SAS Programming - Learn SAS from Beginner to Advanced

Learn how to import data from plaintext and Excel files into SAS, specify a dataset name, provide the file path, and assign columns like year and rent price for analysis.

import excel data into SAS by specifying path, format, and destination; select the worksheet, enable getnames to read headers from the first row, and run to produce a SAS dataset.

Learn to import CSV into SAS by naming the data set, defining variables (strings for names, numeric for fiscal year), controlling input formats, and converting Excel to CSV.

Learn how to enter data directly in a SAS program by hard coding a simple data set, including numeric and character variables, and use proc print to display results.

Learn to export data in SAS OnDemand, including creating a dataset from an uploaded file, using proc export to CSV, and downloading the result to your local computer.

Export SAS data to Excel and text formats by specifying the export path and extension, and verify the saved files.

Discover two SAS print data methods: print a specified table and print the last modified data when no data is specified, and observe outputs.

Create a new data set and variables to store calculations, including an average price of twenty-five thousand, then add a performance variable as the difference between rent and the criterion.

Learn to delete variables and keep only important ones by creating a new dataset with selected columns (year, rent price) and using drop to remove multiple variables.

Apply an if-else condition in SAS to filter records, creating a new data set that keeps only observations after year 2000 or exactly year 2000 using a rent example.

Sort SAS data with proc sort to create a data set, then print with proc print to view ascending or descending by price; omit output to keep the original.

Combine SAS data sets from quarterly files into one annual table, then explore combining tables with different variables where fields become empty, showing append behavior and column alignment.

Create and manage a SAS library to organize multiple datasets, use the libname statement to assign a folder, and load or reference datasets under that library for efficient analysis.

Explore two SAS commenting styles: block comments at the top of files to describe program purpose, and inline comments inserted within code to note details alongside statements.

Learn how to use SAS proc contents to inspect a data set's structure, including variable names, types, and metadata, especially when handling Excel data with many variables.

Learn to use the mean procedure in SAS to obtain descriptive statistics, including mean, median, standard deviation, min, and max, for all or selected variables, with options to tailor outputs.

Explore computing Pearson correlation coefficients with the SAS car procedure, generating a correlation matrix from rental data and interpreting diagonal ones and key variable pairs like age and rent.

Learn the basics of linear regression, including simple and multiple regression, and how to interpret regression coefficients for explanatory and dummy variables.

Learn how to perform a simple regression to assess whether house age influences rental rates, with rental rates as the dependent variable and age as the explanatory variable.

Learn to interpret SAS regression output by reading the parameter estimates table, extracting coefficients (intercept 15, age -0.6), p-values, and explained variance to assess model fit.

Learn how to use regression options in SAS to generate residuals and predicted values, store them in new variables, and print enhanced results for model evaluation.

Extend from simple to multiple regression by adding explanatory variables and interpreting r-squared and adjusted r-squared to assess model validity. Observe improvements in fit as you include more predictors.

Interpret results from multiple regression by using adjusted r-squared to avoid inflating the fit, and evaluate each variable's p-value to determine significance.

Learn to predict future values with SAS regression by building a data table, supplying predictor values, and running a regression to generate three predictions: 15.1, 15.5, and 16.9.

Explore regression analysis with SAS: build and refine a rental model, test variable significance, and compare predicted vs actual values.

Explore data visualization fundamentals, using scatterplots to relate temperature to beach visitors and other plot types like bar plots and pie plots to show single variables and compositions.

Learn how to create a scatterplot in SAS by plotting vacancy rate against square footage, setting the data-point symbol, and disabling interpolation.

Learn how to create a series plot in SAS by turning a scatterplot into a connected line graph of consecutive points, highlighting the need to sort by the x variable.

Learn to create bar plots and pie plots with the gcharts procedure, displaying frequency on the y axis and using the discrete option, including horizontal bars and frequency measures.

Overlay multiple relationships on one plot to compare vacancy rate versus age and operating expense versus age, then add a top center legend for clear identification.

Learn SAS SQL fundamentals for manipulating databases, including running SAS code with a quit command, using a dataset template, and progressing from simple selections to data merging operations.

Learn to select one column, multiple columns, or all columns from a SAS dataset using select syntax and proc print, and view the first 10 observations from the purchase data.

Learn how to use the count function to tally observations in SAS data sets, rename the result column, and apply count with other functions on customers and purchase data.

Learn to use the SQL distinct function to select unique values, count distinct areas and customers, and remove duplicates in SAS data sets.

Apply the group by concept to classify observations by a key variable, create subgroups, and perform counts or selections within each group, revealing area-based insights.

Learn how to store the area distribution results as a new table using a create table statement, and how to obtain distinct customer names from the data.

Merge datasets with a unique key using SQL merge concept, focusing on left join to keep all records from a and add b's matches, using customer name as the key.

Learn proc print to format columns with SAS formats, group observations by province, and produce subtotals, demonstrating how to create detailed SAS reports.

Learn to produce detailed SAS reports with the print procedure on sales data, importing data, limiting output to 10 observations, and removing the OBSS column with the nobis option.

Learn to build detailed SAS reports with the print procedure, select required columns using the VA statement, filter by conditions, apply dollar formats, and add a report title.

Learn how to enhance a SAS detailed report by adding a total row with the print procedure, summing profit and sales using the sum command.

Group profits by province in SAS reports using a BY statement in PROC PRINT after sorting by province, remove the output limit, and optionally combine all provinces into one report.

Explore revenue prediction by analyzing profits and costs using simulated sales data; identify top customers, best sellers, regional differences, risk exposure, shipping costs, and high-return products to inform strategic decisions.

Explore a sas case study that imports data, joins the customer, product, and sale tables into a unified dataset, then analyzes customers and regional revenue using bar and pie charts.

Compute the Halfon Index from profits by customer, their share, and squared contributions, benchmarked against equal contributions, and identify the worst product by returns via order ID merge.

Analyze a baseball player dataset with more than 20 variables in SAS, extracting insights from records featuring name, league, position, and performance metrics such as at-bats, walks, and hits.

Import baseball data from Excel, compute descriptive statistics, and compare home run and salary players; extend regression with hits, walks, put outs, and errors to derive a performance score.

Analyze a car sales dataset to reveal how horsepower, engine size, cylinders, and miles per gallon drive retail price. Compare Porsche versus Volkswagen to illustrate how brand premiums shape pricing.

This case study performs regression analyses to identify horsepower, mpg, and price determinants, checks multicollinearity with variance inflation factors, drops predictors, and tests brand effects with a generalized linear model.

Analyze stock market reaction to the 2016 presidential transition by testing SP 500 returns before and after Trump's inauguration, using adjusted closing prices and intraday volatility.

Learn to use SAS to compute daily returns, create event and effective dummies, calculate abnormal return, and regress to assess the presidential announcement effect on the stock market.

Analyze real estate sale prices in an expanded SAS table, showing higher prices with more space (bedrooms, bathrooms, square footage) and newer years, and explore weekday effects.

Explore SAS programs to import data, run correlations, and regress price on square footage. Create a high/low latitude dummy and test weekday effects on price.