist. It is one of the must-have skills for the Data Scientist. For example, descriptive statistics is required in data analysis.

Here we are going to present the basic concepts of the main branches of Statistics. These concepts will help to advance in the field of data science.

There are **two main branches of Statistics:** 1. Descriptive Statistics and 2. Inferential Statistics.

Let’s discuss one by one.

## A. DESCRIPTIVE STATISTICS

Before going to discuss about descriptive statistics, first we recall the basic concept of data and its types here.

### Data

Data is a collection of factual information based on numbers, words, observations, measurements which can be utilized for calculation, discussion and reasoning.

### TYPES OF DATA

The crude dataset is the basic foundation of data science and it may be of different kinds like Structured Data (Tabular structure), Unstructured Data (pictures, recordings, messages, PDF documents and so forth.) and Semi-Structured.

Furthermore, there are two kinds of data i.e. population data and sample data.

#### Population Data:

Population data is the collection of all items of interest which is denoted by ‘N’ and the numbers we obtain when using population are called parameters.

#### Sample Data:

Sample data is a subset of the population which is denoted by ‘n’ and the numbers we obtain when using sample are called statistics.

### Graphical Representation of variables in form of Graph & Tables

#### Bar Chart

Bar charts are used to display data. In bar chart, each bar represents a category and y-axis shows the frequency as shown in figure

#### Pie chart

Pie charts are frequently used to display market share. If we want to see the share of any item as a part of the total then we utilize pie chart. Pie chart is shown in figure below.

### Frequency Distribution Table

Frequency distribution table shows the category and its corresponding absolute frequency as shown in here.

Category | Frequency |

Black | 12 |

Brown | 5 |

Blond | 3 |

Red | 7 |

** Relative frequency** = frequency / total frequency

### Measures of central tendency

It is a single value that explains a set of data by identifying the central position within that set of data. Measure of central tendency is also called measure of central location. There are three measures of central tendency.

- Mean
- Median
- Mode

#### 1. Mean

It is most popular to measures the central tendency. It is used with both discrete and continuous data.

The mean is equal to the sum of all the values in the data set divided by the number of values in the data set. Therefore, if we have n values in a data set and they have values x_{1}, x_{2}, …, x_{n}, the sample mean, given as under.

If we intend to calculate the population means instead of sample mean then we use the Greek letter µ as

#### 2. Median

It is the mid score of a dataset that has been arranged in order of magnitude. In order to calculate the median, suppose we have the following datasets.

10 20 30 15 20 30 15 20 30 15 20

First of all, we re-arrange this data into order of magnitude from smaller to larger.

10 15 15 15 20 **20** 20 20 30 30 30

Therefore, in this case bold figure 20 is our median. It is the middle mark, as there are 5 scores before it and 5 scores after it. However, if we have an odd number of scores like this one,

10 20 30 15 20 15 20 30 15 20

Then, we re-arrange this data into order of magnitude and we obtain

10 15 15 15 **20** **20** 20 20 30 30

In this case, we have to take two values i.e. 20, 20 and average them to get a median i.e. 20.

#### 3. Mode

It is a value that most often score in our dataset. A dataset can have no mode, one mode or multiple modes. It can be calculated by finding the value with the maximum frequency. For instance,

### Measure of Asymmetry

#### Skewness

It is the measure of asymmetry that shows whether the observations in a dataset are focused on one side. Skewness can be calculated by the following formula.

There are two types of skewness,

- Right or Positive Skewness
- Left or Negative Skewness

**1. Right or Positive Skewness**

Right or positive skewness means that the outliers are to the right (long tail to the right) as shown in figure.

**2. Left or Negative Skewness **

Left or negative skewness means that the outliers are to the left (long tail to the left) as shown in figure.

However, if **mean = median = mode** then there is no skew and therefore, distribution will be symmetrical.

### Variance and Standard Deviation

Variance and Standard Deviation measure the dispersion of a set of data points around its means value.

**Sample Variance formula **

**Population Variance formula **

**Sample Standard Deviation formula **

**Population Standard Deviation formula **

#### Coefficient of Variation

There is no unit of measurement for Coefficient of variation. Coefficient of variation is perfect for comparison and universal across datasets. Formula of coefficient of variation is given below.

