Podcast
Questions and Answers
Match the correlation coefficient types with their corresponding descriptions:
Match the correlation coefficient types with their corresponding descriptions:
Pearson's r = Measures the strength of the linear relationship between two continuous variables Spearman's r = Measures the strength of the monotonic relationship between two ranked variables Parametric Correlation Coefficient = Assumes that the data follows a specific distribution, such as a normal distribution Nonparametric Correlation Coefficient = Does not make assumptions about the distribution of the data
Match the following fundamental principles in model building with their corresponding explanations:
Match the following fundamental principles in model building with their corresponding explanations:
The principle of parsimony = Simple models are preferred to complex models, especially in forecasting The shrinkage principle = Imposing restrictions on estimated parameters or forecasts often improves model performance The KISS principle = Keep it Sophistically Simple None of the above = Not applicable
Match the data types for statistical analysis with their corresponding definitions:
Match the data types for statistical analysis with their corresponding definitions:
Cross-section data = Data collected at a specific point in time for different entities Time-series data = Data collected over time for the same entity Panel data = Data collected over time for multiple entities None of the above = Not applicable
Match the following assumptions of linear regression with their corresponding descriptions:
Match the following assumptions of linear regression with their corresponding descriptions:
Match the following scenarios with the appropriate statistical analysis:
Match the following scenarios with the appropriate statistical analysis:
Match the components of regression analysis with their corresponding descriptions:
Match the components of regression analysis with their corresponding descriptions:
Match the statistical tests with their descriptions:
Match the statistical tests with their descriptions:
Match the assumptions with the corresponding statistical test:
Match the assumptions with the corresponding statistical test:
Match each analysis type with its purpose:
Match each analysis type with its purpose:
Match the statistical terms with their definitions:
Match the statistical terms with their definitions:
Match the test type with an example scenario:
Match the test type with an example scenario:
Match the sampling methods with their descriptions:
Match the sampling methods with their descriptions:
Match each statistical term with its requirements:
Match each statistical term with its requirements:
Match the types of errors in hypothesis testing with their definitions:
Match the types of errors in hypothesis testing with their definitions:
Match the steps in the hypothesis testing procedure with their details:
Match the steps in the hypothesis testing procedure with their details:
Match the statistical concepts with their main focus:
Match the statistical concepts with their main focus:
Match the statistician with their contributions:
Match the statistician with their contributions:
Match the assumptions of the one-sample t-test with their descriptions:
Match the assumptions of the one-sample t-test with their descriptions:
Match the types of population comparisons with their descriptions:
Match the types of population comparisons with their descriptions:
Match the critical concepts in hypothesis testing with their meanings:
Match the critical concepts in hypothesis testing with their meanings:
Match the sampling methods with their unique characteristics:
Match the sampling methods with their unique characteristics:
Match the terms with their roles in hypothesis testing:
Match the terms with their roles in hypothesis testing:
Match the enzyme with its effect on daily milk production:
Match the enzyme with its effect on daily milk production:
Match the type of study with its definition:
Match the type of study with its definition:
Match the data types used in the Chi-Square Test of Independence:
Match the data types used in the Chi-Square Test of Independence:
Match the assumption with its description for the Chi-Square Test:
Match the assumption with its description for the Chi-Square Test:
Match the element of experimental design with its purpose:
Match the element of experimental design with its purpose:
Match the statistical method with the scenario it addresses:
Match the statistical method with the scenario it addresses:
Match the component of the experimental design with its definition:
Match the component of the experimental design with its definition:
Match the component of the research question with its explanation:
Match the component of the research question with its explanation:
Match the following measures of central tendency with their correct descriptions:
Match the following measures of central tendency with their correct descriptions:
Match the following measures of variation with their definitions:
Match the following measures of variation with their definitions:
Match the following statistical methods with their purposes:
Match the following statistical methods with their purposes:
Match the following methods of data presentation with their characteristics:
Match the following methods of data presentation with their characteristics:
Match the following data positions with their definitions:
Match the following data positions with their definitions:
Match the following types of data with their applicable measures:
Match the following types of data with their applicable measures:
Match the following terms with their meanings regarding outliers:
Match the following terms with their meanings regarding outliers:
Match the following statistical concepts with their examples:
Match the following statistical concepts with their examples:
Match the sampling techniques with their descriptions:
Match the sampling techniques with their descriptions:
Match the following experimental design principles with their descriptions:
Match the following experimental design principles with their descriptions:
Match the following experimental designs with their characteristics:
Match the following experimental designs with their characteristics:
Match the following experimental designs with their applications:
Match the following experimental designs with their applications:
Match the following statistical methods with their applications:
Match the following statistical methods with their applications:
Match the following programming languages with their applications in data analysis:
Match the following programming languages with their applications in data analysis:
Match the following Machine Learning concepts with their descriptions:
Match the following Machine Learning concepts with their descriptions:
Match the following statistical software packages with their primary applications:
Match the following statistical software packages with their primary applications:
Match the following statistical concepts with their definitions:
Match the following statistical concepts with their definitions:
Flashcards
Convenience Sampling
Convenience Sampling
A sampling method that selects participants based on ease of access and proximity to the researcher. It's often used when time and resources are limited.
