Variables example sentences

"Variables" Example Sentences

1. The scientist analyzed the data and considered all possible variables.
2. They controlled for outside variables in the experiment.
3. The researcher identified key demographic variables for the study.
4. Accounting for confounding variables is important in medical research.
5. The study examined the relationship between several independent and dependent variables.
6. The team measured 12 variables as part of the project.
7. The model includes both continuous and categorical variables.
8. Variables must be clearly defined in a research study.
9. String variables can contain text like names and locations.
10. Numeric variables store numbers like ages, weights and test scores.
11. Selecting relevant variables is critical for building an accurate model.
12. Variables should be expressed in appropriate units for comparability.
13. The formula has many variables that can be adjusted.
14. Statistical techniques help evaluate relationships between variables.
15. The macro defines several local variables for re-use within the code.
16. Global variables can be accessed from anywhere within a program.
17. Assigning values to variables helps simplify complex calculations.
18. The function takes multiple input variables and returns a single output.
19. Variable names should be meaningful and avoid spaces or special characters.
20. The machine learning algorithm trained on a large set of input variables.
21. The variables were normalized before inputting into the neural network.
22. Random variables describe possible outcomes of a stochastic process.
23. The model performed well with the given set of input variables.
24. Categorical variables needed to be encoded for the algorithm to work.
25. Missing values in variables had to be imputed before analysis.
26. Outlier values in variables were cleaned before building the model.
27. The program asks the user to enter several input variables.
28. Data needs preprocessing before extracting useful variables.
29. Label encoding converted categorical variables to numeric form.
30. One-hot encoding created dummy variables from factors and text.
31. The variable importance plot showed which had the greatest influence.
32. The number of variables determines the complexity of the problem.
33. The programming language supports various variable types.
34. Indexing allows accessing elements within variables.
35. Variables were defined at the top of the function for readability.
36. Variables should have meaningful names that match their purpose.
37. Careless typing can cause "typo variables" that do not work.
38. Constants are variables that do not change value after assignment.
39. Variables were frequently reassigned new values within the loop.
40. Objects behave similarly to structured variables in some languages.
41. Scope determines the visibility and lifetime of variables.
42. Variable shadowing happens when a new variable hides an older one.
43. Variables store data of different types like numbers, strings and arrays.
44. Variables can be reinitialized by assigning a new value.
45. List variables can store items of different types.
46. Tuple variables are immutable and cannot be changed after creation.
47. Dictionary variables store key-value pairs.
48. Color variables are useful for maintaining consistent brand colors.
49. Button variables make UI elements reusable across components.
50. Initializing variables to empty or default values reduces errors.
51. Null variables have no value assigned to them.
52. Environment variables store configuration settings outside of code.
53. Matching variable names between backend and frontend code is a best practice.
54. Longer variable names improve readability at the cost of more characters.
55. Using meaningful variable names is the most important programming style rule.
56. Variable standardization is useful for comparing metrics across data sets.
57. Statistical models need variables with similar scales for best performance.
58. Normalizing variables centers data and reduces the effect of outliers.
59. Standardized variables have a mean of 0 and standard deviation of 1.
60. Transforming variables improves model accuracy and interpretability.

Common Phases

1. Independent variables: Explanatory variables that are hypothesized to influence dependent variables.
2. Dependent variables: Variables that depend on the independent variables. They are the outcomes we seek to explain or predict.
3. Continuous variables: Variables that can take any value within a range.
4. Categorical variables: Variables that represent categories, not numeric values.
5. Random variables: Variables whose outcomes are uncertain.
6. Control variables: Variables that are held constant in an experiment to isolate the effect of other variables.
7. Input variables: Variables provided as input to an algorithm or model.
8. Output variables: Variables that are the result of calculations or modeling.
9. Dummy variables: Categorical variables that are transformed into numeric 0/1 values.
10. Lurk variables: Hidden or unconsidered variables that affect outcomes.
11. Confounding variables: Variables that influence both dependent and independent variables, distorting their relationship.
12. Intervening variables: Variables that mediate the relationship between independent and dependent variables.
13. Moderating variables: Variables that influence the strength or direction of the relationship between dependent and independent variables.
14. Label encoding: Assigning numeric values to categorical variables.
15. One-hot encoding: Transforming categorical variables into binary variables.
16. Normalization: Rescaling variables to a common range, usually 0 to 1.
17. Standardization: Rescaling variables to have mean 0 and standard deviation 1.
18. Variable transformation: Changing variables through operations to make them more suitable for modeling.
19. Variable selection: Choosing a subset of important variables for a model.
20. Multicollinearity: High correlation between two or more independent variables, distorting regression results.