Trending February 2024 # It And End Users Differ On Spam Severity # Suggested March 2024 # Top 9 Popular

You are reading the article It And End Users Differ On Spam Severity updated in February 2024 on the website Cattuongwedding.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 It And End Users Differ On Spam Severity

While slightly more than half of end users say spam isn’t a problem,

nearly 80 percent of IT managers say it’s a problem that they’re

struggling with, according to a new study.

But despite their difference of opinion of the situation today, both end

users and IT managers say it’s a problem that will plague them for years

to come.

”They really don’t have a positive outlook about this,” says Chris

Miller, director of product management for Symantec, Inc., an

information security company based in Cupertino, Calif. ”In some ways,

it’s almost like being on the Titanic. The iceberg has been hit and the

crew is aware of all the impacts. The passengers up on the deck don’t see the damage below and don’t know all the implications, so they think it’s a little more under control. But everybody knows it’s going to be a long night and there’s a lot more icebergs ahead.”

The study, conducted by Insight Express for Symantec, shows that overall

end users are a lot less concerned about spam than their counterparts in

the IT department.

Slightly more than 50 percent of end users surveyed say spam is not a

problem in their workplace. However, 79.1 percent of IT managers say it

is a problem in the workplace.

When end users were asked if they think spam is under control at their

company, 8.4 percent say it’s out of control; 23.3 percent say it’s

barely under control, and 68 percent say it is under control.

Compare that to IT administrators who were asked the same question. A

similar 10 percent say it’s out of control; 33 percent say it’s barely

out of control, and 56 percent say they have it under control.

”End users are experiencing some degree of respite from the amount of

spam they are seeing,” says Miller. ”But the IT administrators are

basically getting the brunt of this problem. They’re not just dealing

with one person’s spam. They’re dealing with the spam that’s coming in

to everybody.

”They’re dealing with bandwidth usage, storage usage, viruses it may be

bringing in, staffing and the hours they have to put in,” adds Miller.

”They’re spending a lot of time with this problem. The end user sees it

as garbage they have to deal with. The IT manager has a lot of other

issues.”

In fact, the survey shows that spam has become one of the top worries

for administrators.

When IT managers were asked what they spend the majority of their time

on, spam came in second only to malware. Miller says 42.7 percent of

managers report malware as their worst problem, and 16.4 percent say

it’s spam.

”For a lot of our customers, I’d say it’s a nightmare,” he adds.

And it appears to be a nightmare that isn’t going away anytime soon.

According to the survey, 70.9 percent of IT administrators say they’ll

still be wrestling with spam three years from now, while 72 percent of

users say it will only get worse.

”This is painting a pretty grim picture moving forward,” says Miller.

”They’re both seeing increases and they’re both seeing it as a

long-term problem.”

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Adobe’s Support For Flash Player Will End: What Does This Mean For Mac Users?

Adobe Flash Player will officially reach its end-of-life (EOL) status on December 31, 2023 after nearly 25 years. If you don’t keep up with tech news, you may have learned about this from a pop-up like the one above. This means that Flash Player will no longer be distributed, supported or updated by Adobe. But what does this mean for Mac users?

See also: Is your Mac ready for Big Sur? Important Steps before you Upgrade

Although you’ve no doubt heard of Adobe’s Flash Player, you may not be that familiar with what it is used for. In this article, we will discuss what Flash Player does, the effects of its upcoming EOL status, and the steps you should take to prepare for Flash Player’s final days.

See also:

About Flash Player

Adobe Flash Player is a computer software, distributed as freeware, that has allowed users to play Adobe Flash content. The content has often included multimedia content, internet applications and streaming audio and video. It was once a common format for web games and animations. Flash Player has most commonly been run as a browser plug-in.

See also: Among Us on Mac: Play for Free, No Steam Required

For quite some time – about ten years – Apple hasn’t pre-loaded Adobe Flash Player on Mac. Users had to install it from Adobe, and then, once it was installed, give permission for each website to run the Flash Plugin.

See also: How to Back Up your Mac with Time Machine

See also: This Password has Appeared in a Data Leak: Security Recommendations on iPhone

Why is it Ending?

