Introduction to Statistics (Self-paced Tutorial) Online Certificate Course

Learn to successfully interpret basic statistical procedures

Introduction to Statistics (Self-paced Tutorial) Online Certificate Course

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Study Introduction to Statistics Online Course and Improve Your Data Analysis 

Our Online Statistics course is perfect for anybody needing to gain a better understanding of statistics. Regardless of whether statistics is a new field for you or you just need to refresh your statistical knowledge, this statistics online course will further your knowledge of statistics and enhance your ability to make informed decisions using the data available to you.

This introductory online statistics course combines gives you the knowledge, and skills to analyze data with confidence. Throughout the course, you will be given opportunities to investigate the use and application of data to real-life problems and circumstances.

The statistics course online will give you a complete introduction to the world of statistics, starting with the all-important concepts of data and data collection practices. With those concepts bedded down, you will go on to learn how to summarize statistical information using graphs, charts, and numbers. You will discover how to determine probabilities and form an understanding of how probabilities are used in the decision-making process.

These statistics classes online will show you how to visualize and measure data relationships. You will also learn how to effectively utilize data for forecasting and prediction purposes. The statistics training online will also explore the fundamentals of statistical inference and introduce you to the concept of statistically significant results. 

This statistics class online training consistently makes use of real data and an array of practical examples sourced across the statistical spectrum. You will use data related to business and industry, sports, politics, health care and medicine, education, politics, the social sciences, and news events.

There is no need to be concerned about having to perform intricate calculations as the online statistics class will introduce you to a range of free online resources that will assist you in making calculations.

After taking statistics online course you will have no trouble in being able to effectively apply and interpret fundamental statistical procedures.

What you will learn with our Introduction to Statistics Online Course

  • Quantitative Data
  • Displaying Quantitative Data
  • Displaying Qualitative Data
  • Linear Regression
  • Probability Models
  • The Key to Inference
  • Testing Hypotheses About Proportions

Introduction to Statistics Online Course - Requirements

The Introduction to Statistics Course is delivered 100 percent online 24/7.

To successfully complete this course, a student must:

  • Have access to the internet and the necessary technical skills to navigate the online learning resources
  • Have access to any mobile device with internet connectivity (laptop, desktop, tablet)
  • Be a self-directed learner
  • Possess sound language and literacy skills

Quick Course Facts

  1. Course content is structured for easy comprehension
  2. Registered students gain unrestricted access to the Introduction to Statistics Course
  3. All course material is available online 24/7 and can be accessed using any device
  4. Study online from anywhere in your own time at your own pace
  5. All students who complete the course will be awarded with a certificate of completion

For any additional questions please see our comprehensive FAQS tab above.

Introduction to Statistics Online Course Outline

Chapter 1: Introduction

Topics to be discussed include:

  • Why Learn Statistics?

Chapter 2: The Basics of Statistics

It's a field of study that deals with data analysis. What is data, by the way? Information is data! It's information with context and meaning, not just a tangle of numbers, in particular. Plan, tools, and methods for collecting, displaying, analyzing, and interpreting data are all part of modern statistics.

Topics to be discussed include:

  • Various Variables
  • It Starts with a question

Chapter 3: Data Collection Using Sampling

Taking a census of the entire population, as you taught in Chapter 2, is frequently prohibitively costly and time-consuming. As a result, you'll need to take a random sample of the population. We'll look at numerous strategies to ensure that the sample appropriately reflects the larger population in this chapter. You'll see how high-quality data isn't created by accident... though it does play a part.

Topics to be discussed include:

  • Sampling Methods
  • Systematic random sampling
  • Stratified random sampling
  • Cluster sampling

Chapter 4: Designing Experiments to Collect Data

Experiments have provided us with a lot of information on health, nutrition, and human behavior. Without deliberate trials, breakthroughs in agriculture that help feed us all and advances in medicine that aid you when you're sick would not have been feasible. We'd have to rely on guessing, luck, folklore, or doing things the way we've always done them if we didn't have them.

Topics to be discussed include:

  • Designed Experiments: The Basics
  • Control
  • Randomization
  • Blinding
  • Replication
  • Observational Studies

Chapter 5: Summary

Every statistical study begins with a question, as we discussed in Chapter 2. It's critical to select samples that accurately reflect the population as a whole in order to obtain credible results. The key is to use random sampling. And every experiment that is designed to answer a question must be controlled, randomized, blinded, and reproducible.

Lesson 2: Quantitative Data: From Averages to z-Scores

Chapter 1: Introduction

One of my keys aims for this course is for you to be able to understand statistics and statistical results that you see in your daily life, interpret them correctly, and even build your own sound statistical studies. Statistics knowledge will aid you in interpreting data and making sound judgments.

Chapter 2: Journey to the Center of Your Data

Look over your data before you start any computations or graphs, especially if you didn't collect and compile it yourself. It's what one of my graduate school professors referred to as "eyeballing" your data. When you eyeball the data, you skim it to get a sense of the size of the values, look for missing or incorrect numbers, and try to notice any values that don't make sense.

Topics to be discussed include:

  • Calculating the Mean
  • Finding the Median
  • Comparing Means and Medians

Chapter 3: Variability: The Spice of Statistics Life

Consider how people differ in a variety of ways. To name a few characteristics, we differ in age, weight, height, gender, race, family history, income, and education. Throughout our lifetimes, we all encounter changes in our income and weight, as well as changes in typical health metrics like temperature, heart rate, and blood pressure.

Topics to be discussed include:

  • Determining the range
  • Standard Deviation

Chapter 4: All About z-Scores

Here's an example of a common method that incorporates some of the concepts covered in this lecture. It also introduces a concept that will be revisited later in the course.

Topics to be discussed include:

  • What are Z-scores
  • Another Option

Chapter 5: Summary

You learned some basic descriptive statistics in this course that you'll need practically every time you study quantitative data. The mean or median can be used to describe the center or average of quantitative data. The mean takes into account all of the data, but extreme results, or outliers, may have an impact. To get the middle number, the median uses the order of the data.

Lesson 3: Displaying Quantitative Data: Dots, Plots, and Histograms

Chapter 1: Introduction

Nobody wants to spend time staring at numbers in columns and rows, trying to see patterns. Data visualizations make it easier to find trends and detect unexpected values, as well as being more pleasing to the eye. If you have to show data at work, you'll almost certainly do it with a graph. The plots and graphs you'll see in this session are the statistician's basic graphical tools.

Chapter 2: Displaying Data: Dots, Stems, and Leaves

It's simpler to see the distribution, or pattern of variation, in statistical data when it's displayed on a graph. Dot plots are frequently used by statisticians as a first step in visualizing data distribution.

Topics to be discussed include:

  • Dot plots
  • Steam-and-Leaf plots
  • Steam-and-Leaf plots: White Refusal Rate

Chapter 3: Histograms

The charts you saw in Chapter 2 may get cluttered with larger data sets. The histogram, which is usually a preferable solution for huge data sets, is covered in this chapter. You split your data into equal-width "bins" to make a histogram. The number of data values that fall within each bin is then counted. 

Topics to be discussed include:

  • What should I look for in a Histogram
  • Where would you guess the mean and median for this variable would fall in the histogram
  • Where would you guess the mean and median are in this histogram?
  • What stands out to you?

Chapter 4: Advanced Visuals: Five-Number Summaries and Box Plots

When performing these kinds of comparisons, a box plot is a useful tool. This is how one appears. This one compares the results of five tests aimed at determining the speed of light.