Purposive Sampling
Purposive Sampling
A sampling method where participants are intentionally chosen based on the researcher's judgment and the study's objectives.
Snowball Sampling
Snowball Sampling
A sampling method where initial participants are asked to refer other potential participants, creating a chain of referrals.
Quota Sampling
Quota Sampling
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Type I Error
Type I Error
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Type II Error
Type II Error
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Hypothesis Testing
Hypothesis Testing
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Student's One-Sample t-Test
Student's One-Sample t-Test
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What is Statistics?
What is Statistics?
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Mean
Mean
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Median
Median
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Mode
Mode
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Range
Range
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Standard Deviation
Standard Deviation
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Coefficient of Variation
Coefficient of Variation
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z-score
z-score
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Percentile
Percentile
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Quartile
Quartile
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Parametric Correlation Coefficient
Parametric Correlation Coefficient
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Pearson's r
Pearson's r
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Nonparametric Correlation Coefficient
Nonparametric Correlation Coefficient
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Spearman's r
Spearman's r
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Regression Analysis
Regression Analysis
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The principle of parsimony
The principle of parsimony
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The shrinkage principle
The shrinkage principle
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The KISS principle
The KISS principle
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Mann-Whitney U Test
Mann-Whitney U Test
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Independent T-test
Independent T-test
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Dependent T-test
Dependent T-test
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ANOVA (Analysis of Variance)
ANOVA (Analysis of Variance)
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Correlation Analysis
Correlation Analysis
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Tukey Test
Tukey Test
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F-test
F-test
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Welch's t-test
Welch's t-test
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Chi-Square Test of Independence
Chi-Square Test of Independence
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Experimental Study
Experimental Study
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Observational Study
Observational Study
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Design of Experiments
Design of Experiments
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Replication
Replication
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Randomization
Randomization
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Blocking
Blocking
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Completely Randomized Design (CRD)
Completely Randomized Design (CRD)
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Randomized Complete Block Design (RCBD)
Randomized Complete Block Design (RCBD)
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Latin Square (LS) Design
Latin Square (LS) Design
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Split-Plot (SP) Design
Split-Plot (SP) Design
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Balanced Incomplete Block Design (BIBD)
Balanced Incomplete Block Design (BIBD)
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Study Notes
2024 Regional Technology & Innovation Week Davao Region
- Basic Research Statistics: PAKIGLAMBIGIT Project
- Presenter: Ronald A. Gica, MSc, RSTW
- Date: November 12, 2024
- Location: The Ritz Hotel, Davao City
Statistical Methods
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Definition: A collection of systematic techniques and procedures employed to convert raw data into meaningful and actionable information that can inform decisions made by stakeholders, businesses, and researchers alike.
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Functions:
- Collecting Data: Involves the systematic gathering of information from various sources to ensure a comprehensive dataset, which may include surveys, experiments, and observational studies.
- Organizing Data: Refers to the arrangement of collected data into a systematic format, such as tables or databases, which facilitates easier access and analysis.
- Summarizing Data: The process of condensing large datasets into simpler forms, often utilizing descriptive statistics such as means, medians, and modes to provide a clearer understanding of trends within the data.
- Presenting Data: Encompasses the visualization of data through charts, graphs, and infographics, making it easier for stakeholders to comprehend complex datasets and outcomes.
- Analyzing Data: Involves applying various mathematical and statistical techniques to uncover patterns and relationships within the data, which aids in drawing conclusions and predictions.
- Interpreting Data: The critical process of making sense of the analyzed data, allowing decision-makers to understand the implications of the findings, derive insights, and formulate strategic actions based on evidence.
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Definition: Collection of tools to convert raw data into useful information for decision-makers.
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Functions:
- Collecting data
- Organizing data
- Summarizing data
- Presenting data
- Analyzing data
- Interpreting data
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Types of Data: Quantitative data
Methods of Data Presentation
- Textual: Brief, concise descriptions of data in paragraph form.
- Tabular: Large datasets organized in rows and columns.
- Graphical: Pictorial representation of data to improve viewer comprehension.
Measures of Central Tendency
- Mean: The sum of the values divided by the total number of values. (Easily affected by extreme values)
- Median: The midpoint of the data array. (Unaffected by extreme values)
- Mode: The value(s) that occur most frequently.