In July of 2023, Adobe announced that Flash Player will no longer be supported after December 2023. The reasons given for sunsetting Flash Player are:

See also: Set Up a New iPhone: How to Transfer Data from your Old iPhone

Diminished usage of the technology

Availability of better, more secure options such as HTML5, WebGL and WebAssembly.

See also: How to Clear the Browser Cache in Safari on Mac

Should I Uninstall Flash Player?

Once Flash Player’s support ends, there will be no more security updates. This means that keeping Flash Player installed poses a significant security threat and, for that reason, all users are encouraged to remove it from their systems before the EOL date.

See also: Is Safari Not Working on Mac? How You Can Fix It

Most browsers will no longer support Flash Player and so the software will not run on updated versions of browsers such as Safari, Chrome and Firefox after December.

See also: Mac App Store not Updating Apps

You may be concerned that some of the websites or resources you use, still require Flash Player, however, very few websites still use Flash, and it has never been supported on iOS devices.

See also: How To Uninstall Flash Player On Mac

Flash Player will no longer be available for download after December 31st and although copies of it may be found on third-party sites, it is highly recommended that you do not download unauthorized copies of Flash Player from third-party sites. Not only will these copies carry the risks that come from not having available security updates, but the third-party copies of Flash Player may very well come with malware attached.

See also: How to Create, View, Edit, and Restore Bookmarks in Safari on Mac

How to Uninstall

It is easy to uninstall a program from your Mac. Although the latest version of Safari, Safari 14, no longer supports Flash Player, you may still have it installed on your system. You may also have been using Flash Player with another browser like Chrome or Firefox or an older version of Safari. Even if you won’t be using a browser that supports Flash Player, you may still want to uninstall it to remove the unused files from your computer. To uninstall Flash Player:

See also: A Guide to Buying a Used iPhone or iPad

Enter your user name and password.

See also: How to Set Up Smart Mailboxes on your Mac

Now, when you look in Finder under Utilities, you will no longer see Adobe Flash Player Install Manager. If you have questions about or problems with uninstalling, you can go to Adobe’s help site, chúng tôi and search for help on the issue you’re having.

Recent Articles:

End To End Statistics For Data Science

Statistics is a type of mathematical analysis that employs quantified models and representations to analyse a set of experimental data or real-world studies. The main benefit of statistics is that information is presented in an easy-to-understand format.

Data processing is the most important aspect of any Data Science plan. When we speak about gaining insights from data, we’re basically talking about exploring the chances. In Data Science, these possibilities are referred to as Statistical Analysis.

Most of us are baffled as to how Machine Learning models can analyse data in the form of text, photos, videos, and other extremely unstructured formats. But the truth is that we translate that data into a numerical form that isn’t exactly our data, but it’s close enough. As a result, we’ve arrived at a crucial part of Data Science.

Data in numerical format gives us an infinite number of ways to understand the information it contains. Statistics serves as a tool for deciphering and processing data in order to achieve successful outcomes. Statistics’ strength is not limited to comprehending data; it also includes methods for evaluating the success of our insights, generating multiple approaches to the same problem, and determining the best mathematical solution for your data.

Table of Contents

· Importance of Statistics

· Type of Analytics

· Probability

· Properties of Statistics

· Central Tendency

· Variability

· Relationship Between Variables

· Probability Distribution

· Hypothesis Testing and Statistical Significance

· Regression

Importance of Statistics

1) Using various statistical tests, determine the relevance of features.

2) To avoid the risk of duplicate features, find the relationship between features.

3) Putting the features into the proper format.

4) Data normalization and scaling This step also entails determining the distribution of data as well as the nature of data.

5) Taking the data for further processing and making the necessary modifications.

6) Determine the best mathematical approach/model after processing the data.

7) After the data are acquired, they are checked against the various accuracy measuring scales.

Acknowledge the Different Types of Analytics in Statistics

 

1. Descriptive Analytics – What happened?

It tells us what happened in the past and helps businesses understand how they are performing by providing context to help stakeholders interpret data.

Descriptive analytics should serve as a starting point for all organizations. This type of analytics is used to answer the fundamental question “what happened?” by analyzing data, which is often historical.

It examines past events and attempts to identify specific patterns within the data. When people talk about traditional business intelligence, they’re usually referring to Descriptive Analytics.