Topics to be discussed include:

  • Five-Number Summaries
  • Minimum
  • First quartile
  • Median
  • Third quartile
  • Maximum
  • Box-Plots

Chapter 5: Summary

You learned about a statistician's core graphical tools for visualizing quantitative data in this session. As computers become more powerful, the discipline of statistical graphics continues to evolve. The basic graphs from the class may usually be modified using most software products. The Minitab software suite, for example, has seven options for making dot plots and four options for creating histograms.

Lesson 4: Displaying Qualitative Data: Percentages, Charts, and Graphs

Chapter 1: Introduction

In Lesson 4, we'll concentrate on qualitative data—how to summarize, show, and truly comprehend it. Percentages, pie charts, and bar charts will all be discussed. They're the kinds of summaries you'll see in the news and on the internet. We'll look at correlations between two qualitative variables after we've covered the basics.

Chapter 2: Percentages and Pies

First and foremost, qualitative data is concerned with attributes, features, or categories, such as male or female, agree or disagree, and Republican, Democratic, or Independent. Categorical data is another name for it. The first step is to generate a frequency table that lists the various categories as well as the number of observatories in each category.

Topics to be discussed include:

  • Percentages Versus Counts
  • Pie Charts

Chapter 3: Bar Charts and Two-Way Tables

The use of bar charts is just as frequent as the use of pie charts. I'm sure you've seen them before, and you've probably even made them. They may display a greater number of categories and are more adaptable than a pie chart. Even if your data is incomplete and the percentages do not add up to 100 percent, you can still make them. Each category in a bar chart is represented by a vertical or horizontal bar.

Topics to be discussed include:

  • Two-way tables

Chapter 4: Beyond the Basics: Is There an Association?

We can now search for possible links or linkages between category variables. We'll use the two-way tables and conditional distributions that we discussed in Chapter 3 to do this. However, before we return to the tables, let's create some stacked bar charts to accompany them.

Topics to be discussed include:

  • Stacked Bar Charts
  • Association int Two-way Tables

Chapter 5: Summary

In Lesson 4, you learned about qualitative data. Frequency tables, pie charts, bar charts, Pareto charts, stacked bar charts, and two-way tables were among the data visualization options you considered. Conditional distributions provided you actual insight into data, such as the differences between male and female survey respondents or organic and conventionally cultivated commodities.    

Lesson 5: Is There a Link? Scatterplots and Correlation

Chapter 1: Introduction

We'll use a different set of procedures with quantitative data than we used in Lesson 4 when we looked at the relationship between categorical variables. You'll learn how to create and understand scatterplots in order to visualize the relationship between variables. To examine for patterns and trends across time, you'll use time plots and line plots.

Chapter 2: Scatterplots

A scatterplot is a graph with one variable on the horizontal (or x) axis and the other variable on the vertical (or y) axis. (Think "Y travels to the sky" if you're having difficulties remembering which is which.)

Topics to be discussed include:

  • Direction of Association
  • Form of Association
  • Strength of Association
  • Unusual Points

Chapter 3: Times and Lines 

Topics to be discussed include:

  • Time Plots
  • Why Look for Trends or Cycles Over Time?

Chapter 4: Correlation: How Strong Is the Link?

Correlation refers to a specific number that you calculate using two quantitative variables. The number you get from the calculation tells you about the strength and direction of the association you've seen in a scatterplot.

Topics to be discussed include:

  • Using the Pearson Correlation Coefficient
  • A Correlation Formula
  • Beware of Outliers!
  • Correlation Doesn’t Man Causation

Chapter 5: Summary

In Lesson 5, you've seen how to display and measure the association between two quantitative variables. Making a scatterplot should always be your first step when exploring relationships between variables. A scatterplot helps you see the direction, form, and strength of any association, plus you can spot unusual values and outliers.

A time plot is a scatterplot where the horizontal variable is some measure of time, like days, months, or years. They're useful for seeing trends over time in all kinds of data, from sports to business to weather forecasting to public health. A line plot is similar to a time plot except the points are connected with lines. Connecting the points can make patterns more visible and allow you to distinguish one series of data values from another.

Lesson 6: Linear Regression: How Can We Predict the Future?

Chapter 1: Introduction

In this lesson, you'll learn to make predictions that are based on data. You'll use methods that statisticians rely on daily, and you'll examine data from sports, business, government, and environmental science.

In this lesson, we'll go beyond describing and measuring the association between two quantitative variables. You'll learn to use a process called linear regression to find an equation that models the pattern in a scatterplot.

Chapter 2: Don't Forget Your Lines!

Before we jump into finding lines to model data, we'll review the equations of lines. Then we'll discuss how statisticians use linear equations. The thought processes we'll use in this lesson are a little different from the thought processes of pure algebra. In case you need a reminder, slope is a number that measures the slant or steepness of the line. It can be positive, negative, zero, or even undefined. The situations we study in statistics will have either a positive slope (to go along with positive associations and correlations) or a negative slope (to go along with negative associations and correlations). In statistics, slope tells us how the data variables change in relation to one another.

Topics to be discussed include:

  • Other Ways to Write the Equation
  • Roles of the Variables
  • Lines as Models in Statistics

Chapter 3: What's My Line?

We first need to define what we mean by "best fit." Look at the graph below showing a line that models the pattern of the eight data points.

Topics to be discussed include:

  • World-Record Times Revisited
  • Predictiong Wind Speed in Hurricanes

Chapter 4: How Good Is My Regression Model?

We'll look at ways to visualize and explain data collected over time in this chapter. You'll learn how to use time plots and line plots to look at topics like quarterly sales, yearly advances in hurricane predictions, and sporting event trends.

Topics to be discussed include:

  • . What is R 2?   
  • Crime and Poverty Revisited
  • Weight in Motion Again
  • Prediction CO levels

Chapter 5: Summary

You found the equation of a linear model for two quantitative variables using linear regression in Lesson 6. One variable is designated as the predictor, while the other is designated as the response. Then you use the least squares approach to fit a line to the data. The slope and intercept of the line of best fit were calculated using this method.

Lesson 7: What's the Chance of That? Probability Concepts

Chapter 1: Introduction

The topic of probability will be the subject of this and the following lessons. To progress beyond the descriptive statistics we covered in the first six sessions, you'll need a basic understanding of probability. In the final lessons of the course, we'll use probability to build the framework we'll need to go on to inferential statistics.

Topics to be discussed include:

  • What is Probability

Chapter 2: Probability Basic Training

Probability allows us to characterize and quantify the likelihood of uncertain events. There is no uncertainty and no need for probability if an event always occurs or never occurs. However, due to randomness and unpredictability, many events are unpredictable, from a simple coin toss at the start of a baseball game to the performance of a complex stock transaction.

Topics to be discussed include:

  • Calculating Probabilities

  • Probability Facts

Chapter 3: Probability Rules!

Two or more simple events combine to form compound events. When you toss a coin once, you get a simple event with two outcomes: heads or tails. If you toss the coin three times, you'll get a compound event composed of three basic events. We'll look at a rule for calculating compound event probability in this chapter.

Topics to be discussed include:

  • Independence and the Product Rule

Chapter 4: The Sum Rule and Conditional Probability

Let's keep talking about compound event probability. First, I'll demonstrate how to calculate probabilities for events including the word or, such as the likelihood of rolling a seven or an eleven on dice. After that, we'll take a quick look at conditional probabilities.