Measures of Variation
- Range: The difference between the highest and lowest values. (Easily affected by extreme values)
- Interquartile Range: Range of the middle 50% of the observations
- Variance: Mean of the squared deviations from the mean
- Standard Deviation: The positive square root of variance. (Measure of spread about the mean)
- Coefficient of Variation: Variability of the dataset relative to its mean
Measures of Position
- z-score: Number of standard deviations a value falls above or below the mean.
- Percentile: Position in hundredths.
- Decile: Position in tenths.
- Quartile: Position in fourths.
- Outlier: Extreme data value differing significantly from other values.
Boxplots
- Graphical representation showing median, quartiles, and outliers.
Confidence Interval
- Point Estimate: Specific numerical value of a parameter estimate.
- Interval Estimate: An interval or range of values used to estimate the parameter.
- Confidence Level: The likelihood that the interval estimate contains the parameter value.
Sampling Techniques
- Probability Sampling:
- Random Sampling
- Systematic Random Sampling
- Stratified Random Sampling
- Cluster Random Sampling
- Non-probability Sampling:
- Convenience Sampling
- Purposive Sampling
- Snowball Sampling
- Quota Sampling
Simple Random Sampling (SRS):
- Assigning all possible samples an equal chance of being selected.
Systematic Random Sampling:
- Researchers selecting members of the population at regular intervals.
Stratified Random Sampling:
- Dividing population into mutually exclusive groups or strata and drawing a sample from each.
Cluster Random Sampling:
- Dividing population into sections or clusters and randomly selecting entire clusters.
Convenience Sampling:
- Selecting participants based on accessibility and proximity.
Purposive Sampling:
- Intentional selection of participants based on the researcher's judgment and study objectives.
Snowball Sampling:
- Researchers apply this method when subjects are difficult to trace
Quota Sampling:
- Selecting participants based on predetermined quotas or characteristics.
Comparing Populations
- Comparing one population
- Comparing two populations
- Comparing three or more populations
Hypothesis Testing Procedure
- State the null (H0) and alternative (Ha) hypotheses.
- Choose a level of significance and formulate the decision rule for rejecting or not rejecting H0.
- Critical-value approach.
- p-value approach.
- Compute the value of the test statistic.
- Make a decision.
- Make a conclusion.
Type I Error
- Rejecting a true null hypothesis.
Type II Error
- Accepting a false null hypothesis.
Student's One-Sample t-Test
- Comparing the mean of a sample to a hypothesized value.
Student's One-Sample t-Test, Wilcoxon Signed-Rank Test:
- Used when normality assumption is not met, to compare the median to a hypothesized median value
Student's Independent-Samples t-Test
- Comparing the means of two independent populations.
Mann-Whitney U Test:
- Nonparametric test to compare two independent samples
Analysis of Variance (ANOVA):
- Comparing the means of three or more independent populations.
- Uses the F-test.
Post-Hoc Analysis:
- Used for pairwise comparisons of means when ANOVA is significant.
- Tukey test
Correlation Analysis
- Measures the strength of the relationship between two variables.
- Determines if variables are related; the strength of their relationship, and the type of relationship
Regression Analysis
- Fits a model to observed data to quantify relationships between variables or predict new values.
- Determines which factors are most/least important
Assumptions of Linear Regression
- Zero mean of the error term.
- Homoscedasticity
- No serial correlation
- Non-stochastic explanatory variable
- Positive degrees of freedom
- No perfect multicollinearity
- Normality of the error term
Statistical Software
- X
- JASP
- SPSS
- R
- STATA.
Open Source Tools
- Hadoop
- NoSQL
Data Visualization Tools
- Tableau
- Power BI
Experiment Designs and Analysis
- Experimental study
- Observational Study
- Principles for Designing Experiments:
- Replication
- Randomization
- Blocking
- Common Experimental Designs:
- Completely Randomized Design (CRD)
- Randomized Complete Block Design (RCBD)
- Latin Square (LS) Design
- Split-Plot (SP) Design
- Balanced Incomplete Block Design (BIBD)
- Factorial Design
- Statistical Methods for Analyzing Experiments
- Analysis of variance (ANOVA)
- Multivariate analysis of variance (MANOVA)
- Kruskal-Wallis test
Python and SQL
- Programming languages commonly used in data analysis
- Versatility
- Efficiency
Machine learning algorithms
- Algorithms enable machines to learn from massive datasets
- Boosts predictive analytics accuracy
Big data analytics
- Processes vast quantities of information efficiently
- Fosters data scientist and intelligence professional synergies
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Description
Test your knowledge on various statistics concepts including correlation coefficients, regression analysis, and hypothesis testing. This quiz includes matching types of data and statistical tests to their definitions and scenarios. Perfect for those studying statistics or data analysis.