What Is an Aptitude Test?

An aptitude test is an exam used to determine an individual’s skill or propensity to succeed in a given activity. Aptitude tests assume that individuals have inherent strengths and weaknesses and have a natural inclination toward success or failure in specific areas based on their innate characteristics.

Aptitude tests are generally used for job placement, college program entry, and to help people to get an idea of where their interests and aptitudes can take them regarding careers.

Understanding Aptitude Tests

Aptitude tests can be used to determine your capabilities in a variety of subjects. For example, individuals may take an aptitude test to determine the careers that are a good match for their skills and interests. Similarly, high school students may take an aptitude test when thinking about what would be an appropriate college major or whether college is the best choice for them.

In general, aptitude tests measure one’s competence in logical reasoning, numerical skills, or verbal ability; competency can be evaluated through problem-solving tasks and testing one’s critical thinking across various contexts.

When You Might Take an Aptitude Test

Some schools administer aptitude tests to students beginning in elementary school. Combined with intelligence and achievement tests that measure student mastery of academic content, aptitude tests may be used to determine placement in gifted and talented programs or other specific educational tracks.

Language Aptitudes

For example, the Modern Language Aptitude Test (MLAT) measures a student’s potential for successfully mastering foreign languages. Aptitude tests can also help determine if a student needs special education services.

Older Students

For older students, Differential Aptitude Tests (DATs) assess a range of aptitudes from spatial relations to language usage. The results can help administrators make curricular recommendations.

Curriculum Guidance

Counselors might use high scores in mechanical reasoning tests to guide students toward courses that prepare them for engineering or designing studies in college. For instance, students who score well in tests measuring speed, accuracy, and problem-solving might choose coursework in computer science, finance, or other fields requiring attention to detail.

Hiring Decisions

Some companies use aptitude tests to help them make hiring decisions. These tests, called career assessment tests, help human resources (HR) personnel learn more about a prospective employee’s strengths and weaknesses. Career assessment tests may also be used within a company to make promotion decisions.

Situational judgment tests are a specific kind of career aptitude test that can help predict how an employee might respond to specific situations in the workplace. They are also used to assess an employee’s communication style and ability to work within a team.

Some situational judgment tests focus specifically on customer service careers. These tests analyze personality traits predictive of successful public interaction, such as empathy, diplomacy, and patience. Situational judgment tests may also be used to predict future competency in sales-related careers.

Types of Aptitude Tests

Many types of aptitude tests are taken for educational or career discovery and ability purposes. The most common aptitude tests are:

• Mechanical reasoning: These types test your knowledge of physical concepts and are generally used to evaluate you for technical positions.
• Situational judgment: These tests gauge your reactions to situations and your decision-making.
• Diagrammatic/spatial reasoning: These tests see how well you can reach a conclusion based on processes contained in diagrams.
• Abstract reasoning: These measure problem-solving abilities and identify relationships between abstract arrays.
• Numerical reasoning: In these tests, your abilities with numbers, math, and data are tested.
• Verbal reasoning: Your language, reading comprehension, and vocabulary are tested in these types.
• Inductive reasoning: Under the pressure of time, these tests see how well you analyze patterns and data.
• Logical reasoning: Logical reasoning tests measure how well you recognize patterns and sequences and identify relationships between objects.
• Clerical aptitude: Clerical tests measure how fast and accurate you can be, seeing how well you can concentrate.

How to Pass an Aptitude Test

You might have heard that you can’t prepare for an aptitude test, but nothing could be further from the truth. Study guides and practice aptitude tests are readily available online and in community or high school libraries.

Conduct an online search for the type of test you’ll be taking. You’ll find many guides that offer tips on subjects you can read about and practice tests. Once you’ve found the practice tests and some study material:

• Practice the test daily: Practicing every day gives you continuous exposure to the questions and answers and helps you identify weak spots.
• Learn the test format: Remembering the format helps you move through the test. It also allows you to identify areas you need to work on.
• Carefully read the instructions: When you take the test, don’t dive into it. Instead, read all instructions and ensure you understand them, so you don’t work through a section giving wrong answers.
• Manage your testing time: Answer the questions you know first. Then, go back and work on the more challenging questions.

The Bottom Line

An aptitude test is a measure of your ability to learn or perform required tasks and succeed in the environment you’re in. They are not a measure of intelligence—these tests look for your strengths and weaknesses, giving you and the evaluator an idea of your abilities.

Most aptitude tests can indicate success in education and a career, but they are not always 100% accurate predictions. For example, it is possible to score very low on an aptitude test for a specific job and be successful with hard work and dedication—the test may have merely taken a snapshot of your knowledge and abilities at a point where you didn’t know much about the field.

Remember that preparation can help you pass an aptitude test even if you have no prior knowledge in an area—if you think of it as learning something new and interesting, you may surprise yourself with what you can do.

What Is an Autoregressive Integrated Moving Average (ARIMA)?

An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends. 

A statistical model is autoregressive if it predicts future values based on past values. For example, an ARIMA model might seek to predict a stock’s future prices based on its past performance or forecast a company’s earnings based on past periods.

Understanding Autoregressive Integrated Moving Average (ARIMA)

An autoregressive integrated moving average model is a form of regression analysis that gauges the strength of one dependent variable relative to other changing variables. The model’s goal is to predict future securities or financial market moves by examining the differences between values in the series instead of through actual values.