Topics to be discussed include:

  • Sum Rule

  • Conditional Probability

Chapter 5: Summary

We've covered the fundamentals of probability in this session. You've seen how to use theoretical and empirical models to determine probability. We've used probability in scenarios as simple as card and dice games to more sophisticated ones like determining the likelihood of at least one inaccurate drug test result. 

Lesson 8: Probability Models: What's Normal?

Chapter 1: Introduction

Many of the general rules that influence probability were covered in Lesson 7. In this session, we'll apply some of these concepts, but our main focus will be on probability models, particularly the normal model. You've seen a normal model if you've ever heard the term "bell curve" or seen a picture of one.

Chapter 2: Probability Models: What Should You Expect?

A probability model (or distribution) for a random process specifies the process's various outcomes as well as the likelihood of each result. The probability model for rolling two dice and then adding the numbers that come up is shown in the table below.

Topics to be discussed include:

  • Expected Value

Chapter 3: The Normal Model

A normal model will not fully match a data set at every point. It does, however, provide us with a summary that helps us to acquire insight, evaluate probabilities, and make predictions. What we can learn without a model is restricted to the sample data available. We can make larger population generalizations using a model.

Topics to be discussed include:

  • Properties of Normal Model

  • The 68-95-99.7% Rule

Chapter 4: Estimating Normal Probabilities

We're ready to move on to more detailed normal probability calculations now that you've seen the normal model. Every computation can be simplified by following one rule. Over intervals, we'll always calculate normal probability. A range of numbers is referred to as an interval. You can calculate the probability that an observation is higher, less, or equal to some value, but not the probability that it is exactly equal to some value.

Topics to be discussed include:

  • From z-Scores to Normal Probabilities

  • From Probabilities to z-Scores to Percentiles

Chapter 5: Summary

We've looked at fundamental probability models in this session. We looked at the concept of a model's mean or expected value, and you saw how important the normal model is. A discrete probability model is one in which all conceivable outcomes and their probabilities can be counted and listed.

Lesson 9: The Key to Inference: Sampling Distributions

Chapter 1: Introduction

We're ready to bridge the gap between descriptive and inferential statistics and enter the domain of statistical inference now that you've learned about probability and the normal model. You'll learn the basics to moving beyond the sample at hand to make predictions and draw inferences about the overall population in this session.

Chapter 2: Sampling Distributions

In a same way, you can show the distribution of a sample statistic, such as the mean or a proportion. All we need to do now is expand on our prior conversations beyond a single example. Consider what would happen if the sampling process were repeated numerous times and the statistic was determined each time.

Topics to be discussed include:

  • Simulation of Sampling Distributions

  • What the Central Limit Theorem Says
  • How Big is Big Enough?

Chapter 3: Modeling Sample Means

In addition to understanding that the normal distribution may be used to model the mean of a sample, we also need to know the model's mean and standard deviation. We can't calculate probability or utilize the standard model without these numbers.

Topics to be discussed include:

  • Calculating With the Sampling Distribution of x?

Chapter 4: Modeling Sample Proportions

The normal model can also be used to model the distribution of a sample proportion in this chapter. There will be many parallels to the events in Chapter 3. There are various distinctions in nomenclature and standard deviation, so keep an eye out for these. If you haven't already read how a proportion compares to a mean in the FAQs, you might wish to do so now.

Topics to be discussed include:

  • Model for the Sample Proportion

  • Visualizing the Population, Data, and Sampling Distribution of p?
  • Calculations With the Sampling Distribution of p?

Chapter 5: Summary

You learned how to transition from describing a specific sample to making conclusions about a whole population in Lesson 9. We started with the concept of a statistic's sample distribution. When you take a sample from a population, you often compute a statistic like the sample mean or sample proportion. If you ran the sample process several times and plotted all of the resulting averages or proportions.

Lesson 10: How Certain Are We? Confidence Intervals for Proportions

Chapter 1: Introduction

In this lesson, you'll learn how to use the margin of error to go from a sample statement like "37 percent of those surveyed agreed with this statement" to a population statement like "We're 95 percent confident that between 35 percent and 39 percent of the population would agree with this statement."

Chapter 2: Estimating With Confidence

In reality, we frequently utilize the point estimate to estimate the associated population parameter as our best guess. This is a population summary number—for example, the proportion of all students who have taken an online class or the average selling price of all residences sold in the area. Keep in mind that in most cases, the demographic parameters are unknown.

Topics to be discussed include:

  • Confidence Intervals for a Proportion

  • Margin of Error

Chapter 3: Building Your Confidence in Confidence Intervals

We'll go a little deeper now that you've seen a few confidence intervals and know how to analyze them. We'll go over the technical assumptions and standards that must be met in order for your confidence intervals to be valid. You'll also have additional opportunities to practice calculating and understanding them in a variety of settings.

Topics to be discussed include:

  • The Necessary Details 

  • Is This Drug Any Better Than a Placebo?
  • Would You Pay More to Help the Environment?
  • How Safe is Your Chicken?

Chapter 4: How Large a Sample Do I Need?

It's impossible to predict how someone will respond to a survey or an experiment. The sample size is the one variable that you can control that affects the variability of proportions and the margin of error. Researchers frequently estimate the size sample they'll need when planning a survey or experiment in order to achieve a certain level of precision in their results.

Topics to be discussed include:

  • Estimating the Sample Size

  • Sample Size Formula
  • Planning a Donation Campaign 
  • Planning an Experiment

Chapter 5: Summary

You've seen the basics of proportion inference in this lesson. You built the margin of error by starting with the standard error and replacing the unknown population proportion with the sample proportion in the standard deviation formula. 

Lesson 11: Trial by Data: Testing Hypotheses About Proportions

Chapter 1: Introduction

You learned how to infer proportions using confidence intervals in the last session. We'll now turn our attention to the other important type of inference: hypothesis tests. Because assertions involving proportions are more prominent in everyday life, we'll start by testing a hypothesis regarding one. Then, in Lesson 12, you'll learn how to test a mean hypothesis.

Chapter 2: Hypothesis Testing Basics

You've seen how statistics starts with a question since the beginning of the course. Usually, you won't be able to answer it readily using information from a book or past knowledge. The data you collect serves as proof for you. You assess the evidence using statistics and probability to test the hypothesis. Formal hypothesis testing are used in this situation.

Topics to be discussed include:

  • Forming Hypotheses

  • Test Statistics: Weighing the Evidence
  • P-Values
  • Drawing a Conclusion

Chapter 3: A Trial by Data: The Reasoning of a Hypothesis Test

At first glance, the logic of hypothesis testing may appear to be backward. You don't come up with a hypothesis and then try to prove it. Instead, you state a null hypothesis (something you don't actually want to prove), assume it's true, and then try to disprove it. This oblique technique is a contradiction proof.

Topics to be discussed include:

  • How is All This like a Trial by Jury?

  • Interpreting p-Values
  • Is there really a Home Field Advantage?
  • Did Advertising Increase Awareness of your Product?

Chapter 4: Hypothesis Testing Details: Assumptions, Conditions, and Possible Errors

We need to go through a few things now that you've seen some hypothesis tests in action. In order for a test's results to be valid, you must consider certain assumptions and criteria, much as you do with confidence intervals. There's always the risk of making the wrong decision when drawing a conclusion from a test. 