Pie charts, bar charts, tables, and line graphs are common visualizations for Description Analytics.

This is the level at which you should begin your analytics journey because it serves as the foundation for the other three tiers. To move forward with your analytics, you must first determine what happened.

Consider some sales use cases to gain a better understanding of this. For instance, how many sales occurred in the previous quarter? Was it an increase or a decrease?

2. Diagnostic Analytics – Why did it happen?

It goes beyond descriptive data to assist you in comprehending why something occurred in the past.

This is the second step because you want to first understand what occurred to work out why it occurred. Typically, once an organisation has achieved descriptive insights, diagnostics will be applied with a bit more effort.

3. Predictive Analytics – What is likely to happen?

It forecasts what is likely to happen in the future and provides businesses with data-driven actionable insights.

The transition from Predictive Analytics to Diagnostics Analytics is critical. multivariate analysis, forecasting, multivariate statistics, pattern matching, predictive modelling, and forecasting are all a part of predictive analytics.

These techniques are more difficult for organisations to implement because they necessitate large amounts of high-quality data. Furthermore, these techniques necessitate a thorough understanding of statistics as well as programming languages such as R and Python.

Many organisations may lack the internal expertise required to effectively implement a predictive model.

So, why should any organisation bother with it? Although it can be difficult to achieve, the value that Predictive Analytics can provide is enormous.

A Predictive Model, for example, will use historical data to predict the impact of the next marketing campaign on customer engagement.

If a company can accurately identify which action resulted in a specific outcome, it can predict which actions will result in the desired outcome. These types of insights are useful in the next stage of analytics.

4. Prescriptive Analytics – What should be done?

It makes recommendations for actions that will capitalise on the predictions and guide the potential actions toward a solution.

Prescriptive Analytics is an analytics method that analyses data to answer the question “What should be done?”

Techniques used in this type of analytics include graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

This is the toughest level to reach. The accuracy of the three levels of the analytics below has a significant impact on the dependability of Prescriptive Analytics. The techniques required to obtain an effective response from a prescriptive analysis are determined by how well an organisation has completed each level of analytics.

Considering the quality of data required, the appropriate data architecture to facilitate it, and the expertise required to implement this architecture, this is not an easy task.

Its value is that it allows an organisation to make decisions based on highly analysed facts rather than instinct. That is, they are more likely to achieve the desired outcome, such as increased revenue.

Once again, a use case for this type of analytics in marketing would be to assist marketers in determining the best mix of channel engagement. For instance, which segment is best reached via email?

Probability

In a Random Experiment, the probability is a measure of the likelihood that an event will occur. The number of favorable outcomes in an experiment with n outcomes is denoted by x. The following is the formula for calculating the probability of an event.

Probability (Event) = Favourable Outcomes/Total Outcomes = x/n

Let’s look at a simple application to better understand probability. If we need to know if it’s raining or not. There are two possible answers to this question: “Yes” or “No.” It is possible that it will rain or not rain. In this case, we can make use of probability. The concept of probability is used to forecast the outcomes of coin tosses, dice rolls, and card draws from a deck of playing cards.

Properties of Statistics 

· Complement: Ac, the complement of an event A in a sample space S, is the collection of all outcomes in S that are not members of set A. It is equivalent to rejecting any verbal description of event A.

P(A) + P(A’) = 1

· Intersection: The intersection of events is a collection of all outcomes that are components of both sets A and B. It is equivalent to combining descriptions of the two events with the word “and.”

P(A∩B) = P(A)P(B)

· Union: The union of events is the collection of all outcomes that are members of one or both sets A and B. It is equivalent to combining descriptions of the two events with the word “or.”

P(A∪B) = P(A) + P(B) − P(A∩B)

· Mutually Exclusive Events: If events A and B share no elements, they are mutually exclusive. Because A and B have no outcomes in common, it is impossible for both A and B to occur on a single trial of the random experiment. This results in the following rule

P(A∩B) = 0

Any event A and its complement Ac are mutually exclusive if and only if A and B are mutually exclusive, but A and B can be mutually exclusive without being complements.

· Bayes’ Theorem: it is a method for calculating conditional probability. The probability of an event occurring if it is related to one or more other events is known as conditional probability. For example, your chances of finding a parking space are affected by the time of day you park, where you park, and what conventions are taking place at any given time.

