Statistics & Probability Worksheets

What is the Difference Between Statistics and Probability? When it comes to determining the relative frequency of events in mathematics, there are two concepts that we need to understand, and these include statistics and probability. A lot of students confuse these two terms, even though these are two separate concepts dealing with the analysis of relative frequency. The most dominant difference between probability and statistics is that we probability deals with future event predictions and statistics helps in analyzing past events. Probability is more of a theoretical branch in mathematics that governs the consequences of mathematical definitions. Statistics, on the other hand, is an applied branch of mathematics that deals with the data in the real world. We can say that probability is a form an analysis of consequences in an ideal world while statistics is a measure of how close our real world is to the ideal world. This topic was reserved to older students for a very long time in many curriculum frameworks, but the common core standards introduces key skills in early middle school. It makes a great deal of sense because most of the new careers that have been created over the past two decades rely on understanding and manipulating data, just like this.

• Addition Rule of Probability - This is for when you two mutually exclusive events.
• Analyzing Plotted Functions - We look for trends and make predictions of the future of the data.
• Averages - Learn how to calculate this measure of central tendency in many different forms.
• Analyzing Probabilities and Decisions - Students will learn to make sense of data and make decisions based on how it looks.
• Calculating the Payoff of a Game of Chance - You need to review this before ever stepping in a casino. The games are not in your favor there.
• Comparing Two Data Sets - How do prepare to pit two data sets against one another.
• Conditional Probability - This is when certain things are set in motion and you need to figure out where it is going.
• Correlation vs. Causation - We learn to intensify how to the understand the reasoning behind the data.
• Creating Probability Models - What are all the possible outcomes and where do they have us headed.
• Determining Dependent and Independent Events - We look at every possible outcome can exist.
• Estimating The Mean of Sample Surveys - This will help you better understand the opinion of different people.
• Frequency and Data Distribution - We look at how data is spilled around and help give it order.
• Generate Math Frequencies Through Design - The foundation of where algorithms are designed.
• Graphing Probability Distributions - This is a long-range way to help you interpret data sets.
• Identifying Random and Bias Data Samples - We learn the difference between the types of samples that we are looking at.
• Interpreting Slope and Intercept - We learn to interpret important properties of line in order to find where they are situated on a graph.
• Interpreting Slope and Rate of Change in Context - Understanding tells you the impact a change the value of a variable will have on the overall outcome. Is it a drop in the bucket or a major bomb?
• Intersection and Union of Sets - Learn to detect where similarities exist between two sets.
• Likelihood of a Single Event - This happens you make educated guesses for an event.
• Linear Function Word Problems - This can be used to help you solve complex problems.
• Making and Understanding Box and Whisker Plots - This can help us understand large volumes of data.
• Making Inferences From Random Data - We can order the data in such a way that we can make well thought out decisions.
• Mean and Standard Deviation Distributions - The mean gives use a general understanding, but the standard deviation can tell us how all over the place the data is.
• Mean, Median, and Range from Data Displays - We learn how to interpret visual images of data sets.
• Measures of Center and Variability - These measures tell us what the norm looks like and how many outliers you may have.
• Measures of Mean, Median, Mode, and Range - Learn how to find the average, middle value, most frequent value, and differences in data sets.
• Multiplication Rule of Probability - This tell us how likely two events are going to happen at the same time.
• Outliers in Data Sets - We work on understanding how many values of data are just off the charts.
• Patterns of Association (Using Data Tables) - Is there some form of relationship found within a data set?
• Permutations and Combinations - When the order of events matters and when it does not at all.
• Predicting the Outcome of Events - This helps us make positive choices based on the data that is present.
• Probability Distribution - A nice way to sum up all the outcomes that affect a system.
• Probability Distribution Based on Empirical Probabilities - This is when we have solid data to work with. If you have confidence in your data, you will want to focus on this method of interpretation.
• Probabilities of Compound Events - When you have several possible outcomes for an event.
• Probability of a Chance Event - To help get you ready for that next game show you are on. Play the odds to your favor.
• Probability Word Problems - These situational based problems will help you better understand the outcomes of your situation.
• Relative Conditional Probability - This helps you examine more complex outcomes.
• Scatter Plots for Bivariate Data - When you have two forms of related data.
• Scatter Plots of Linear Functions - It usually ends up in forming a straight line or a line of best fit.
• Standard and Absolute Deviation - We look at how from the mean data strays.
• Statistical Variability in Data - To what extent is your data all over the place?
• Stem and Leaf Plots - When you are working with large amounts of data, this comes in handy for displaying it.
• Summarizing and Interpreting Data Sets - This is what we are fully working towards understanding in this topic.
• Summarizing Numerical Data Sets - This is the first step towards being able to interpret data.
• The Attributes of Data and Units of Measure - We take time to pick apart our data.
• The Expected Value of Random Variables - This can help you make good guesses when you are approaching random data values.
• The Measure of Center of Data Sets - We are trying to find where most of your lies.
• Two Way Tables - This is helpful to understand the frequency distributions present in data sets.
• Understanding Data Displays - We look at a wide range images of data sets.
• Understanding Random Sampling - What is the data truly random and valid?
• Using Probabilities to Make Fair Decisions - We know when we play games that the outcome varies but following this method will help you make the best play.
• Using Two-Way Tables - We move on to the application process with data sets.
• Using Straight Lines to Model Relationships - This can help us make valid and accurate predictions.
• Validating Data Generating Processes - How to determine if an outcome or a decision is truly correct.
• Working With Assessing Overlapping Data Sets - What likenesses can we spot between different data sets?