#### Co-variance and correlation

Covariance | Correlation |

Covariance is a statistical measure which is defined as a systematic relationship between a pair of random variables wherein change in one variable responded by an equivalent change in another variable | Correlation is a statistical measure which is defined as a systematic relationship between a pair of random variables wherein movement in one variable responded by an equivalent movement in another variable |

The value of covariance lies between -∞ and +∞ | The value of correlation lies between -1 and +1 |

A covariance of 0 means that the two variables are independent. | A correlation of 0 means that the two variables are independent |

A positive covariance means that two variables move together | A correlation of 1 means a perfect positive correlation |

A negative covariance means that the two variables move in opposite directions. | A correlation of -1 means perfect negative correlation. |

## B. INFERENTIAL STATISTICS

### Probability distribution

It is a statistical function that explains all the possible values and likelihoods that a random variable can take within a given range.

This range will be bounded between the least and the highest possible values, but precisely where the possible value is likely to be plotted on the probability distribution depends on a number of factors like distribution’s mean, standard deviation, skewness, and kurtosis.

Few examples of distributions are,

- Normal distribution
- Binominal distribution
- Student’s T distribution
- Uniform distribution
- Poission distribution

Mostly, there is a confusion that distribution is a graph but in fact, this rule helps us in determining how the values are positioned in relation to each other.

#### Normal Distribution

It is also known as Gaussian distribution or Bell Curve. It is mostly used in regression analysis. A lot of things closely follow this distribution:

- heights of people
- size of things produced by machines
- errors in measurements
- blood pressure
- marks on a test
- stock market information

When data is normal distributed then distribution is symmetric and

Mean = median = mode

**N ~ (µ, σ**^{2}**) **

Where, N for normal, ~ for distribution, µ is mean, and σ^{2} is the variance

#### Standard Normal Distribution

It is a normal distribution with a mean of 0 and a standard deviation of 1. Every normal distribution can be standardized using the following formula.

Standardization permit to compare different normally distributed datasets, test hypothesis, detect outliers and normality, create confidence intervals and perform regression analysis.

#### Central Limit Theorem

This theorem states that the distribution of sample means approximates a normal distribution as the sample size gets larger (assuming that all samples are the same in size), regardless of population distribution shape.

If the sample sizes= or >30 are considered enough for the Central Limit Theorem to hold.

The main aspect of this theorem is that the average of the sample means and standard deviations will equal the population mean and standard deviation.

Furthermore, adequately large sample size can forecast the characteristics of a population accurately. In Central Limit Theorem,

- No matter the distribution
- The more samples, closer to Normal (k ->∞)
- The bigger the samples, the closer to Normal (n -> ∞)

**Estimators and Estimates:**

**Estimators**

It is a mathematical function of the sample that tell us that how to calculate an estimate of a parameter from a sample.

Smaller the variance, most efficient the estimator. Hence, we required to find what are the “good” estimators.

Few vital criteria for goodness of an estimator are based on these properties.

- Bias
- Variance
- Mean Square Error

Examples of estimators and equivalent parameters are given in below table.

**Estimates**

An estimate is the output value that you can get from an estimator. There are following types of estimates:

- Point Estimates – a single value, e.g. 1, 6, 12.34, 0.123
- Confidence Interval Estimates – an interval, e.g. (1,4), (43, 45), (3.22, 5.33), (-0.24, 0.26). We mostly use confidence interval estimates when making inferences because it is more precise as compared to point estimates.

**Confidence Interval**

It is an interval within which we are assured with a certain %age of confidence, the population parameter will fall.

**Margin of Error**

A margin of error explains how many percentages points your results will differ from the real population value. It can be calculated by the following two ways.

- Margin of error = Critical value x Standard deviation
- Margin of error = Critical value x Standard error of the statistic

#### Student’s T Distribution

It is mostly used to estimate population parameters when the sample size is small and/or population variance is not known.

It is pertinent to mention here that it is very useful in such cases where we have not enough information or too much cost is involved to acquire the requisite information.

It has fatter tails as compared to normal distribution and lower peak. Following formula can be used to get the student’s T distribution for a variable with a normally distributed population:

## HYPOTHESIS TESTING

### Scientific Method

The scientific method is a process for gathering data and processing information.

