SAS for Statistics

Name: SAS for Statistics
Rating: 4.4 (27 reviews)

SAS for Statistical Concepts and Data Visualization

Created byAakash Taneja

Last updated 3/2022

English

English [Auto],

What you'll learn

Introduction to statistical analysis using SAS
Use of Statistics In Data Science
Use of Statistics in Decision Making
Descriptive Statistics concepts and use cases
Central Tendency - Mean, Median and mode
Mean, Median and mode - implementation using SAS
Measures of Variance - Range, Quartiles, Percentiles, Variance and Standard Deviation
Range, Quartiles, Percentiles, Variance and Standard Deviation - Using SAS code
Exploratory Data Analysis
Data Visualization using SAS
Bar charts, Bar and Line charts, Bubble chart and scatter plot implementation in SAS
Inferential Statistics
Statistics use cases in different industries
Probability - Random Variable and Probability Distribution
Sampling - Sampling Techniques and Stratified Sampling
Sampling implementation using SAS

Course content

4 sections • 16 lectures • 1h 3m total length

What is Statistics?4:17
Understand statistics as the science of collecting, organizing, analyzing, presenting, and interpreting data to inform decisions, with population data examples and multiple valid perspectives.
Types of Statistics4:31
Describe descriptive statistics as simple summaries of observations, shown via Walmart's 2019 quarterly revenue. Use inferential statistics to forecast 2020 revenue from 2019–2018 data with regression.
Use of statistics in data science6:25
Explore how statistics powers data science to solve real-world problems across industries, using forecasting, segmentation, and personalized recommendations to boost engagement, retention, and supply chain decisions.
Use of statistics in decision making7:41
Explore how statistics guide decision making across banking, insurance, education, health care, retail, and telecom. Learn data summarization, mean and standard deviation, charts, and hypothesis testing for predicting trends.

What is descriptive statistics1:38
Use descriptive statistics to describe data with numerical and graphical summaries, revealing distribution and shape while avoiding conclusions beyond the analyzed data.
Central Tendency - Mean in SAS4:27
Describe central tendency with mean, median, and mode in SAS. Use the means procedure to compute mean for numeric variables like score and final grade, plus min, max, and counts.
Central Tendency - Median in SAS2:09
understand central tendency via median, the middle value in an ordered dataset, including its behavior with even observations and using sas class to compute it with the mean by gender.
Central Tendency - Mode in SAS1:06
Explore central tendency by calculating the mode in SAS using a dataset with student ID, name, and subjects, and learn how to compute the mode with the student ID variable.
Measures of variance in SAS6:16
Explore measures of variability in SAS, including range, quartiles, percentiles, variance, and standard deviation, to quantify data dispersion from the center.

Bar Chart and Line charts Using SAS5:22
Learn to apply exploratory data analysis and data visualization in SAS by building bar and line charts to compare groups and trends in a student performance dataset.
Bubble Chart and Scatter Plot Using SAS5:26
Learn to build bubble charts and scatter plots in SAS, using height and weight with bubble size for age, grouping by gender, and plot cholesterol histograms to explore health data.

Inferential Statistics and its uses3:50
Explore inferential statistics and how to use samples to infer population characteristics. Learn population and sample concepts, estimate parameters, and perform hypothesis testing with Wal-Mart sales and vitamin c examples.
Probability4:37
Explore the basics of probability, including zero to one scales, coin toss examples, and conditional probability, then distinguish between discrete and continuous random variables and understand probability distributions.
Sampling1:56
Explore sampling concepts, including probability sampling and stratified sampling, and implement sampling using the tax code. See how randomization gives each population element an equal chance in simple random sampling.
Stratified Sampling0:52
Divide the population into homogeneous subgroups by similarity, then sample randomly from each group to improve representativeness. Use age-based subgroups like 18–29 and 30–39 to illustrate analysis.
Sampling - Implementation in SAS3:07
Implement sampling in SAS using simple random sampling, selecting 100 of 200 customers with a 0.5 probability, and explore 5% rate, seed, and stratification by gender.

Requirements

Basic SAS Knowledge

Description

This course is intended to give you an overview and detailed walkthrough of the various statistical concepts, data visualization techniques along with its implementation in SAS that are necessary for anyone interested in career of Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist.

After this course, you will clearly know the statistical concepts discussed and be able to relate and think of real time use cases of those concepts across different industries.

Also, with implementation of these statistical concepts in SAS programming language, you will get an edge over the industry required skills to implement and visualize the data.

This course will help you to understand and learn:

Statistical analysis using SAS
Use of Statistics In Data Science
Use of Statistics in Decision Making
Descriptive Statistics - concepts and use cases
Central Tendency - Mean, Median and mode
Mean, Median and mode - implementation using SAS
Measures of Variance - Range, Quartiles, Percentiles, Variance and Standard Deviation
Range, Quartiles, Percentiles, Variance and Standard Deviation - Using SAS code
Exploratory Data Analysis
Data Visualization using SAS
Bar charts, Bar and Line charts, Bubble chart and scatter plot implementation in SAS
Inferential Statistics
Statistics use cases in different industries
Probability - Random Variable and Probability Distribution
Sampling - Sampling Techniques and Stratified Sampling
Sampling implementation using SAS

Hope you will get the intended learning out of this course!

Who this course is for:

Has a basic knowledge of SAS and wants to implement statistical concepts using SAS

SAS for Statistics

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 23min

Descriptive statistics5 lectures • 16min

Data Visualization using SAS2 lectures • 11min

Inferential Statistics5 lectures • 14min

Requirements

Description

Who this course is for: