R for Data Science - Statistics Full Course - Statistical Data Analysis #datascience #rprogramming

#datascience #rprogramming
Download the dataset used in the video: http://softlect.com/datasets/murders.csv
Download the dataset used in the video: http://softlect.com/datasets/GEStock.csv
Download the dataset used in the video: http://softlect.com/datasets/IBMStock.csv
Download the dataset used in the video: http://softlect.com/datasets/CocaColaStock.csv
Download the dataset used in the video: http://softlect.com/datasets/ZominosCheese.csv
Download the dataset used in the video: http://softlect.com/datasets/ZominosSales.csv

In this video, we will talk about Statistics for Data Science using R programming language, more specifically about the types of Statistical methods, different statistical measures and categorization of Descriptive Statistics.

What is Statistics?
Statistics is a form of mathematical analysis that uses quantified models, representations and synopses for a given set of experimental or real-life data. Statistics studies methodologies to gather, review, analyze and draw conclusions from data.

There are many different types of statistics pertaining to which situation you need to analyze. Statistics are used to make better business decisions

Statistical Measures
Some statistical measures include the following:
• Mean
• Regression analysis
• Skewness
• Variance
• Analysis of variance

Mean
A mean is the mathematical average of a group of two or more numerals. The mean for a specified set of numbers can be computed in multiple ways, including the arithmetic mean, which shows how well a specific commodity performs over time, and the geometric mean, which shows the performance results of an investor’s portfolio invested in that same commodity over the same period.

Regression Analysis
The regression analysis determines the extent to which specific factors such as interest rates, the price of a product, influence the price fluctuations of an asset. This is depicted in the form of a straight line called linear regression.

Skewness
Skewness describes the degree a set of data varies from the standard distribution in a set of statistical data. Most data sets, including commodity returns and stock prices, have either positive skew or negative skew.

Variance
Variance is a measurement of the span of numbers in a data set. The variance measures the distance each number in the set is from the mean. Variance can help determine the risk an investor might accept when buying an investment.

Understanding Statistics
Statistics is a term used to summarize a process that an analyst uses to characterize a data set. Statistical analysis involves the process of gathering and evaluating data and then summarizing the data into a mathematical form.

Statistics is used in various disciplines such as business, social sciences, etc. Two types of statistical methods are used in analyzing data: descriptive statistics and inferential statistics.

Descriptive statistics are used to summarize data from a sample exercising the mean or standard deviation. Inferential statistics are used when data is viewed as a subclass of a specific population.

What is Descriptive Statistics?
Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire or a sample of a population.

Descriptive statistics are broken down into measures of central tendency and measures of variability (spread).

Measures of central tendency focus on the average or middle values of data sets; whereas, measures of variability focus on the dispersion of data. These two measures use graphs, tables, and general discussions to help people understand the meaning of the analyzed data.

Measures of central tendency include the mean, median, and mode. Measures of central tendency describe the centre position of a distribution for a data set. A person analyzes the frequency of each data point in the distribution and describes it using the mean, median, or mode, which measures the most common patterns of the analyzed data set.

Measures of variability include the standard deviation, variance, the minimum and maximum variables, and skewness. Measures of variability, or the measures of spread, aid in analyzing how spread-out the distribution is for a set of data. For example, while the measures of central tendency may give a person the average of a data set, it does not describe how the data is distributed within the set.

Inferential Statistics:
P-Value in Statistical Hypothesis
Degrees of Freedom
Confidence Interval
Hypothesis Testing
Chi-square Test

👉 Subscribe: https://www.youtube.com/c/Soft....lect?sub_confirmatio