Central Tendency in Statistics

1) Mean: The mean (or average) is that the most generally used and well-known measure of central tendency. It will be used with both discrete and continuous data, though it’s most typically used with continuous data (see our styles of Variable guide for data types). The mean is adequate the sum of all the values within the data set divided by the number of values within the data set. So, if we have n values in a data set and they have values x1,x2, …,xn, the sample mean, usually denoted by “x bar”, is:

2) Median: The median value of a dataset is the value in the middle of the dataset when it is arranged in ascending or descending order. When the dataset has an even number of values, the median value can be calculated by taking the mean of the middle two values.

The following image gives an example for finding the median for odd and even numbers of samples in the dataset.

3) Mode: The mode is the value that appears the most frequently in your data set. The mode is the highest bar in a bar chart. A multimodal distribution exists when the data contains multiple values that are tied for the most frequently occurring. If no value repeats, the data does not have a mode.

4) Skewness: Skewness is a metric for symmetry, or more specifically, the lack of it. If a distribution, or data collection, looks the same to the left and right of the centre point, it is said to be symmetric.

5) Kurtosis: Kurtosis is a measure of how heavy-tailed or light-tailed the data are in comparison to a normal distribution. Data sets having a high kurtosis are more likely to contain heavy tails or outliers. Light tails or a lack of outliers are common in data sets with low kurtosis.

Variability in Statistics

Range: In statistics, the range is the smallest of all dispersion measures. It is the difference between the distribution’s two extreme conclusions. In other words, the range is the difference between the distribution’s maximum and minimum observations.

Range = Xmax – Xmin

Where Xmax represents the largest observation and Xmin represents the smallest observation of the variable values.

Percentiles, Quartiles and Interquartile Range (IQR)

· Percentiles — It is a statistician’s unit of measurement that indicates the value below which a given percentage of observations in a group of observations fall.

For instance, the value QX represents the 40th percentile of XX (0.40)

· Quantiles— Values that divide the number of data points into four more or less equal parts, or quarters. Quantiles are the 0th, 25th, 50th, 75th, and 100th percentile values or the 0th, 25th, 50th, 75th, and 100th percentile values.

· Interquartile Range (IQR)— The difference between the third and first quartiles is defined by the interquartile range. The partitioned values that divide the entire series into four equal parts are known as quartiles. So, there are three quartiles. The first quartile, known as the lower quartile, is denoted by Q1, the second quartile by Q2, and the third quartile by Q3, known as the upper quartile. As a result, the interquartile range equals the upper quartile minus the lower quartile.

IQR = Upper Quartile – Lower Quartile

= Q3 − Q1

· Variance: The dispersion of a data collection is measured by variance. It is defined technically as the average of squared deviations from the mean.

· Standard Deviation: The standard deviation is a measure of data dispersion WITHIN a single sample selected from the study population. The square root of the variance is used to compute it. It simply indicates how distant the individual values in a sample are from the mean. To put it another way, how dispersed is the data from the sample? As a result, it is a sample statistic.

· Standard Error (SE): The standard error indicates how close the mean of any given sample from that population is to the true population mean. When the standard error rises, implying that the means are more dispersed, it becomes more likely that any given mean is an inaccurate representation of the true population mean. When the sample size is increased, the standard error decreases – as the sample size approaches the true population size, the sample means cluster more and more around the true population mean.

Relationship Between Variables

· Causality: The term “causation” refers to a relationship between two events in which one is influenced by the other. There is causality in statistics when the value of one event, or variable, grows or decreases as a result of other events.

Each of the events we just observed may be thought of as a variable, and as the number of hours worked grows, so does the amount of money earned. On the other hand, if you work fewer hours, you will earn less money.

· Covariance: Covariance is a measure of the relationship between two random variables in mathematics and statistics. The statistic assesses how much – and how far – the variables change in tandem. To put it another way, it’s a measure of the variance between two variables. The metric, on the other hand, does not consider the interdependence of factors. Any positive or negative value can be used for the variance.

The following is how the values are interpreted:

· Positive covariance: When two variables move in the same direction, this is called positive covariance.

· Negative covariance indicates that two variables are moving in opposite directions.

