2_Introduction to Descriptive Statistics
Page 1: Introduction & Descriptive Statistics
Page 2: Learning Outcome
Understand about statistics
Identify types of statistics
Explain about descriptive statistics
Use SPSS to measure descriptive statistics
Page 3: Introduction to Statistics
Data examples from various months (not specific)
Various values mentioned but not clearly defined
Page 4: What Is Statistics?
Analyzing data to make inferences and draw conclusions
Key processes include:
Collecting
Describing
Interpreting
Page 5: Who Uses Statistics?
Widely used by:
Marketing
Accounting
Quality control
Consumers
Professional sports
Hospital administrators
Educators
Politicians
Researchers
Students
Page 6: Basic Terms Used in Statistics
Population: Set of individuals or events analyzed
Sample: Subset of a population representing the whole
Variable: Characteristic of interest for each element
Data: Values collected for the variable from elements of the sample
Page 7: Scale of Measurement
Categorical: Describes or categorizes
Nominal: No order (e.g., gender)
Ordinal: Ordered categories
Numerical: Quantifies elements
Interval: Arbitrary zero, can add or subtract
Ratio: Non-arbitrary zero, can multiply/divide
Page 8: Nominal Variable
Classifies characteristics into categories
Data categories are mutually exclusive and not ranked
Examples:
Gender
Dichotomous variables (e.g., patient status)
Page 9: Ordinal Variable
Incorporates ranking
Differences/distances between ranks are not quantifiable
Examples:
Socioeconomic status
Attitude scales
Page 10: Interval Variable
Quantitative scale (discrete/continuous)
Discrete: countable values (gaps)
Continuous: uncountable values (decimal values)
Zero point is arbitrary (e.g., temperature)
Page 11: Ratio Variable
Similar to interval but with a non-arbitrary zero
Allows multiplication/division
Examples:
Temperature in Kelvin
Blood pressure readings
Page 12: Study Variable
Dependent Variable: Outcome being studied
Independent Variable: Hypothesized to influence the outcome
Examples provided on sports and job satisfaction
Page 13: Classification of Statistics
Descriptive Statistics:
Describes data from a study
Includes tables, graphs, etc.
Inferential Statistics:
Draws broader conclusions from results
Generalizes population characteristics from sample data
Page 14: Comparison of Statistics
Key concepts introduced include:
Population
Sample
Sampling technique
Descriptive and inferential statistics
Page 15: Descriptive Statistics
Overview of the topic
Page 16: Types of Categorical Variables
Frequency analysis using bar charts
Numerical variables: central tendency and variability measures
Page 17: Frequency Presentation
Frequency distribution visualizations (bar charts)
Page 18: Frequency Table
Displays values paired with frequency
May include cumulative and relative frequency
Page 19: Generating Frequency Table Using JAMOVI
Instructions for using JAMOVI for data analysis presented
Page 20: Example Frequency Table (JAMOVI Output)
Displays frequency of gender
Page 21: APA 7th Style Frequency Table
Structure of presenting results in APA format
Page 22: Categorical Variable Bar Graph
Graphical representation of frequencies for categorical data
Page 23: Bar Chart Specifications
Axes representations and bar detailing
Page 24: Types of Bar Charts
Different formats including stacked and clustered bars
Page 25: Generating Bar Graphs Using JAMOVI
Steps to create bar graphs through JAMOVI
Page 26: Gender Frequency Results
Summary of gender data in graph form
Page 27: Excellence Graph
Importance of clear data representation
Page 28: Numerical Variable Measures
Central tendency, variability, and graphical presentations defined
Page 29: Mean Calculation
Calculation of sample average with sensitivity explained
Page 30: Example Mean Calculation
Practical example calculating the mean from blood pressure values
Page 31: Median Definition
Explanation on finding the middle value of ordered data sets
Page 32: Example Odd Median Calculation
Provides an example with arranged basketball scores
Page 33: Example Even Median Calculation
Explanation through cigarette nicotine content data
Page 34: Mode Definition
Description of the mode and its significance in statistics
Page 35: Comparing Mean, Median, and Mode
Summary of respective scenarios for each measure's utility
Page 36: Calculate Mean, Median, Mode
Hands-on exercise with sample data provided
Page 37: Answers to Hands-on Exercise
Mean, median, mode calculations based on provided data
Page 38: Variance Definition
Description of variance in relation to observation spread
Page 39: Variance Example
Illustrated example with office equipment inventory
Page 40: Variance Example Calculation
Elaborate calculation providing insights on variance determination
Page 41: Standard Deviation Background
Definition and importance of standard deviation
Page 42: Standard Deviation Example
Calculation and illustration of scores given by judges
Page 43: Further Standard Deviation Calculation
Another example to solidify understanding
Page 44: Hands-On Exercise for Variance and Standard Deviation
Hands-on exercise prompts provided
Page 45: Hand-On Exercise Solutions
Provides calculations for variance and standard deviation
Page 46: Summary of Variance and Standard Deviation Computations
Key takeaway values from examples presented
Page 47: Range Explanation
Basics of calculating range for quick variability estimations
Page 48: Outlier Definition
Description of outliers in a dataset and their significance
Page 49: School Students Data
Sample data depicting student distribution in schools
Page 50: Data Mean and Standard Deviation Analysis
Analysis variations between different datasets
Page 51: Conclusion on Outlier Influence
Impact of outliers on mean and standard deviations
Page 52: Quartiles Overview
Introduction to quartiles and their significance in datasets
Page 53: Inter Quartile Range (IQR)
Definition and calculation of IQR for data dispersion assessment
Page 54: Example for Quartiles and IQR Calculation
Mathematics exercise involving student scores
Page 55: Calculation of Quartiles and IQR
Worked out example detailing quartiles with interpretations
Page 56: Further IQR Exploration
Continued emphasis on interpreting IQR findings
Page 57: Generating Summary Data Using JAMOVI
Overview of how JAMOVI handles summary data
Page 58: JAMOVI Output Summary
Specific statistics showcased in JAMOVI outputs
Page 59: APA 7th Style Results Reporting
Proper structuring of statistical reporting in APA format
Page 60: Standard Score (Z-score) Overview
Definition and utility of z-scores in statistics
Page 61: Z-score Example and Comparisons
Examples comparing two scores with respective z-scores
Page 62: Standard Normal Distribution Properties
Characteristics of normal distribution explained
Page 63: Area Under the Standard Normal Curve
Example of calculating area using z-scores
Page 64: Utility of Graphs
Importance of graphical data representation
Page 65: Histogram Explanation
Details concerning histograms and their properties
Page 66: Generating Histograms in JAMOVI
Steps to visualize data distributions via histograms
Page 67: Stem-and-Leaf Plot Explanation
Introduction to stem-and-leaf as a data visualization tool
Page 68: Stem-and-Leaf Example
Practical example constructing a stem-and-leaf display
Page 69: Displaying Stem-and-Leaf Data
Further detailing of the example constructed
Page 70: Additional Hands-On Exercise
Further practice with provided numerical values
Page 71: Additional Data Output Examining
Output analysis provided for hands-on tasks
Page 72: Box Plot Explanation
Box plots as a graphical display based on percentiles
Page 73: Box Plot Components
Details on components and interpretation of box plots
Page 74: Outlier Detection in Box Plots
Recognizing outliers within box plot data visualizations
Page 75: Scatter Plot Overview
Details on plotting bivariate data
Page 76: Scatter Plot Interpretation
Interpretation of data spread in scatter plots
Page 77: Closing Remarks
Thank you message for the presentation