Syllabus

• Week 1

  Descriptive Statistics

  • About the Course

    This activity introduces you to the course outline and learning outcomes, as well as information on navigating the course on the FutureLearn platform.

  • Defining Data

    Statistics is fundamentally concerned with collecting, organizing, and interpreting data. In this activity, we will be defining what data is and what are its types.

  • Histogram and Skewness

    In this activity, we will be discussing one of the ways how to represent data in statistics which is Histogram as well its Skewness.

  • Descriptive Statistics with Analysis ToolPak

    In this activity, we will be exploring descriptive statistics and also how to maximize Microsoft Excel Analysis ToolPak for statistics.

  • What is a Boxplot?

    In statistics, another way to visualize data is by using Boxplot. For this activity, we will identify and visualize data using Boxplots.

  • Categorical Data, PivotTables, and PivotCharts

    In this activity, we will dive into how we can categorize data by the scale of measurements. This helps us describe what the values mean when we visualize our statistics.

  • Summarizing Heirarchal Data

    In the activity, we will be discussing hierarchal data. Hierarchal data is a way to organize data with multiple one-to-many relationships.

  • 80-20 Rule and Pareto Charts

    You might be familiar with the 80-20 rule or the Pareto principle. In this activity, you'll find out how the Pareto principle is intertwined with statistics.

  • Week 1 Test

    This test will gauge how much have learned for this week.

• Week 2

  Basic Probability

  • Introduction to Probability

    In this activity, we will be introducing you to concepts related to basic probability. Probability is an estimation that some of that will occur in a random experiment.

  • Law of Complements

    In this activity, we will introduce the Law of Complements and how it simplifies the calculation of probabilities of an event.

  • Mutually Exclusive Events and Finding Prob (A or B)

    In this activity, we will be introducing the concept of Mutually Exclusive Events. These are events that do not happen at the same time. Like a coin toss!

  • Conditional Probability

    The probability that event A occurs given that event B has occurred is called a conditional probability. In this activity, we will be exploring Conditional Probabilities.

  • Law of Total Probability and Bayes Rule

    In this activity, we will discuss the Law of Total Probability. It is a powerful tool that relates marginal probabilities to conditional probabilities.

  • Basic Probability Review

    In this activity, we will be giving five probability problems to help you review the topics discussed for this week.

  • Week 2 Test

    This test will gauge how much have learned for this week.

• Week 3

  Random Variables

  • Random Variable Definitions

    Random Variables help you represent values determined by a chance event. In this activity, we will be introducing Random Variables.

  • Mean, Variance, and Standard Deviation of a Random Variable

    In this activity, we will be discussing Mean, Variance and Standard Deviation of Random Variable.

  • Mean, Variance, and Standard Deviation for Sum of Random Variables

    In this activity , we will discuss the Mean, Variance, and Standard Deviation for the Sum of Random Variable.

  • Binomial Random Variable

    A binomial random variable is a specific type of discrete random variable that counts how often a particular event occurs in a fixed number of trials.

  • Poisson Random Variable

    A Poisson random variable is a discrete random variable that counts the number of times a certain event occurs in a specific interval.

  • Normal Random Variable

    Normal random variables are the bell-shaped distribution of many continuous random variables that can take on values across a wide, infinite range and varies randomly of continuous variables like height, weight, or temperature.

  • Central Limit Theorem

    In a selection of random variables grouped together, Central Limit Theorem states that these variables will most likely be distributed normally.

  • Z Scores

    A z-score or standard score, indicates how many standard deviations something is from the mean.

  • Week 3 Test

    This test will gauge how much have learned for this week.

• Week 4

  Sampling and Confidence Intervals

  • Populations and Samples

    In the activity, we will discuss population and samples. A population includes all elements, a sample consists of a subset of the observations from the population.

  • Point Estimation of a Population Mean and Proportion

    Sample statistics are point estimates of population parameters to find the unknown mean of a population we calculate X-bar. In this activity, we will discuss Point Estimation of a Population Mean and Proportion.

  • The Standard Normal

    The standard normal distribution is a special case of the normal distribution. It is a distribution that occurs when a normal random variable has a mean of zero and a standard deviation of one.

  • Confidence Interval Estimation

    In this activity, we will explore Confidence Interval Estimation. Statisticians use confidence intervals to describe the amount of uncertainty associated with a sample estimate of a population parameter.

  • Sample Size Determination

    How many responses do you really need? This simple question is a never-ending quandary for researchers. In this activity, we will discuss determining the Sample Size you need for statistics.

  • The Finite Correction Factor

    Finite Correction Factor is applied to formulas that are used to compute for standard error. In this activity, we will discuss the Finite Correction Factor.

  • Additional Reading

    Additional Reading for Sampling and Confidence Interval

  • Week 4 Quiz

    This quiz will gauge how much have learned for this week.

• Week 5

  Hypothesis Testing

  • Defining Hypotheses

    Statisticians follow a formal process to determine whether the result of their analysis is meaningful, often referred to as statistically significant. This process is called hypotheses testing.

  • Type I and Type II Error

    There are two types of errors that can occur during hypothesis testing. In this activity, we will discuss both of these errors.

  • One Sample Z-Test

    In this activity, we will discuss One Sample Z-Test.

  • One Sample T-Test

    In this activity, we will discuss One Sample T-Test.

  • Single Sample Test for Population Proportion

    In this activity, we shall discuss the Single Sample Test for Population Proportion.

  • Testing Equality of Variances

    In this activity, we will discuss Testing Equality of Variances.

  • Testing the Difference Between Two Population Means

    In this activity, we shall discuss testing the difference between two population means.

  • Chi-Squared Test for Independence

    In this activity, we will discuss Chi-Squared Test for Independence.

  • Additional Reading

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  • Week 5 Quiz

    This quiz will gauge how much have learned for this week.

  • Wrapping Up the Course

    As we wrap up the course, we will have a final exam for the Analysis and Statistics using Microsoft Excel Course. You may review the weekly quizzes to help you with this exam.