Course curriculum

    1. 1000 Introduction

    2. 1120 Getting a Picture – Data & Distribution

    1. OneNote Recourse

    2. 1011 Hamlet, Harry Potter, & Statistics

    3. 1014 Where to Find Data to Practice With

    4. 1021 Wages Data Box Plot or Box & Whiskers

    5. 1025 Wages Data Box Plot or Box Whiskers vs Histogram

    6. 1031 Histogram vs. Bar Chart29e63df.autosave

    7. 1041 Histograms with Different Bucket Sizes

    8. 1051 Misleading Histogram

    9. 1056 Histograms with Car Related Data

    10. 1061 Scatter Plots with Car Related Data

    11. 1066 Histograms and Scatter Plots with Population Data

    12. 1070 Histogram Examples

    1. 1010 Hamlet, Harry Potter, & Statistics

    2. 1015 Generating Practice Data in Excel

    3. 1016 Sort Comma & Space Delimited Data into a Column

    4. 1017 Sort Data Randomly

    5. 1020 Wages Data Box Plot or Box & Whiskers

    6. 1022 Wages Data Box Plot or Box & Whiskers Analysis

    7. 1024 Wages Data Box Plot or Box Whiskers vs Histogram

    8. 1030 Histogram vs. Bar Chart

    9. 1040 Histograms with Different Bucket Sizes

    10. 1050 Misleading Histogram

    11. 1055 Histograms with Car Related Data

    12. 1060 Scatter Plots with Car Related Data

    13. 1065 Histograms and Scatter Plots with Population Data

    1. 1306 Statistical Inference - Questions of How Close & How Confident

    1. OneNote Recourse

    2. 1311 Height Statistical Inference Data Practice Problem

    3. 1316 Coin Flip Statistics Example

    4. 1326 Deck of Cards, Statistics, & Excel

    5. 1336 Election Poll Statistics Example

    6. 1346 Combining Two Histograms on One Chart

    7. 1361 Calories Data Statistics Sample Example

    1. 1310 Height Statistical Inference Data - Excel Practice Problem

    2. 1315 Coin Flip Statistics Example in Excel

    3. 1319 Coin Flip Statistics Example in Excel Part 2

    4. 1325 Deck of Cards, Statistics, & Excel

    5. 1329 Deck of Cards, Statistics, & Excel Part 2

    6. 1335 Election Poll Statistics Example

    7. 1339 Election Poll Statistics Example Part 2

    8. 1345 Combining Two Histograms on One Chart

    9. 1349 Combining Two Histograms on One Chart Part 2

    10. 1353 Combining Two Histograms on One Chart Part 3

    11. 1360 Calories Data Statistics Sample Example

About this course

  • $49.99
  • 46 lessons
  • 16 hours of video content

Description

This course delves into the fundamental concepts of statistics with a unique focus on Microsoft Excel as a tool for data analysis. It caters to a diverse group of students from fields like business, economics, social sciences, and natural sciences, who seek to comprehend and analyze data effectively.

Course Content

The journey begins with an exploration of how to understand and interpret data. Students will learn to navigate through extensive data tables, transforming raw data into meaningful information. The course emphasizes the pivotal role of visual representation in statistics. Through Microsoft Excel, students will master the art of creating graphs that unveil patterns, relationships, and critical features within the data, thereby understanding its true essence.

A significant portion of the course is dedicated to characterizing distributions. Here, students will identify the shape, center, and spread of data distributions using Excel's analytical tools. This knowledge is expanded in the section on organizing and summarizing data, where efficient data grouping, ordering techniques, and summarization methods such as means, medians, quartiles, histograms, and box plots are covered.

The course also addresses examining relationships between variables. It includes practical examples like analyzing the correlation between SAT scores and GPAs, employing scatter plots for visualization. This section provides a robust understanding of how different variables interact and influence each other.

A critical component of the course is statistical inference, which revolves around the concepts of how close and how confident we can be about our data interpretations. This part includes real-world applications like election polling and medical trials, highlighting the practicality of statistical inference. Students will grasp the importance of principles such as randomness in sampling, understanding estimates, confidence intervals, and the foundational role of probability theory.

Learning Outcomes and Methodology

By the end of the course, students will have developed the ability to interpret and analyze data using Excel proficiently. They will be well-versed in visualizing data, understanding its distributions, and applying statistical concepts to real-life scenarios. The course combines theoretical knowledge with hands-on Excel exercises, ensuring an immersive learning experience. Regular assignments, case studies, and project work will enhance the practical application of the concepts learned.