Topics to be discussed include:

  • Checking the Assumptions and Conditions

  • Is there Evidence of Gender Bias in Promotion?
  • What can Go wrong
  • Did the events of September 11 Affect the plans of High School Seniors?

Chapter 5: Summary

You learned the techniques and reasoning processes needed to conduct and analyze a percentage hypothesis test in Lesson 11. Over the last few sessions, we've been building up to this lesson. The computations themselves weren't really novel at this time, but we put them to fresh use.

Lesson 12: Inference About Means

Chapter 1: Introduction

We'll look at inference for means now that you've seen how it works for a proportion. Not all data is binary, like "yes or no" or "success or failure." In Lesson 12, you'll learn how to establish confidence intervals for the mean of a quantitative variable and how to test hypotheses for it.

Chapter 2: Building Confidence in the Mean

The sample mean x (x-bar) is our best estimation, or point estimate, of the population mean for quantitative data (mu). Even though it's our best estimation, there's only one figure that deviates from the population mean. We'll establish a margin of error around the sample mean, just like we did with proportions, to form an interval that's likely to capture the population mean.

Topics to be discussed include:

  • Student’s t
  • Confidence Intervals for the Mean
  • Assumptions and Conditions
  • Small Sample Example

Chapter 3: Testing Claims About a Mean

A catheter is manufactured by a medical equipment firm for use during an angiography, a heart treatment. The catheter is inserted into a vein in the leg and threaded up into the heart during the surgery. During manufacture, it's critical to keep the catheter's diameter at 2 millimeters. It may not perform properly if it is too big or too little.

Topics to be discussed include:

  • Does College Delay Marriage?

  • Dows a New Tire Design Improve Gas Mileage

Chapter 4: Paired Samples

The fuel-mileage data we discussed at the end of the previous chapter isn't truly two separate samples. They're an example of paired data instead. Engineers tested both types of tires under identical settings with the same nine test vehicles, each providing a pair of data values.

Topics to be discussed include:

  • How Effective are Low-Carb Diets?

Chapter 5: Summary

Inference for proportions and means has now been observed. Both types of statistics have comparable concepts. When it comes to the standard error, standard deviation, and critical value models, the computations differ. Instead of using the normal model for critical values and p-values, you use the t-distribution as the probability model for means.

How is the Online Statistics Course studied?

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The CoursesForSuccess.com online statistics course is designed to be as easy to follow and comprehend.

Recognition & Accreditation

All students who completeIntroduction to Statistics Online Course receive a certificate of completion with a passing score (for the online assessment) and will be issued a certificate via email.

There are 12 units of study

What Is Statistics, Anyway?
What's statistics really about? How do you collect reliable data and use it to make informed decisions? In Lesson 1, you'll learn some of the concepts and terms we'll use throughout the course. You'll also find out how statistics affects events in the news and in your everyday life.

Quantitative Data: From Averages to z-Scores
Once you have a set of data, how can you summarize and interpret it to figure out what it really means? In Lesson 2, you'll learn to summarize data and describe its center along with its variability. As you learn to calculate mean, median, range, and standard deviation, I'll provide examples from different workplaces. You'll see how statistics plays a part in medicine, human resources, education, politics, finance, and marketing.

Displaying Quantitative Data: Dots, Plots, and Histograms
Is there an easier way of understanding data than peering at column after column of numbers? Yes! In Lesson 3, you'll see quantitative data displayed in dot plots, histograms, and many other forms. Knowing how to read and construct these graphs will help you see patterns and spot unusual values in data. Our examples in this lesson come from medicine, mortgage lending, classrooms, biologists' field notes, and the aisles of your local grocery store. 

Displaying Qualitative Data: Percentages, Charts, and Graphs
"How much satisfaction do you get from your friendships?" "Would you vote for a qualified woman for president?" "Which mountain in the Himalayas is most dangerous to climb?" In Lesson 4, you'll learn to summarize and display qualitative data from questions like these. We'll focus on charts and tables, and along the way you'll see examples from business, government, and medicine.

Is There a Link? Scatterplots and Correlation
Is there a link between the poverty rate and the crime rate? Is your score on a math exam related to your anxiety level? In Lesson 5, we'll look at relationships between two quantitative variables. You'll learn to make scatterplots and describe what you see. You'll also use correlation to measure the strength of a particular link.

Linear Regression: How Can We Predict the Future?
Can we predict the next world-record time in the mile run? How can we forecast CO2 levels in the atmosphere? In Lesson 6, we'll go beyond describing and measuring association between variables. You'll use linear regression to find an equation that models the data. Then you'll use the equation to make predictions.

What's the Chance of That? Probability Concepts
What's the chance you'll have a coin come up "heads" five times in a row? What about drawing four aces from a deck of cards or picking the winning lottery numbers? In Lesson 7, we'll study the basics of probability. You'll learn the rules that govern probability and see how to apply them in a variety of situations. 

Probability Models: What's Normal?
What should you expect to happen in a game involving chance? How can you estimate the probability that a healthy baby will be born underweight? In Lesson 8, we'll talk about probability models and expected value. Our focus will be the most common probability model in statistics: the normal model. You'll see this bell-shaped distribution in a variety of settings, and you'll learn to use it to estimate probabilities.

The Key to Inference: Sampling Distributions
How can we move beyond the sample at hand to make predictions and draw conclusions about the population? In Lesson 9, you'll discover the key that lets you make inferences about the population. You'll see the most important result in all of statistics—the central limit theorem—and you'll learn why the normal model plays such an important role in statistics. You'll also find out how to use data and probability to evaluate a claim.

How Certain Are We? Confidence Intervals for Proportions
"The margin of error for this poll is plus or minus 3%." What does that mean, anyway? In Lesson 10, we'll begin our journey into statistical inference by focusing on confidence intervals for proportions. You'll learn to calculate the margin of error and use it to build an interval for estimating a population proportion. You'll also see how to estimate the sample size you'd need for a survey.

Trial by Data: Testing Hypotheses About Proportions
Is there really a home team advantage in sports? Did that television ad your company bought result in increased awareness of your product? In Lesson 11, you'll learn to answer questions such as these by testing an appropriate hypothesis using proportions. You'll find out what steps make up a hypothesis test and how you can interpret the results, make inferences, and arrive at informed decisions.

Inference About Means
How do you test hypotheses about means? For example, how can you use a confidence interval to estimate the average number of hours Americans use the Internet each week? In our last lesson together, you'll get an introduction to inference for means. You'll learn to calculate and interpret confidence intervals and hypothesis tests for a mean, and you'll see how to analyze data from a paired experiment. And while we're at it, you'll find out what the history of statistics has to do with the quality of beer in Ireland.

Entry requirements

Students must have basic literacy and numeracy skills.

Minimum education

Open entry. Previous schooling and academic achievements are not required for entry into this course.

Computer requirements

Students will need access to a computer and the internet.

Minimum specifications for the computer are:

Windows:

  • Microsoft Windows XP, or later
  • Modern and up to date Browser (Internet Explorer 8 or later, Firefox, Chrome, Safari)

MAC/iOS

  • OSX/iOS 6 or later
  • Modern and up to date Browser (Firefox, Chrome, Safari)

All systems

  • Internet bandwidth of 1Mb or faster
  • Flash player or a browser with HTML5 video capabilities(Currently Internet Explorer 9, Firefox, Chrome, Safari)

Students will also need access the following applications:

Adobe Acrobat Reader

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About this Course

Study Introduction to Statistics Online Course and Improve Your Data Analysis 

Our Online Statistics course is perfect for anybody needing to gain a better understanding of statistics. Regardless of whether statistics is a new field for you or you just need to refresh your statistical knowledge, this statistics online course will further your knowledge of statistics and enhance your ability to make informed decisions using the data available to you.

