Consolidation is the process of combining multiple elements into a single, more effective or coherent whole. In the context of data analytics, consolidation would involve merging similar fields to reduce the overall number of fields in a dataset. This is particularly useful when a dataset contains redundant or similar data across multiple fields, as it helps to simplify the data structure and improve efficiency. Techniques such as dimensionality reduction are often applied to achieve this, where the goal is to retain the most informative and representative features of the data while reducing the number of total features.
Reference:
Applied Dimensionality Reduction - 3 Techniques using Python1.
Seven Techniques for Data Dimensionality Reduction2.
Best practices when working with datasets3.
Effectively Handling Large Datasets4.

Two reasons to conduct data cleansing are:
To improve accuracy: Data cleansing helps to ensure that the data is correct, consistent, and reliable. This can improve the quality and validity of the analysis, as well as the decision-making and outcomes based on the data12 To calculate trends: Data cleansing helps to remove or resolve any errors, outliers, or missing values that could distort or skew the dat a. This can help to identify and measure the patterns, changes, or relationships in the data over time13

Explanation
The correct answer is: 60
Range is the interval between the highest and the lowest score.
Range is a measure of variability or scatteredness of the varieties or observations among themselves and does not give an idea about the spread of the observations around some central value.
Symbolically R = Hs - Ls.
Where R = Range; Hs is the 'Highest score' and Ls is the Lowest Score.
The scores of ten students in a test are: 17, 23, 30, 36, 45, 51, 58, 66, 72, 77.
The highest score is 77 and the lowest score is 17.
So the range is the difference between these two scores Range = 77 - 17 = 60

The correct answer is D. Spearman's rank correlation.
Spearman's rank correlation is a non-parametric test that measures the strength and direction of the relationship between two variables that are ranked (ordinal) or continuous. Spearman's rank correlation does not assume that the data follows a normal distribution or that the variables are linearly related. Spearman's rank correlation is based on the ranks of the data rather than the actual values12 A) One-sample t-test is not correct, because it is a parametric test that compares the mean of a sample to a specified value. One-sample t-test assumes that the data follows a normal distribution and has a known population standard deviation34 B) Two-way ANOVA is not correct, because it is a parametric test that compares the means of two or more groups that are influenced by two independent factors. Two-way ANOVA assumes that the data follows a normal distribution, has homogeneous variances, and has independent observations.
C) Correlation coefficient is not correct, because it is a parametric test that measures the strength and direction of the linear relationship between two continuous variables. Correlation coefficient assumes that the data follows a bivariate normal distribution and has no outliers.