Learning Objectives
After completing this module, you should be able to:
1. State the two main types of data and why it’s important to differentiate between types.
2. Describe the difference between quantitative and qualitative data.
3. State the subtypes of quantitative data and examples of each.
4. State the subtypes of qualitative data and examples of each.
5. Describe the difference between a population and a sample.
6. Define a descriptive statistic.
7. Identify and define two statistics for central tendency and the impact of outliers on them.
8. Calculate an average, median, and weighted average.
9. Define variability.
10. Identify and define two statistics for variability and the impact of outliers on them.
11. Identify and define the degrees of freedom in a variance calculation.
12. Define the range as a measure of variability.
13. Calculate the range for a sample of data.
14. Define the interquartile range (IQR) as a measure of variability.
15. Discuss the impact of outliers on the range and IQR.
16. Define a percentile.
17. Define a quartile.
18. Define Q1, Q2, and Q3.
19. Determine a particular percentile for a given sample of data.
20. Determine Q1, Q2, and Q3 for a given sample of data.
21. State the various steps and elements in the process of statistical inference
22. Define simple random sampling and the type of population it is suited for.
23. Describe how and why stratified random sampling is conducted
24. Determine if a particular sample is biased.
25. Explain why statistics are random variables.
26. Define what a sampling distribution is.
27. State a condition under which the sampling distribution of the mean will be normal. Explain why the sampling distribution is normal under this condition
28. Calculate a standard normal variable (Z) for any normal random variable.
29. Describe what it means for a normal random variable to be standard/standardized.
30. State the relationship between Z and the number of standard deviations.
31. Discuss why normal random variables are standardized.
Videos
Title: V1 – Descriptive Statistics (Data Types)
Summary: This video covers quantitative vs. qualitative data/variables and subtypes of each of them.
Learning Objectives: 1) State the two main types of data and why it’s important to differentiate between types. 2) Describe the difference between quantitative and qualitative data. 3) State the subtypes of quantitative data and examples of each. 4) State the subtypes of qualitative data and examples of each. 5) Describe the difference between a population and a sample.  Â
Transcript: Read the transcript
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Title: V2 – Descriptive Statistics (Central Tendency & Variability)
Summary:Â This video covers a population vs. a sample, central tendency and variability, and descriptive statistics for central tendency and variability, including the average, median, weighted average, variance, and standard deviation.
Learning Objectives: 5) Describe the difference between a population and a sample. 6) Define a descriptive statistic. 7) Identify and define two statistics for central tendency and the impact of outliers on them. 8) Calculate an average, median, and weighted average. 9) Define variability. 10) Identify and define two statistics for variability and the impact of outliers on them. 11) Identify and define the degrees of freedom in a variance calculation.
Transcript: Read the transcript
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Non-Annotated Slides: See the slides
Title: V3 – Descriptive Statistics (Range, IQR, & Percentiles)
Summary: This video covers additional descriptive statistics for variability (i.e., range and Interquartile Range (IQR)) as well as percentiles, including quartiles.
Learning Objectives: 12) Define the range as a measure of variability. 13) Calculate the range for a sample of data. 14) Define the interquartile range (IQR) as a measure of variability. 15) Discuss the impact of outliers on the range and IQR. 16) Define a percentile. 17) Define a quartile. 18) Define Q1, Q2, and Q3. 19) Determine a particular percentile for a given sample of data.
20) Determine Q1, Q2, and Q3 for a given sample of data.
Transcript: Read the transcript
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Title: V4 – Descriptive Statistics (Symmetry, Skew, Kurtosis)
Summary: This video covers the notion of a data distribution, symmetric versus skewed distributions, and descriptive statistics for distributional shape, including skewness and kurtosis.
Learning Objectives: 1) Describe what a data distribution is. 2) Define and describe a symmetric data distribution. 3) Define what a skewed distribution is. 4) Describe both a right and left skewed distribution. 5) Identify two statistics for distribution shape. 6) Determine whether a distribution is symmetric, fairly symmetric, moderately skewed, or highly skewed. 7) Describe the difference between a distribution having a positive kurtosis versus a distribution having a negative kurtosis.
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Title: V5 – Descriptive Statistics (Graphical Displays)
Summary: This video covers graphical displays of data that are useful when summarizing data as part of inferential statistics, including a histogram, bar chart, stem and leaf plot, and cumulative frequency graph.
Learning Objectives: 8) State some of the graphical methods that are used to summarize data. 9) Describe a histogram and the type of data it summarizes. 10) Describe a bar chart and the type of data it summarizes. 11) Describe a Stem and Leaf Plot and how it is constructed. 12) Describe a Cumulative Frequency Graph and how it is constructed.