This introductory online statistics course combines gives you the knowledge, and skills to analyze data with confidence. Throughout the course, you will be given opportunities to investigate the use and application of data to real-life problems and circumstances.

The statistics course online will give you a complete introduction to the world of statistics, starting with the all-important concepts of data and data collection practices. With those concepts bedded down, you will go on to learn how to summarize statistical information using graphs, charts, and numbers. You will discover how to determine probabilities and form an understanding of how probabilities are used in the decision-making process.

These statistics classes online will show you how to visualize and measure data relationships. You will also learn how to effectively utilize data for forecasting and prediction purposes. The statistics training online will also explore the fundamentals of statistical inference and introduce you to the concept of statistically significant results. 

This statistics class online training consistently makes use of real data and an array of practical examples sourced across the statistical spectrum. You will use data related to business and industry, sports, politics, health care and medicine, education, politics, the social sciences, and news events.

There is no need to be concerned about having to perform intricate calculations as the online statistics class will introduce you to a range of free online resources that will assist you in making calculations.

After taking statistics online course you will have no trouble in being able to effectively apply and interpret fundamental statistical procedures.

What you will learn with our Introduction to Statistics Online Course

  • Quantitative Data
  • Displaying Quantitative Data
  • Displaying Qualitative Data
  • Linear Regression
  • Probability Models
  • The Key to Inference
  • Testing Hypotheses About Proportions

Introduction to Statistics Online Course - Requirements

The Introduction to Statistics Course is delivered 100 percent online 24/7.

To successfully complete this course, a student must:

  • Have access to the internet and the necessary technical skills to navigate the online learning resources
  • Have access to any mobile device with internet connectivity (laptop, desktop, tablet)
  • Be a self-directed learner
  • Possess sound language and literacy skills

Quick Course Facts

  1. Course content is structured for easy comprehension
  2. Registered students gain unrestricted access to the Introduction to Statistics Course
  3. All course material is available online 24/7 and can be accessed using any device
  4. Study online from anywhere in your own time at your own pace
  5. All students who complete the course will be awarded with a certificate of completion

For any additional questions please see our comprehensive FAQS tab above.

Introduction to Statistics Online Course Outline

Chapter 1: Introduction

Topics to be discussed include:

  • Why Learn Statistics?

Chapter 2: The Basics of Statistics

It's a field of study that deals with data analysis. What is data, by the way? Information is data! It's information with context and meaning, not just a tangle of numbers, in particular. Plan, tools, and methods for collecting, displaying, analyzing, and interpreting data are all part of modern statistics.

Topics to be discussed include:

  • Various Variables
  • It Starts with a question

Chapter 3: Data Collection Using Sampling

Taking a census of the entire population, as you taught in Chapter 2, is frequently prohibitively costly and time-consuming. As a result, you'll need to take a random sample of the population. We'll look at numerous strategies to ensure that the sample appropriately reflects the larger population in this chapter. You'll see how high-quality data isn't created by accident... though it does play a part.

Topics to be discussed include:

  • Sampling Methods
  • Systematic random sampling
  • Stratified random sampling
  • Cluster sampling

Chapter 4: Designing Experiments to Collect Data

Experiments have provided us with a lot of information on health, nutrition, and human behavior. Without deliberate trials, breakthroughs in agriculture that help feed us all and advances in medicine that aid you when you're sick would not have been feasible. We'd have to rely on guessing, luck, folklore, or doing things the way we've always done them if we didn't have them.

Topics to be discussed include:

  • Designed Experiments: The Basics
  • Control
  • Randomization
  • Blinding
  • Replication
  • Observational Studies

Chapter 5: Summary

Every statistical study begins with a question, as we discussed in Chapter 2. It's critical to select samples that accurately reflect the population as a whole in order to obtain credible results. The key is to use random sampling. And every experiment that is designed to answer a question must be controlled, randomized, blinded, and reproducible.

Lesson 2: Quantitative Data: From Averages to z-Scores

Chapter 1: Introduction

One of my keys aims for this course is for you to be able to understand statistics and statistical results that you see in your daily life, interpret them correctly, and even build your own sound statistical studies. Statistics knowledge will aid you in interpreting data and making sound judgments.

Chapter 2: Journey to the Center of Your Data

Look over your data before you start any computations or graphs, especially if you didn't collect and compile it yourself. It's what one of my graduate school professors referred to as "eyeballing" your data. When you eyeball the data, you skim it to get a sense of the size of the values, look for missing or incorrect numbers, and try to notice any values that don't make sense.

Topics to be discussed include:

  • Calculating the Mean
  • Finding the Median
  • Comparing Means and Medians

Chapter 3: Variability: The Spice of Statistics Life

Consider how people differ in a variety of ways. To name a few characteristics, we differ in age, weight, height, gender, race, family history, income, and education. Throughout our lifetimes, we all encounter changes in our income and weight, as well as changes in typical health metrics like temperature, heart rate, and blood pressure.

Topics to be discussed include:

  • Determining the range
  • Standard Deviation

Chapter 4: All About z-Scores

Here's an example of a common method that incorporates some of the concepts covered in this lecture. It also introduces a concept that will be revisited later in the course.

Topics to be discussed include:

  • What are Z-scores
  • Another Option

Chapter 5: Summary

You learned some basic descriptive statistics in this course that you'll need practically every time you study quantitative data. The mean or median can be used to describe the center or average of quantitative data. The mean takes into account all of the data, but extreme results, or outliers, may have an impact. To get the middle number, the median uses the order of the data.

Lesson 3: Displaying Quantitative Data: Dots, Plots, and Histograms

Chapter 1: Introduction

Nobody wants to spend time staring at numbers in columns and rows, trying to see patterns. Data visualizations make it easier to find trends and detect unexpected values, as well as being more pleasing to the eye. If you have to show data at work, you'll almost certainly do it with a graph. The plots and graphs you'll see in this session are the statistician's basic graphical tools.

Chapter 2: Displaying Data: Dots, Stems, and Leaves

It's simpler to see the distribution, or pattern of variation, in statistical data when it's displayed on a graph. Dot plots are frequently used by statisticians as a first step in visualizing data distribution.

Topics to be discussed include:

  • Dot plots
  • Steam-and-Leaf plots
  • Steam-and-Leaf plots: White Refusal Rate

Chapter 3: Histograms

The charts you saw in Chapter 2 may get cluttered with larger data sets. The histogram, which is usually a preferable solution for huge data sets, is covered in this chapter. You split your data into equal-width "bins" to make a histogram. The number of data values that fall within each bin is then counted. 

Topics to be discussed include:

  • What should I look for in a Histogram
  • Where would you guess the mean and median for this variable would fall in the histogram
  • Where would you guess the mean and median are in this histogram?
  • What stands out to you?

Chapter 4: Advanced Visuals: Five-Number Summaries and Box Plots

When performing these kinds of comparisons, a box plot is a useful tool. This is how one appears. This one compares the results of five tests aimed at determining the speed of light.

