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Questions and Answers
What is the correct command to load the necessary library to check normality in RStudio?
What is the correct command to load the necessary library to check normality in RStudio?
- `library(dplyr)`
- `library(ggplot2)`
- `library(stats)`
- `library(psych)` (correct)
What is the correct command to load the data from a file into RStudio, assuming the file is located in the same folder as the R script?
What is the correct command to load the data from a file into RStudio, assuming the file is located in the same folder as the R script?
- `data = read.csv("Data.csv")` (correct)
- `data <- load("Data.csv")`
- `read.csv("Data.csv")`
- `data <- read.csv("Data")`
What is the purpose of setting the level of significance (alpha = 0.05)?
What is the purpose of setting the level of significance (alpha = 0.05)?
- To specify the degrees of freedom for the test.
- To determine the probability of a Type I error. (correct)
- To specify the number of observations required for the test.
- To calculate the p-value of the test.
What is the significance of checking the data requirements for an independent t-test?
What is the significance of checking the data requirements for an independent t-test?
What is the specific purpose of an independent t-test, based on the provided context?
What is the specific purpose of an independent t-test, based on the provided context?
Flashcards
Independent t-test
Independent t-test
A statistical test to compare the means of two independent groups.
Set working directory
Set working directory
Defines the folder where files are saved or loaded in R.
Activate library
Activate library
Load a package in R to access its functions.
Level of significance (alpha)
Level of significance (alpha)
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Data requirement check
Data requirement check
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Study Notes
Independent t-test
- Used to compare the means of two independent groups
- Data must meet specific assumptions for the validity of the test
- To determine if there is a statistically significant difference in means between two groups
Normality
- The data must be normally distributed within each group
- Skewness and kurtosis values should be within ±2 to indicate normality.
- Checking for normality can be done via descriptive statistics, e.g., describeBy( Data$Score, Data$Group) in the code
Levene's test
- Tests for equality of variances between two groups
- A p-value greater than 0.05 indicates that the variances are equal
- If the p-value is less than 0.05, the variances are not equal, and an alternative t-test (Welch's t-test) or a different analysis may be needed.
t-test
- Compares means of 2 independent groups when variances are equal.
- Compares means of 2 independent groups when variances are unequal.
- Determines if there is a significant difference in group means.
Assumptions
- Independence of observations, meaning each participant in one group is independent of others
- Independent variable is categorical with two groups and the dependent variable is continuous
Data requirements
- Independent variable should be either numerical or categorical in nature
- Dependent variable must be a continuous numerical variable.
Analysis
- The t-test is used to compare between two independent groups
Visual representation in the analysis
- Box plot, to present a visual summary of the distribution of scores for each group visually.
- The plot of score distributions using boxplot helps to visually evaluate the data to confirm or reject any assumptions.
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