Sas guide to tracking pdf

Search Knowledge Base. Ask SAS Community. Contact Technical Support. Documentation Documentation Technical Papers. Training Courses. Learning path for Data Management. However, as I need this functionality now, I broke down and installed it. Immediately, EG picked it up and started using it. I'm not going to mark this "solved" because I'd still like to know how to fire up the Windows reader from EG, but I'm not going to sweat it.

With this, shall we understand that you tried to call the windows reader from its own path and it didn't worked out? If it is anything like the rest of their products since roughly Gates leaving then I am not suprised it doesn't work. I would suggest always to use either the Adobe reader, or by preference one of the free open source varieties - Foxit or SumatraPDF.

The problem is that the Windows reader is in a new "WindowsApps" at least new to me directory structure under "Program Files", and it seems to be swathed in security, such that I can't even figure out what the. With regards to the Windows reader, I didn't find it that bad Mercifully, I don't have to create PDFs in the ordinary course of events, just consume them. Thank heavens!

I can understand the frustration and it is not very worthy the time if the workaround is possible. If I would be on your shoes, I would leave to your system admins the task to to figure out the filesystem path to the executable of the windows reader, if it is available. When they get it, you can just add it on the viewer options. Ah, Windows 10 and Windows Store apps. Yes, there is your problem. As I mention above, almost all the development and decisions since and including Windows Vista have been bad ones in my opinion.

How Data Is Collected. Types of Events. Last updated: November 30, A scatterplot is a type of graph which uses values from two variables plotted in a Cartesian plane. It is usually used to find out the relationship between two variables. Please note that we create the data set named CARS1 in the first example and use the same data set for all the subsequent data sets. This data set remains in the work library till the end of the SAS session.

In a simple scatterplot we choose two variables form the dataset and group them with respect a third variable. We can also label the data. The result shows how the two variables are scattered in the Cartesian plane.

We use the additional options in the procedure to draw the ellipse as shown below. We can also have a scatterplot involving more than two variables by grouping them into pairs. In the example below we consider three variables and draw a scatter plot matrix. We get 3 pairs of resulting matrix. A Boxplot is graphical representation of groups of numerical data through their quartiles.

Box plots may also have lines extending vertically from the boxes whiskers indicating variability outside the upper and lower quartiles. The bottom and top of the box are always the first and third quartiles, and the band inside the box is always the second quartile the median. In a simple Boxplot we choose one variable from the data set and another to form a category.

The values of the first variable are categorized in as many number of groups as the number of distinct values in the second variable. In the below example we choose the variable horsepower as the first variable and type as the category variable.

So we get boxplots for the distribution of values of horsepower for each type of car. We can divide the Boxplots of a variable into many vertical panels columns. Each panel holds the boxplots for all the categorical variables. But the boxplots are further grouped using another third variable which divides the graph into multiple panels.

In the below example we have paneled the graph using the variable 'make'. As there are two distinct values of 'make' so we get two vertical panels. We can divide the Boxplots of a variable into many horizontal panels rows. As there are two distinct values of 'make' so we get two horizontal panels. The arithmetic mean is the value obtained by summing value of numeric variables and then dividing the sum with the number of variables. It is also called Average. Using this SAS procedure we can find the mean of all variables or some variables of a dataset.

We can also form groups and find mean of variables of values specific to that group. The mean of each of the numeric variable in a dataset is calculated by using the PROC by supplying only the dataset name without any variables.

We specify the maximum digits after decimal place to be 2 and also find the sum of those variables. We can find the mean of the numeric variables by organizing them to groups by using some other variables. In the example below we find the mean of the variable horsepower for each type under each make of the car. Standard deviation SD is a measure of how varied is the data in a data set. Mathematically it measures how distant or close are each value to the mean value of a data set.

A standard deviation value close to 0 indicates that the data points tend to be very close to the mean of the data set and a high standard deviation indicates that the data points are spread out over a wider range of values.

It brings out the SD values for each numeric variable present in the data set. This procedure is also used for measurement of SD along with some advance features like measuring SD for categorical variables as well as provide estimates in variance. The below example describes the use of class option which creates the statistics for each of the values in the class variable.

The below code gives example of BY option. In it the result is grouped for each value in the BY option. A frequency distribution is a table showing the frequency of the data points in a data set. Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of values in the sample.

In this case the result will show the frequency of each value of the variable. The result also shows the percentage distribution, cumulative frequency and cumulative percentage. We can see the result divided into two categories of results. One for each make of the car. We can find the frequency distributions for multiple variables which groups them into all possible combinations.

In the below example we calculate the frequency distribution for the make of a car for grouped by car type and also the frequency distribution of each type of car grouped by each make. With the weight option we can calculate the frequency distribution biased with the weight of the variable. Here the value of the variable is taken as the number of observations instead of the count of value.

In the below example we calculate the frequency distribution of the variables make and type with weight assigned to horsepower. Cross tabulation involves producing cross tables also called contingent tables using all possible combinations of two or more variables. Consider the case of finding how many car types are available under each car brand from the dataset cars1 which is created form SASHELP.

CARS as shown below. In this case we need the individual frequency values as well as the sum of the frequency values across the makes and across the types.

