“Data Analyst frequently Asked Questions in various Data Analysis job Interviews by interviewer. The set of questions here ensures that you offer a perfect answer posed to you. So get preparation for your new job hunting”
All normal distributions satisfy the following properties:
★ Approximately 68% of the observations fall within 1 standard deviation of the mean
★ Approximately 95% of the observations fall within 2 standard deviations of the mean
★ The mean, median and mode of the data are nearly all equal
The median of a set of numbers is the number that is exactly half way between the largest and smallest number, when the numbers are arranged from smallest to largest.
A box plot graphically displays groups of numerical data through their five-number summaries: the smallest observation (sample minimum), lower quartile (Q1), median (M), upper quartile (Q2) and largest observation (sample maximum).
The box plot is often called a "whisker" plot because "whiskers" extend outward from the boxes to the least and greatest values.
There are many data analysis topics that you should be familiar with. Many of them are described here. They include:
★ Averages
★ Box Plots
★ Normal Distributions
★ Counting Methods - Sets
★ Mode
★ Median
The average or the arithmetic mean is defined as the sum of all measurements divided by the number of observations. For example, the average of the numbers 10, 12 and 22 is 14.6 because 10+12+22=44 and there are three observations, so 44 divided by three is 14.6. When there are only numbers involved, an average problem is quite simple.
Data analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap and evaluate data.
Two operations to perform sets are:
★ Intersection
★ Union
There are two objects of set:
★ Members
★ Elements
Short-term outcomes are most likely include changes in skills, attitudes and knowledge.
Long-term outcomes are persistence of behaviors and broader lifestyle changes.
Medium-term outcomes include changes in behavior and decision-making.
One of the most important issues in interpreting research findings is understanding how outcomes relate to the intervention that is being evaluated. This involves making the distinction between association and causation and the role that can be played by confounding factors in skewing the evidence.
To rule out that a relationship between two events has been distorted by other, external factors, it is necessary to control for confounding. Confounding factors may actually be the reason we see particular outcomes, which may have nothing to do with what is being measured.
To rule out confounding, additional information must be gathered and analyzed. This includes any information that can possibly influence outcomes.
Example:
When mounting a campaign against alcohol-impaired driving, it is important to know whether other interventions aimed at road traffic safety are being undertaken at the same time. Similarly, if the campaign coincides with tighter regulations around BAC limits and with increased enforcement and roadside testing by police, it would be difficult to say whether any drop in the rate of drunk-driving crashes was attributable to the campaign or to these other measures.
A set that contains no members is called the empty set and is denoted with the symbol '∅'.
A causal relationship exists when one event (cause) is necessary for a second event (effect) to occur. The order in which the two occur is also critical.
Example:
★ For intoxication to occur, there must be heavy drinking, which precedes intoxication.
★ Determining cause and effect is an important function of evaluation, but it is also a major challenge.
Causation can be complex:
★ Some causes may be necessary for an effect to be observed, but may not be sufficient; other factors may also be needed.
★ While one cause may result in a particular outcome, other causes may have the same effect.
Being able to correctly attribute causation is critical, particularly when conducting an evaluation and interpreting the findings.
Short and long-term outcomes can be measured by using different methodologies for collecting data.
★ Cross-sectional study
★ Longitudinal study
A set is a collection of distinct objects that have a specific property.
For example, the set of all digits greater than 5 includes the numbers 6, 7, 8 and 9.
The normal distribution is a pattern for the distribution of data which follows a bell-shaped curve. The normal distribution is called normal because the data points are concentrated in the center near the mean. The data does not usually contain extreme values and the probability of deviations of the data points from the mean are (nearly) identical in either direction.
An association exists when one event is more likely to occur because another event has taken place. However, although the two events may be associated. One does not necessarily cause the other. The second event can still occur independently of the first.
Example:
Some research supports an association between certain patterns of drinking and the incidence of violence. However, even though harmful drinking and violent behavior may co-occur, there is no evidence showing that it is drinking that causes violence.
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