BAR: FS Analysis : Using Outputs from Data Analytic Techniques Flashcards
Sorting
Arranging data in ascending or descending order to identify outliers in the dataset.
Data visualizations
Graphical representations used to depict trends and relationships in financial data.
Regression analysis
Statistical method used to estimate the relationship between variables.
Multiple R
Correlation coefficient that measures the direction and strength of the linear relationship between two variables; ranges from -1 (perfectly negative correlation) to 1 (perfectly positive correlation).
R squared
Measures the proportion of the change in the dependent variable explained by changes in the independent variable; ranges from 0 to 1, with higher values indicating better regression explanation.
Adjusted R
Adjusts for the number of terms in a model and should be used when there are multiple independent variables.
Standard error
Indicates the precision of the regression coefficient; reflects the spread of the dependent variable around the mean.
Observations
Number of data points in the sample.
ANOVA
Analysis of variance; decomposes the sum of squares used in the analysis into individual components.
Regression coefficients
Specific information about the inputs of the regression analysis.
T-statistic
A higher value indicates that the results are significantly different from the average results, and vice versa.
P-value
A statistical measure that indicates the probability of the results occurring by chance; a higher value suggests results are likely by chance.
Linear regression equation
Dependent variable (Y) = [slope x independent variable (X)] + intercept
Y=mx+b
Comparative and Trend Analysis
A method of analyzing financial data to show balances, changes, and trends over time.
Horizontal analysis
Measures the dollar and percentage change over a period of time; useful in identifying trends and significant changes in the business.