· Correlation: Correlation is a statistical method for determining whether or not two quantitative or categorical variables are related. To put it another way, it’s a measure of how things are connected. Correlation analysis is the study of how variables are connected.

Ø Here are a few examples of data with a high correlation:

1) Your calorie consumption and weight.

2) Your eye colour and the eye colours of your relatives.

3) The amount of time you spend studying and your grade point average

Ø Here are some examples of data with poor (or no) correlation:

1) Your sexual preference and the cereal you eat are two factors to consider.

2) The name of a dog and the type of dog biscuit that they prefer.

3) The expense of vehicle washes and the time it takes to get a Coke at the station.

Correlations are useful because they allow you to forecast future behaviour by determining what relationship variables exist. In the social sciences, such as government and healthcare, knowing what the future holds is critical. Budgets and company plans are also based on these facts.

Probability Distributions   Probability Distribution Functions

1) Probability Mass Function (PMF): The probability distribution of a discrete random variable is described by the PMF, which is a statistical term.

The terms PDF and PMF are frequently misunderstood. The PDF is for continuous random variables, whereas the PMF is for discrete random variables. Throwing a dice, for example (you can only choose from 1 to 6 numbers (countable))

2) Probability Density Function (PDF): The probability distribution of a continuous random variable is described by the word PDF, which is a statistical term.

The Gaussian Distribution is the most common distribution used in PDF. If the features / random variables are Gaussian distributed, then the PDF will be as well. Because the single point represents a line that does not span the area under the curve, the probability of a single outcome is always 0 on a PDF graph.

3) Cumulative Density Function (CDF): The cumulative distribution function can be used to describe the continuous or discrete distribution of random variables.

If X is the height of a person chosen at random, then F(x) is the probability of the individual being shorter than x. If F(180 cm)=0.8, then an individual chosen at random has an 80% chance of being shorter than 180 cm (equivalently, a 20 per cent chance that they will be taller than 180cm).

  Continuous Probability Distribution

A coin flip that returns a head or tail has a probability of p = 0.50 and would be represented by a line from the y-axis at 0.50.

2) Normal/Gaussian Distribution: The normal distribution, also known as the Gaussian distribution, is a symmetric probability distribution centred on the mean, indicating that data around the mean occur more frequently than data far from it. The normal distribution will show as a bell curve on a graph.

Points to remember: –

· A probability bell curve is referred to as a normal distribution.

· The mean of a normal distribution is 0 and the standard deviation is 1. It has a kurtosis of 3 and zero skew.

· Although all symmetrical distributions are normal, not all normal distributions are symmetrical.

· Most pricing distributions aren’t totally typical.

3) Exponential Distribution: The exponential distribution is a continuous distribution used to estimate the time it will take for an event to occur. For example, in physics, it is frequently used to calculate radioactive decay, in engineering, it is frequently used to calculate the time required to receive a defective part on an assembly line, and in finance, it is frequently used to calculate the likelihood of a portfolio of financial assets defaulting. It can also be used to estimate the likelihood of a certain number of defaults occurring within a certain time frame.

4) Chi-Square Distribution: A continuous distribution with degrees of freedom is called a chi-square distribution. It’s used to describe a sum of squared random variable’s distribution. It’s also used to determine whether a data distribution’s goodness of fit is good, whether data series are independent, and to estimate confidence intervals around variance and standard deviation for a random variable from a normal distribution. Furthermore, the chi-square distribution is a subset of the gamma distribution.

Discrete Probability Distribution

1) Bernoulli Distribution: A Bernoulli distribution is a discrete probability distribution for a Bernoulli trial, which is a random experiment with just two outcomes (named “Success” or “Failure” in most cases). When flipping a coin, the likelihood of getting ahead (a “success”) is 0.5. “Failure” has a chance of 1 – P. (where p is the probability of success, which also equals 0.5 for a coin toss). For n = 1, it is a particular case of the binomial distribution. In other words, it’s a single-trial binomial distribution (e.g. a single coin toss).

2) Binomial Distribution: A discrete distribution is a binomial distribution. It’s a well-known probability distribution. The model is then used to depict a variety of discrete phenomena seen in business, social science, natural science, and medical research.