An ARIMA model can be understood by outlining each of its components as follows:

• Autoregression (AR): refers to a model that shows a changing variable that regresses on its own lagged, or prior, values.
• Integrated (I): represents the differencing of raw observations to allow the time series to become stationary (i.e., data values are replaced by the difference between the data values and the previous values).
• Moving average (MA):  incorporates the dependency between an observation and a residual error from a moving average model applied to lagged observations.

ARIMA Parameters

Each component in ARIMA functions as a parameter with a standard notation. For ARIMA models, a standard notation would be ARIMA with p, d, and q, where integer values substitute for the parameters to indicate the type of ARIMA model used. The parameters can be defined as:

• p: the number of lag observations in the model, also known as the lag order.
• d: the number of times the raw observations are differenced; also known as the degree of differencing.
• q: the size of the moving average window, also known as the order of the moving average.

For example, a linear regression model includes the number and type of terms. A value of zero (0), which can be used as a parameter, would mean that particular component should not be used in the model. This way, the ARIMA model can be constructed to perform the function of an ARMA model, or even simple AR, I, or MA models.

ARIMA and Stationary Data

In an autoregressive integrated moving average model, the data are differenced in order to make it stationary. A model that shows stationarity is one that shows there is constancy to the data over time. Most economic and market data show trends, so the purpose of differencing is to remove any trends or seasonal structures. 

Seasonality, or when data show regular and predictable patterns that repeat over a calendar year, could negatively affect the regression model. If a trend appears and stationarity is not evident, many of the computations throughout the process cannot be made and produce the intended results.

How to Build an ARIMA Model

To begin building an ARIMA model for an investment, you download as much of the price data as you can. Once you’ve identified the trends for the data, you identify the lowest order of differencing (d) by observing the autocorrelations. If the lag-1 autocorrelation is zero or negative, the series is already differenced. You may need to difference the series more if the lag-1 is higher than zero.

Next, determine the order of regression (p) and order of moving average (q) by comparing autocorrelations and partial autocorrelations. Once you have the information you need, you can choose the model you’ll use.

Pros and Cons of ARIMA

ARIMA models have strong points and are good at forecasting based on past circumstances, but there are more reasons to be cautious when using ARIMA. In stark contrast to investing disclaimers that state “past performance is not an indicator of future performance…,” ARIMA models assume that past values have some residual effect on current or future values and use data from the past to forecast future events.

The following table lists other ARIMA traits that demonstrate good and bad characteristics.

The Bottom Line

The ARIMA model is used as a forecasting tool to predict how something will act in the future based on past performance. It is used in technical analysis to predict an asset’s future performance.

ARIMA modeling is generally inadequate for long-term forecastings, such as more than six months ahead, because it uses past data and parameters that are influenced by human thinking. For this reason, it is best used with other technical analysis tools to get a clearer picture of an asset’s performance.

What Is All Risks?

“All risks” refers to a type of insurance coverage that automatically covers any risk that the contract does not explicitly omit. For example, if an “all risk” homeowner’s policy does not expressly exclude flood coverage, then the house will be covered in the event of flood damage.

This type of policy is found only in the property-casualty market.

Understanding All Risks

Insurance providers generally offer two types of property coverage for homeowners and businesses—named perils and “all risks.” A named perils insurance contract only covers the perils stipulated explicitly in the policy.

For example, an insurance contract might specify that any home loss caused by fire or vandalism will be covered. Therefore, an insured who experiences a loss or damage caused by a flood cannot file a claim to his or her insurance provider, as a flood is not named as a peril under the insurance coverage. Under a named perils policy, the burden of proof is on the insured.

An all risks insurance contract covers the insured from all perils, except the ones specifically excluded from the list. Contrary to a named perils contract, an all risks policy does not name the risks covered, but instead, names the risks not covered. In so doing, any peril not named in the exclusions list is automatically covered.

The most common types of perils excluded from “all risks” include earthquake, war, government seizure or destruction, wear and tear, infestation, pollution, nuclear hazard, and market loss. An individual or business who requires coverage for any excluded event under “all risks” may have the option to pay an additional premium, known as a rider or floater, to have the peril included in the contract.

Burden of Proof

The trigger for coverage under an “all risks” policy is physical loss or damage to property. An insured must prove physical damage or loss has occurred before the burden of proof shifts to the insurer, who then has to prove that an exclusion applies to the coverage.

For example, a small business that experienced a power outage may file a claim citing physical loss. The insurance company, on the other hand, might reject the claim stating that the company experienced a loss of income from a mere loss of property use, which is not the same thing as a physical loss of property.

Special Considerations

Because “all risks” is the most comprehensive type of coverage available and protects the insured from a greater number of possible loss events, it is priced proportionately higher than other types of policies. The cost of this type of insurance should, therefore, be measured against the probability of a claim.

It is possible to have named perils and “all risks” in the same policy. For example, an insured may have a property insurance policy that has all risks coverage on the building and named perils on his personal property. Everyone should read the fine print of any insurance agreement to ensure that they understand what is excluded in the policy.

Also, just because an insurance policy is termed “all risks” does not mean that it covers “all risks” since the exclusions reduce the level of coverage that is offered. Make sure you look for the exclusions in any prospective policy.

The Bottom Line

All risk insurance, also called all risk coverage, is an insurance product that covers any incident that isn’t explicitly mentioned. These policies assume a good deal of risk for the insurer and are less common than named risk coverage, which states exactly what is covered, versus stating only what is to be omitted (which is the case with all risk).