We can observer that the result shows values across the rows and the columns. When we have three variables we can group 2 of them and cross tabulate each of these two with the third varaible. So in the result we have two cross tables. In the below example we find the frequency of each type of car and each model of car with respect to the make of the car.

Also we use the nocol and norow option to avoid the sum and percentage values. With 4 variables, the number of paired combinations increases to 4. Each variable from group 1 is paired with each variable of group 2. In the below example we find the frequency of length of the car for each make and each model. Similarly the frequency of horsepower for each make and each model. The T-tests are performed to compute the confidence limits for one sample or two independent samples by comparing their means and mean differences.

Below we see one sample t test in which find the t test estimation for the variable horsepower with 95 percent confidence limits. The paired T Test is carried out to test if two dependent variables are statistically different from each other or not. As length and weight of a car will be dependent on each other we apply the paired T test as shown below. In our case we compare the mean of the variable horsepower between the two different makes of the cars "Audi" and "BMW".

Correlation analysis deals with relationships among variables. The correlation coefficient is a measure of linear association between two variables. Correlation coefficients between a pair of variables available in a dataset can be obtained by use their names in the VAR statement. In the below example we use the dataset CARS1 and get the result showing the correlation coefficients between horsepower and weight. Correlation coefficients between all the variables available in a dataset can be obtained by simply applying the procedure with the dataset name.

In the below example we use the dataset CARS1 and get the result showing the correlation coefficients between each pair of the variables. We can obtain a scatterplot matrix between the variables by choosing the option to plot matrix in the PROC statement.

Linear Regression is used to identify the relationship between a dependent variable and one or more independent variables. A model of the relationship is proposed, and estimates of the parameter values are used to develop an estimated regression equation. Various tests are then used to determine if the model is satisfactory.

If it is then, the estimated regression equation can be used to predict the value of the dependent variable given values for the independent variables. The below example shows the process to find the correlation between the two variables horsepower and weight of a car by using PROC REG. In the result we see the intercept values which can be used to form the regression equation. The above code also gives the graphical view of various estimates of the model as shown below.

Being an advanced SAS procedure it simply does not stop at giving the intercept values as the output. The Bland-Altman analysis is a process to verify the extent of agreement or disagreement between two methods designed to measure same parameters. A high correlation between the methods indicate that good enough sample has been chosen in data analysis. In SAS we create a Bland-Altman plot by calculating the mean, upper limit and lower limit of the variable values. In the below example we take the result of two experiments generated by two methods named new and old.

We calculate the differences in the values of the variables and also the mean of the variables of the same observation. We also calculate the standard deviation values to be used in the upper and lower limit of the calculation.

A chi-square test is used to examine the association between two categorical variables. It can be used to test both extent of dependence and extent of independence between Variables. This variable has six levels and we assign percentage to each level as per the design of the test.

We also get the bar chart showing the deviation of the variable type as shown in the following screenshot. In the below example we apply chi-square test on two variables named type and origin.

The result shows the tabular form of all combinations of these two variables. Fisher's exact test is a statistical test used to determine if there are nonrandom associations between two categorical variables. We use the Tables option to use the two variables subjected to Fisher Exact test.

To apply Fisher's Exact Test, we choose two categorical variables named Test1 and Test2 and their result. Repeated measure analysis is used when all members of a random sample are measured under a number of different conditions.

As the sample is exposed to each condition in turn, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures. One should be clear about the difference between a repeated measures design and a simple multivariate design. For both, sample members are measured on several occasions, or trials, but in the repeated measures design, each trial represents the measurement of the same characteristic under a different condition.

Consider the example below in which we have two groups of people subjected to test of effect of a drug. The reaction time of each person is recorded for each of the four drug types tested. Here 5 trials are done for each group of people to see the strength of correlation between the effect of the four drug types.

It performs analysis of data from a wide variety of experimental designs. In this process, a continuous response variable, known as a dependent variable, is measured under experimental conditions identified by classification variables, known as independent variables.

The variation in the response is assumed to be due to effects in the classification, with random error accounting for the remaining variation. Here we study the dependence between the variables car type and their horsepower. As the car type is a variable with categorical values, we take it as class variable and use both these variables in the MODEL. We can also extend the model by applying the MEANS statement in which we use Turkey's Studentized method to compare the mean values of various car types.

The category of car types are listed with the mean value of horsepower in each category along with some additional values like error mean square etc. Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. The usual process of hypothesis testing consists of four steps as shown below. Formulate the null hypothesis H0 commonly, that the observations are the result of pure chance and the alternative hypothesis H1 commonly, that the observations show a real effect combined with a component of chance variation.

Compute the P-value, which is the probability that a test statistic at least as significant as the one observed would be obtained assuming that the null hypothesis were true.

The smaller the P-value, the stronger the evidence against the null hypothesis. Compare the p-value to an acceptable significance value alpha sometimes called an alpha value. SAS programming language has features to carry out various types of hypothesis testing as shown below. Code And Create. Juan Galvan. Previous Page. Next Page.

Useful Video Courses. More Detail. Previous Page Print Page. Save Close. Base SAS It is a core component which contains data management facility and a programming language for data analysis. Log Window A log window is like an execution window where we can check the execution of the SAS program.

Output Window Output window is the result window where we can see the output of our program. Result Window It is like an index to all the outputs. Explore Window Here all the libraries listed.