Topics to be discussed include:

  • Five-Number Summaries
  • Minimum
  • First quartile
  • Median
  • Third quartile
  • Maximum
  • Box-Plots

Chapter 5: Summary

You learned about a statistician's core graphical tools for visualizing quantitative data in this session. As computers become more powerful, the discipline of statistical graphics continues to evolve. The basic graphs from the class may usually be modified using most software products. The Minitab software suite, for example, has seven options for making dot plots and four options for creating histograms.

Lesson 4: Displaying Qualitative Data: Percentages, Charts, and Graphs

Chapter 1: Introduction

In Lesson 4, we'll concentrate on qualitative data—how to summarize, show, and truly comprehend it. Percentages, pie charts, and bar charts will all be discussed. They're the kinds of summaries you'll see in the news and on the internet. We'll look at correlations between two qualitative variables after we've covered the basics.

Chapter 2: Percentages and Pies

First and foremost, qualitative data is concerned with attributes, features, or categories, such as male or female, agree or disagree, and Republican, Democratic, or Independent. Categorical data is another name for it. The first step is to generate a frequency table that lists the various categories as well as the number of observatories in each category.

Topics to be discussed include:

  • Percentages Versus Counts
  • Pie Charts

Chapter 3: Bar Charts and Two-Way Tables

The use of bar charts is just as frequent as the use of pie charts. I'm sure you've seen them before, and you've probably even made them. They may display a greater number of categories and are more adaptable than a pie chart. Even if your data is incomplete and the percentages do not add up to 100 percent, you can still make them. Each category in a bar chart is represented by a vertical or horizontal bar.

Topics to be discussed include:

  • Two-way tables

Chapter 4: Beyond the Basics: Is There an Association?

We can now search for possible links or linkages between category variables. We'll use the two-way tables and conditional distributions that we discussed in Chapter 3 to do this. However, before we return to the tables, let's create some stacked bar charts to accompany them.

Topics to be discussed include:

  • Stacked Bar Charts
  • Association int Two-way Tables

Chapter 5: Summary

In Lesson 4, you learned about qualitative data. Frequency tables, pie charts, bar charts, Pareto charts, stacked bar charts, and two-way tables were among the data visualization options you considered. Conditional distributions provided you actual insight into data, such as the differences between male and female survey respondents or organic and conventionally cultivated commodities.    

Lesson 5: Is There a Link? Scatterplots and Correlation

Chapter 1: Introduction

We'll use a different set of procedures with quantitative data than we used in Lesson 4 when we looked at the relationship between categorical variables. You'll learn how to create and understand scatterplots in order to visualize the relationship between variables. To examine for patterns and trends across time, you'll use time plots and line plots.

Chapter 2: Scatterplots

A scatterplot is a graph with one variable on the horizontal (or x) axis and the other variable on the vertical (or y) axis. (Think "Y travels to the sky" if you're having difficulties remembering which is which.)

Topics to be discussed include:

  • Direction of Association
  • Form of Association
  • Strength of Association
  • Unusual Points

Chapter 3: Times and Lines 

Topics to be discussed include:

  • Time Plots
  • Why Look for Trends or Cycles Over Time?

Chapter 4: Correlation: How Strong Is the Link?

Correlation refers to a specific number that you calculate using two quantitative variables. The number you get from the calculation tells you about the strength and direction of the association you've seen in a scatterplot.

Topics to be discussed include:

  • Using the Pearson Correlation Coefficient
  • A Correlation Formula
  • Beware of Outliers!
  • Correlation Doesn’t Man Causation

Chapter 5: Summary

In Lesson 5, you've seen how to display and measure the association between two quantitative variables. Making a scatterplot should always be your first step when exploring relationships between variables. A scatterplot helps you see the direction, form, and strength of any association, plus you can spot unusual values and outliers.

A time plot is a scatterplot where the horizontal variable is some measure of time, like days, months, or years. They're useful for seeing trends over time in all kinds of data, from sports to business to weather forecasting to public health. A line plot is similar to a time plot except the points are connected with lines. Connecting the points can make patterns more visible and allow you to distinguish one series of data values from another.

Lesson 6: Linear Regression: How Can We Predict the Future?

Chapter 1: Introduction

In this lesson, you'll learn to make predictions that are based on data. You'll use methods that statisticians rely on daily, and you'll examine data from sports, business, government, and environmental science.

In this lesson, we'll go beyond describing and measuring the association between two quantitative variables. You'll learn to use a process called linear regression to find an equation that models the pattern in a scatterplot.

Chapter 2: Don't Forget Your Lines!

Before we jump into finding lines to model data, we'll review the equations of lines. Then we'll discuss how statisticians use linear equations. The thought processes we'll use in this lesson are a little different from the thought processes of pure algebra. In case you need a reminder, slope is a number that measures the slant or steepness of the line. It can be positive, negative, zero, or even undefined. The situations we study in statistics will have either a positive slope (to go along with positive associations and correlations) or a negative slope (to go along with negative associations and correlations). In statistics, slope tells us how the data variables change in relation to one another.

Topics to be discussed include:

  • Other Ways to Write the Equation
  • Roles of the Variables
  • Lines as Models in Statistics

Chapter 3: What's My Line?

We first need to define what we mean by "best fit." Look at the graph below showing a line that models the pattern of the eight data points.

Topics to be discussed include:

  • World-Record Times Revisited
  • Predictiong Wind Speed in Hurricanes

Chapter 4: How Good Is My Regression Model?

We'll look at ways to visualize and explain data collected over time in this chapter. You'll learn how to use time plots and line plots to look at topics like quarterly sales, yearly advances in hurricane predictions, and sporting event trends.

Topics to be discussed include:

  • . What is R 2?   
  • Crime and Poverty Revisited
  • Weight in Motion Again
  • Prediction CO levels

Chapter 5: Summary

You found the equation of a linear model for two quantitative variables using linear regression in Lesson 6. One variable is designated as the predictor, while the other is designated as the response. Then you use the least squares approach to fit a line to the data. The slope and intercept of the line of best fit were calculated using this method.

Lesson 7: What's the Chance of That? Probability Concepts

Chapter 1: Introduction

The topic of probability will be the subject of this and the following lessons. To progress beyond the descriptive statistics we covered in the first six sessions, you'll need a basic understanding of probability. In the final lessons of the course, we'll use probability to build the framework we'll need to go on to inferential statistics.

Topics to be discussed include:

  • What is Probability

Chapter 2: Probability Basic Training

Probability allows us to characterize and quantify the likelihood of uncertain events. There is no uncertainty and no need for probability if an event always occurs or never occurs. However, due to randomness and unpredictability, many events are unpredictable, from a simple coin toss at the start of a baseball game to the performance of a complex stock transaction.

Topics to be discussed include:

  • Calculating Probabilities

  • Probability Facts

Chapter 3: Probability Rules!

Two or more simple events combine to form compound events. When you toss a coin once, you get a simple event with two outcomes: heads or tails. If you toss the coin three times, you'll get a compound event composed of three basic events. We'll look at a rule for calculating compound event probability in this chapter.

Topics to be discussed include:

  • Independence and the Product Rule

Chapter 4: The Sum Rule and Conditional Probability

Let's keep talking about compound event probability. First, I'll demonstrate how to calculate probabilities for events including the word or, such as the likelihood of rolling a seven or an eleven on dice. After that, we'll take a quick look at conditional probabilities.