It was first sketched by Sir Francis Bacon (1561-1626) to provide logical, rational problem solving across many scientific fields.

The main principle of the scientific method is systematic observation, predictability, verifiability, and amendment of hypothesis. The basic steps of the scientific method are:

- Make an observation that explains the issue
- Make a hypothesis or potential solution to the problem
- Test the hypothesis
- If the hypothesis is true then find further evidence or against-evidence
- If the hypothesis is false then create a new one or try again
- Draw conclusions and purify the hypothesis

### What is hypothesis?

A hypothesis is an assumption based on inadequate evidence that requires further testing and experimentation. After further testing, a hypothesis can generally be confirmed true or false.

### Null Hypothesis (H_{0})

A null hypothesis is a hypothesis which is required to be tested. It is the hypothesis that the investigator is trying to show to be false. It is a status-quo.

The concept of null is similar to someone remain innocent until enough evidence to prove guilty. For instance, someone say, data engineer normal salary is Rs.1,25,000/- but in our opinion he may be wrong, so, we make statistical testing to reject this hypothesis, it is called null hypothesis.

### Alternative Hypothesis (H_{1} or H_{A})

An alternative hypothesis is inverses of the null hypothesis which is usually based on our own opinion.

For instance, someone say, data engineer normal salary is Rs.1,25,000/- but in our opinion, data engineer cannot earn this value (less salary), it is called alternative hypothesis.

### DECISIONS

After testing, there will be two possibilities of decisions i.e. **accept the null hypothesis** or **reject the null hypothesis. **

Accept the null hypothesis means there is insufficient data to support the alteration or novelty brought by the unconventional. Reject the null hypothesis means there is sufficient statistical evidence that shows this null hypothesis is false.

### Level of Significance

It is the probability of rejecting a null hypothesis via test when it is really true. It is denoted by α (Alpha).

### Confidence Level

It is a possibility of a parameter that lies within a specified range of values. It is denoted as C. Level of significance is connected with the confidence level and the relationship between them is denoted by c = 1 – α.

The common level of significance and the corresponding confidence level are given below.

Level of significance | Related to |

0.10 | 90% confidence level |

0.05 | 95% confidence level |

0.01 | 99% confidence level |

The rejection rule is as follows.

- If p-value ≤ level of significance, then reject the null hypothesis.
- If p-value > level of significance, then do not reject the null hypothesis.

### Rejection region:

The rejection region is the values of test statistic for which the null hypothesis is rejected.

### Non rejection region:

The set of all possible values for which the null hypothesis is not rejected is called the rejection region.

One sided (one-tailed) test is used when the null does not contain equality or inequality sign (<, >, ≤, ≥). The rejection region for one-sided (one-tailed) test is shown in figure:

- In the left-tailed test, the rejection region is shaded in left side (as shown in above figure).
- In the right-tailed test, the rejection region is shaded in right side.

Two sided (two-tailed) test is used when the null contains equality (=) or inequality (≠) sign.

The rejection region for two-sided (two-tailed) test is shown in figure:-

### Statistical Errors:

There are two types of statistical errors:

- Type I Error (False Positive)
- Type II Error (False Negative)

#### Type-I Error (False Positive):

Type-I error occurs when we **reject** a null hypothesis that is actually **true. **The probability of committing type-I error is denoted by **α (alpha).**

#### Type-I Error (False Negative):

Type-II error occurs when we **accept** a null hypothesis that is actually **false. **The probability of committing type-II error is denoted by **β (Beta). **

### P-value

The p-value is the smallest level of marginal significance at which the null hypothesis would be rejected.

A smaller p-value means that there is stronger evidence in support of the alternative hypothesis. Usually, *p*-value is found with 3 digits after the dot (x.xxx).

The number that lies between 0 – 1 is called *p*-value. It is

**A small****p****-value (typically ≤ 0.05)**makes a strong evidence against null hypothesis, so, we reject the null hypothesis.**A large****p****-value (> 0.05)**represents weak evidence against the null hypothesis, so, we fail to reject the null hypothesis. 0.05 is often the*“cut-off-line”*.

**Further Reading: **

**Download** Statistics for Data Science pdf