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In data analysis, the independent variable represents the input or cause that influences changes in the dependent variable, which is the outcome. By controlling or altering the independent variable, analysts can study its impact on the dependent variable. For example, in a marketing analysis, the budget (independent variable) may be adjusted to observe effects on sales revenue (dependent variable), providing insight into budget effectiveness. This causal relationship allows analysts to understand how different factors influence outcomes. Option A is incorrect as it describes a dependent variable, not an independent one. Option B is incorrect because variables in analysis are often manipulated, not held constant. Option D is incorrect as independent variables significantly affect analysis outcomes. Option E is incorrect as random variables are not independent by definition in this context.
√ 27556.11 × √ 624.9 – (22.02) 2 =? × 5.95
1120.04 – 450.18 + 319.98 ÷ 8.06 = ?
24.99 × 32.05 + ? - 27.01 × 19.97 = 29.99 × 27.98
Find the approximate value of Question mark(?). No need to find the exact value.
18.07 × (47.998 ÷ 12.03) + 59.78% of 150.14 – √(255.86) = ...
(124.901) × (11.93) + 219.95 = ? + 114.891 × 13.90
41.5% of ? + 64.69% of 419.1 = 504.2
10.10% of 999.99 + 14.14 × 21.21 - 250.25 = ?
{(1799.89 ÷ 8.18) ÷ 9.09 + 175.15} = 25.05% of ?
(27.08)2 – (14.89)2 – (22.17)2 = ?
159.98% of 4820 + 90.33% of 2840 = ? + 114.99% of 1980