Stats

Question 1

What are the 2 names of the Tukey test for outliers

Answer 1

Tukey’s Range Test
Tukey’s Honestly Significant Difference (HSD)

Question 2

What are the 5 steps in the Tukey test

Answer 2

1 - Conduct ANOVA
2- Calculate the Critical Value
3 - Compute the Honestly Significant Difference (HSD)
4 - Compare Mean Differences
5 - Interpretation

Question 3

What does ANOVA stand for

Answer 3

Analysis of variance test - a method used to compare means across multiple groups or treatments.

Question 4

What is an F-statistic

Answer 4

is a ratio of two variances: the variance between group means and the variance within groups. It quantifies the extent to which the variation among group means is greater than the variation within individual groups.

Question 5

What is a p-value

Answer 5

indicates the significance of the observed differences

Question 6

What are the 7 outputs of the Tukey test

Answer 6

1- Comparison Matrix
2 - Mean Differences
3- Tukey’s HSD
4 - Confidence Intervals
5 - Adjusted p-values
6 - Significance Indicators
7 -Summary Statistics

Question 7

Define Tukey’s HSD

Answer 7

The Honestly Significant Difference (HSD) value calculated based on the critical value and the standard error of the means. This value is used to determine whether the observed differences between group means are statistically significant.

Question 8

What Python library supports Tukey

Answer 8

statsmodels.stats.multicomp

Question 9

explain Z-Scores

Answer 9

indicates how many standard deviations a data point is from the mean of the dataset.

Question 10

What are the 4 steps to calculating a Z-Score

Answer 10

1 - Calculate the Mean (μ): Find the average of the dataset.
2 - Calculate the Standard Deviation (σ)
3 - Subtract the Mean from the Data Point
4 - Divide by the Standard Deviation.

Question 11

what does a Z-score of 0 indicate

Answer 11

Data is at the mean

Question 12

what does a Z-score of +1indicate

Answer 12

Z-score of +1 (or -1) indicates that the data point is 1 standard deviation above (or below) the mean

Question 13

What is R Squared

Answer 13

represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s) in a regression model. In simpler terms, it indicates how well the independent variable(s) explain the variability of the dependent variable.

Question 14

What is the difference between R Squared and correlation

Answer 14

Correlation measures the strength and direction of the linear relationship between two continuous variables.

R-squared represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

In summary, correlation tells you how closely related two variables are in a linear sense, while R-squared tells you how much of the variability in the dependent variable is explained by the independent variable(s).

Question 15

Name 8 methods that a data analyst may use to test equity signals

Answer 15

1 Correlation coefficient
2 Scatter plots
3 Covariance
4 Cross-correlation analysis
5 Granger causality test
6 Regression analysis
7 Correlation heatmaps
8 Principal Component Analysis (PCA)

Question 16

What is the Correlation coefficient

Answer 16

Pearson correlation coefficient is widely used to measure the linear relationship between two signals. Analysts calculate correlation to understand how closely the movements of two signals are related.

Question 17

What are scatter plots

Answer 17

Visual representation of the data points from both signals on a scatter plot helps analysts to visually inspect the relationship between the signals. Patterns such as clustering around a line or curve can indicate a relationship.

Question 18

What is Covariance

Answer 18

Covariance measures how much two signals vary together. Positive covariance indicates that the signals move in the same direction, while negative covariance indicates they move in opposite directions.

Question 19

what is Cross-correlation analysis

Answer 19

This involves analyzing the correlation between the signals at different time lags. It helps in understanding whether one signal leads or lags the other and the strength of this relationship at different time intervals.

Question 20

what is the Granger causality test

Answer 20

This statistical hypothesis test is used to determine whether one time series is useful in forecasting another. It helps analysts understand if one signal is a leading indicator of the other.

Question 21

what is Regression analysis

Answer 21

regression analysis quantifies the relationship between the two signals. They may use simple linear regression or more complex models to estimate how changes in one signal affect the other.

Question 22

what are Correlation heatmaps

Answer 22

These are visual representations of correlation coefficients between multiple signals. Heatmaps help in identifying patterns of correlation among multiple signals simultaneously.

Question 23

What is Principal Component Analysis (PCA)

Answer 23

PCA is a dimensionality reduction technique that can be used to identify underlying patterns and relationships between multiple signals. It helps in understanding the dominant sources of variability in the data.