Topics to be discussed include:

  • Sum Rule

  • Conditional Probability

Chapter 5: Summary

We've covered the fundamentals of probability in this session. You've seen how to use theoretical and empirical models to determine probability. We've used probability in scenarios as simple as card and dice games to more sophisticated ones like determining the likelihood of at least one inaccurate drug test result. 

Lesson 8: Probability Models: What's Normal?

Chapter 1: Introduction

Many of the general rules that influence probability were covered in Lesson 7. In this session, we'll apply some of these concepts, but our main focus will be on probability models, particularly the normal model. You've seen a normal model if you've ever heard the term "bell curve" or seen a picture of one.

Chapter 2: Probability Models: What Should You Expect?

A probability model (or distribution) for a random process specifies the process's various outcomes as well as the likelihood of each result. The probability model for rolling two dice and then adding the numbers that come up is shown in the table below.

Topics to be discussed include:

  • Expected Value

Chapter 3: The Normal Model

A normal model will not fully match a data set at every point. It does, however, provide us with a summary that helps us to acquire insight, evaluate probabilities, and make predictions. What we can learn without a model is restricted to the sample data available. We can make larger population generalizations using a model.

Topics to be discussed include:

  • Properties of Normal Model

  • The 68-95-99.7% Rule

Chapter 4: Estimating Normal Probabilities

We're ready to move on to more detailed normal probability calculations now that you've seen the normal model. Every computation can be simplified by following one rule. Over intervals, we'll always calculate normal probability. A range of numbers is referred to as an interval. You can calculate the probability that an observation is higher, less, or equal to some value, but not the probability that it is exactly equal to some value.

Topics to be discussed include:

  • From z-Scores to Normal Probabilities

  • From Probabilities to z-Scores to Percentiles

Chapter 5: Summary

We've looked at fundamental probability models in this session. We looked at the concept of a model's mean or expected value, and you saw how important the normal model is. A discrete probability model is one in which all conceivable outcomes and their probabilities can be counted and listed.

Lesson 9: The Key to Inference: Sampling Distributions

Chapter 1: Introduction

We're ready to bridge the gap between descriptive and inferential statistics and enter the domain of statistical inference now that you've learned about probability and the normal model. You'll learn the basics to moving beyond the sample at hand to make predictions and draw inferences about the overall population in this session.

Chapter 2: Sampling Distributions

In a same way, you can show the distribution of a sample statistic, such as the mean or a proportion. All we need to do now is expand on our prior conversations beyond a single example. Consider what would happen if the sampling process were repeated numerous times and the statistic was determined each time.

Topics to be discussed include:

  • Simulation of Sampling Distributions

  • What the Central Limit Theorem Says
  • How Big is Big Enough?

Chapter 3: Modeling Sample Means

In addition to understanding that the normal distribution may be used to model the mean of a sample, we also need to know the model's mean and standard deviation. We can't calculate probability or utilize the standard model without these numbers.

Topics to be discussed include:

  • Calculating With the Sampling Distribution of x?

Chapter 4: Modeling Sample Proportions

The normal model can also be used to model the distribution of a sample proportion in this chapter. There will be many parallels to the events in Chapter 3. There are various distinctions in nomenclature and standard deviation, so keep an eye out for these. If you haven't already read how a proportion compares to a mean in the FAQs, you might wish to do so now.

Topics to be discussed include:

  • Model for the Sample Proportion

  • Visualizing the Population, Data, and Sampling Distribution of p?
  • Calculations With the Sampling Distribution of p?

Chapter 5: Summary

You learned how to transition from describing a specific sample to making conclusions about a whole population in Lesson 9. We started with the concept of a statistic's sample distribution. When you take a sample from a population, you often compute a statistic like the sample mean or sample proportion. If you ran the sample process several times and plotted all of the resulting averages or proportions.

Lesson 10: How Certain Are We? Confidence Intervals for Proportions

Chapter 1: Introduction

In this lesson, you'll learn how to use the margin of error to go from a sample statement like "37 percent of those surveyed agreed with this statement" to a population statement like "We're 95 percent confident that between 35 percent and 39 percent of the population would agree with this statement."

Chapter 2: Estimating With Confidence

In reality, we frequently utilize the point estimate to estimate the associated population parameter as our best guess. This is a population summary number—for example, the proportion of all students who have taken an online class or the average selling price of all residences sold in the area. Keep in mind that in most cases, the demographic parameters are unknown.

Topics to be discussed include:

  • Confidence Intervals for a Proportion

  • Margin of Error

Chapter 3: Building Your Confidence in Confidence Intervals

We'll go a little deeper now that you've seen a few confidence intervals and know how to analyze them. We'll go over the technical assumptions and standards that must be met in order for your confidence intervals to be valid. You'll also have additional opportunities to practice calculating and understanding them in a variety of settings.

Topics to be discussed include:

  • The Necessary Details 

  • Is This Drug Any Better Than a Placebo?
  • Would You Pay More to Help the Environment?
  • How Safe is Your Chicken?

Chapter 4: How Large a Sample Do I Need?

It's impossible to predict how someone will respond to a survey or an experiment. The sample size is the one variable that you can control that affects the variability of proportions and the margin of error. Researchers frequently estimate the size sample they'll need when planning a survey or experiment in order to achieve a certain level of precision in their results.

Topics to be discussed include:

  • Estimating the Sample Size

  • Sample Size Formula
  • Planning a Donation Campaign 
  • Planning an Experiment

Chapter 5: Summary

You've seen the basics of proportion inference in this lesson. You built the margin of error by starting with the standard error and replacing the unknown population proportion with the sample proportion in the standard deviation formula. 

Lesson 11: Trial by Data: Testing Hypotheses About Proportions

Chapter 1: Introduction

You learned how to infer proportions using confidence intervals in the last session. We'll now turn our attention to the other important type of inference: hypothesis tests. Because assertions involving proportions are more prominent in everyday life, we'll start by testing a hypothesis regarding one. Then, in Lesson 12, you'll learn how to test a mean hypothesis.

Chapter 2: Hypothesis Testing Basics

You've seen how statistics starts with a question since the beginning of the course. Usually, you won't be able to answer it readily using information from a book or past knowledge. The data you collect serves as proof for you. You assess the evidence using statistics and probability to test the hypothesis. Formal hypothesis testing are used in this situation.

Topics to be discussed include:

  • Forming Hypotheses

  • Test Statistics: Weighing the Evidence
  • P-Values
  • Drawing a Conclusion

Chapter 3: A Trial by Data: The Reasoning of a Hypothesis Test

At first glance, the logic of hypothesis testing may appear to be backward. You don't come up with a hypothesis and then try to prove it. Instead, you state a null hypothesis (something you don't actually want to prove), assume it's true, and then try to disprove it. This oblique technique is a contradiction proof.

Topics to be discussed include:

  • How is All This like a Trial by Jury?

  • Interpreting p-Values
  • Is there really a Home Field Advantage?
  • Did Advertising Increase Awareness of your Product?

Chapter 4: Hypothesis Testing Details: Assumptions, Conditions, and Possible Errors

We need to go through a few things now that you've seen some hypothesis tests in action. In order for a test's results to be valid, you must consider certain assumptions and criteria, much as you do with confidence intervals. There's always the risk of making the wrong decision when drawing a conclusion from a test. 

Topics to be discussed include:

  • Checking the Assumptions and Conditions

  • Is there Evidence of Gender Bias in Promotion?
  • What can Go wrong
  • Did the events of September 11 Affect the plans of High School Seniors?

Chapter 5: Summary

You learned the techniques and reasoning processes needed to conduct and analyze a percentage hypothesis test in Lesson 11. Over the last few sessions, we've been building up to this lesson. The computations themselves weren't really novel at this time, but we put them to fresh use.

Lesson 12: Inference About Means

Chapter 1: Introduction

We'll look at inference for means now that you've seen how it works for a proportion. Not all data is binary, like "yes or no" or "success or failure." In Lesson 12, you'll learn how to establish confidence intervals for the mean of a quantitative variable and how to test hypotheses for it.

Chapter 2: Building Confidence in the Mean

The sample mean x (x-bar) is our best estimation, or point estimate, of the population mean for quantitative data (mu). Even though it's our best estimation, there's only one figure that deviates from the population mean. We'll establish a margin of error around the sample mean, just like we did with proportions, to form an interval that's likely to capture the population mean.

Topics to be discussed include:

  • Student’s t
  • Confidence Intervals for the Mean
  • Assumptions and Conditions
  • Small Sample Example

Chapter 3: Testing Claims About a Mean

A catheter is manufactured by a medical equipment firm for use during an angiography, a heart treatment. The catheter is inserted into a vein in the leg and threaded up into the heart during the surgery. During manufacture, it's critical to keep the catheter's diameter at 2 millimeters. It may not perform properly if it is too big or too little.

Topics to be discussed include:

  • Does College Delay Marriage?

  • Dows a New Tire Design Improve Gas Mileage

Chapter 4: Paired Samples

The fuel-mileage data we discussed at the end of the previous chapter isn't truly two separate samples. They're an example of paired data instead. Engineers tested both types of tires under identical settings with the same nine test vehicles, each providing a pair of data values.

Topics to be discussed include:

  • How Effective are Low-Carb Diets?

Chapter 5: Summary

Inference for proportions and means has now been observed. Both types of statistics have comparable concepts. When it comes to the standard error, standard deviation, and critical value models, the computations differ. Instead of using the normal model for critical values and p-values, you use the t-distribution as the probability model for means.

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The CoursesForSuccess.com online statistics course is designed to be as easy to follow and comprehend.

Recognition & Accreditation

All students who completeIntroduction to Statistics Online Course receive a certificate of completion with a passing score (for the online assessment) and will be issued a certificate via email.

There are 12 units of study

What Is Statistics, Anyway?
What's statistics really about? How do you collect reliable data and use it to make informed decisions? In Lesson 1, you'll learn some of the concepts and terms we'll use throughout the course. You'll also find out how statistics affects events in the news and in your everyday life.

Quantitative Data: From Averages to z-Scores
Once you have a set of data, how can you summarize and interpret it to figure out what it really means? In Lesson 2, you'll learn to summarize data and describe its center along with its variability. As you learn to calculate mean, median, range, and standard deviation, I'll provide examples from different workplaces. You'll see how statistics plays a part in medicine, human resources, education, politics, finance, and marketing.

Displaying Quantitative Data: Dots, Plots, and Histograms
Is there an easier way of understanding data than peering at column after column of numbers? Yes! In Lesson 3, you'll see quantitative data displayed in dot plots, histograms, and many other forms. Knowing how to read and construct these graphs will help you see patterns and spot unusual values in data. Our examples in this lesson come from medicine, mortgage lending, classrooms, biologists' field notes, and the aisles of your local grocery store. 

Displaying Qualitative Data: Percentages, Charts, and Graphs
"How much satisfaction do you get from your friendships?" "Would you vote for a qualified woman for president?" "Which mountain in the Himalayas is most dangerous to climb?" In Lesson 4, you'll learn to summarize and display qualitative data from questions like these. We'll focus on charts and tables, and along the way you'll see examples from business, government, and medicine.

Is There a Link? Scatterplots and Correlation
Is there a link between the poverty rate and the crime rate? Is your score on a math exam related to your anxiety level? In Lesson 5, we'll look at relationships between two quantitative variables. You'll learn to make scatterplots and describe what you see. You'll also use correlation to measure the strength of a particular link.

Linear Regression: How Can We Predict the Future?
Can we predict the next world-record time in the mile run? How can we forecast CO2 levels in the atmosphere? In Lesson 6, we'll go beyond describing and measuring association between variables. You'll use linear regression to find an equation that models the data. Then you'll use the equation to make predictions.

What's the Chance of That? Probability Concepts
What's the chance you'll have a coin come up "heads" five times in a row? What about drawing four aces from a deck of cards or picking the winning lottery numbers? In Lesson 7, we'll study the basics of probability. You'll learn the rules that govern probability and see how to apply them in a variety of situations. 

Probability Models: What's Normal?
What should you expect to happen in a game involving chance? How can you estimate the probability that a healthy baby will be born underweight? In Lesson 8, we'll talk about probability models and expected value. Our focus will be the most common probability model in statistics: the normal model. You'll see this bell-shaped distribution in a variety of settings, and you'll learn to use it to estimate probabilities.

The Key to Inference: Sampling Distributions
How can we move beyond the sample at hand to make predictions and draw conclusions about the population? In Lesson 9, you'll discover the key that lets you make inferences about the population. You'll see the most important result in all of statistics—the central limit theorem—and you'll learn why the normal model plays such an important role in statistics. You'll also find out how to use data and probability to evaluate a claim.

How Certain Are We? Confidence Intervals for Proportions
"The margin of error for this poll is plus or minus 3%." What does that mean, anyway? In Lesson 10, we'll begin our journey into statistical inference by focusing on confidence intervals for proportions. You'll learn to calculate the margin of error and use it to build an interval for estimating a population proportion. You'll also see how to estimate the sample size you'd need for a survey.

Trial by Data: Testing Hypotheses About Proportions
Is there really a home team advantage in sports? Did that television ad your company bought result in increased awareness of your product? In Lesson 11, you'll learn to answer questions such as these by testing an appropriate hypothesis using proportions. You'll find out what steps make up a hypothesis test and how you can interpret the results, make inferences, and arrive at informed decisions.

Inference About Means
How do you test hypotheses about means? For example, how can you use a confidence interval to estimate the average number of hours Americans use the Internet each week? In our last lesson together, you'll get an introduction to inference for means. You'll learn to calculate and interpret confidence intervals and hypothesis tests for a mean, and you'll see how to analyze data from a paired experiment. And while we're at it, you'll find out what the history of statistics has to do with the quality of beer in Ireland.

Entry requirements

Students must have basic literacy and numeracy skills.

Minimum education

Open entry. Previous schooling and academic achievements are not required for entry into this course.

Computer requirements

Students will need access to a computer and the internet.

Minimum specifications for the computer are:

Windows:

  • Microsoft Windows XP, or later
  • Modern and up to date Browser (Internet Explorer 8 or later, Firefox, Chrome, Safari)

MAC/iOS

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  • Modern and up to date Browser (Firefox, Chrome, Safari)

All systems

  • Internet bandwidth of 1Mb or faster
  • Flash player or a browser with HTML5 video capabilities(Currently Internet Explorer 9, Firefox, Chrome, Safari)

Students will also need access the following applications:

Adobe Acrobat Reader

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Course Summary

Course ID: 007STAT
Delivery Mode: Online
Access: 3 months
Tutor Support: Yes
Time: 40 Hours
Assessments: Yes
Qualification: Certificate

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