Because of its relationship with a binomial distribution, the binomial distribution is commonly employed. For binomial distribution to be used, the following conditions must be met:

1. There are n identical trials in the experiment, with n being a limited number.

2. Each trial has only two possible outcomes, i.e., each trial is a Bernoulli’s trial.

3. One outcome is denoted by the letter S (for success) and the other by the letter F (for failure) (for failure).

4. From trial to trial, the chance of S remains the same. The chance of success is represented by p, and the likelihood of failure is represented by q (where p+q=1).

5. Each trial is conducted independently.

6. The number of successful trials in n trials is the binomial random variable x.

If X reflects the number of successful trials in n trials under the preceding conditions, then x is said to follow a binomial distribution with parameters n and p.

3) Poisson Distribution: A Poisson distribution is a probability distribution used in statistics to show how many times an event is expected to happen over a certain amount of time. To put it another way, it’s a count distribution. Poisson distributions are frequently accustomed comprehend independent events that occur at a gradual rate during a selected timeframe.

The Poisson distribution is a discrete function, which means the variable can only take values from a (possibly endless) list of possibilities. To put it another way, the variable can’t take all of the possible values in any continuous range. The variable can only take the values 0, 1, 2, 3, etc., with no fractions or decimals, in the Poisson distribution (a discrete distribution).

Hypothesis testing may be a method within which an analyst verifies a hypothesis a couple of population parameters. The analyst’s approach is set by the kind of the info and also the purpose of the study. the utilization of sample data to assess the plausibility of a hypothesis is thought of as hypothesis testing.

Null and Alternative Hypothesis Null Hypothesis (H0)

A population parameter (such as the mean, standard deviation, and so on) is equal to a hypothesised value, according to the null hypothesis. The null hypothesis is a claim that is frequently made based on previous research or specialised expertise.

Alternative hypothesis (H1)

The alternative hypothesis says that a population parameter is less, more, or different than the null hypothesis’s hypothesised value. The alternative hypothesis is what you believe or want to prove to be correct.

Type 1 and Type 2 error Type 1 error:

A type 1 error, often referred to as a false positive, happens when a researcher rejects a real null hypothesis incorrectly. this suggests you’re claiming your findings are noteworthy after they actually happened by coincidence.

Your alpha level (), which is that the p-value below which you reject the null hypothesis, represents the likelihood of constructing a sort I error. When rejecting the null hypothesis, a p-value of 0.05 suggests that you simply are willing to tolerate a 5% probability of being mistaken.

By setting p to a lesser value, you’ll lessen your chances of constructing a kind I error.

Type 2 error:

A type II error commonly said as a false negative happens when a researcher fails to reject a null hypothesis that’s actually true. during this case, a researcher finds that there’s no significant influence when, in fact, there is.

Beta () is that the probability of creating a sort II error, and it’s proportional to the statistical test’s power (power = 1- ). By ensuring that your test has enough power, you’ll reduce your chances of constructing a sort II error.

This can be accomplished by ensuring that your sample size is sufficient to spot a practical difference when one exists.

 

Interpretation

P-value: The p-value in statistics is that the likelihood of getting outcomes a minimum of as extreme because the observed results of a statistical hypothesis test, given the null hypothesis is valid. The p-value, instead of rejection points, is employed to work out the smallest amount level of significance at which the null hypothesis is rejected. A lower p-value indicates that the choice hypothesis has more evidence supporting it.

Critical Value: it is a point on the test distribution that is compared to the test statistic to see if the null hypothesis should be rejected. You can declare statistical significance and reject the null hypothesis if the absolute value of your test statistic is larger than the crucial value.

Significance Level and Rejection Region: The probability that an event (such as a statistical test) occurred by chance is the significance level of the occurrence. We call an occurrence significant if the level is very low, i.e., the possibility of it happening by chance is very minimal. The rejection region depends on the importance level. the importance level is denoted by α and is that the probability of rejecting the null hypothesis if it’s true.

Z-Test: The z-test may be a hypothesis test within which the z-statistic is distributed normally. The z-test is best utilized for samples with quite 30 because, in line with the central limit theorem, samples with over 30 samples are assumed to be approximately regularly distributed.

The null and alternative hypotheses, also because the alpha and z-score, should all be reported when doing a z-test. The test statistic should next be calculated, followed by the results and conclusion. A z-statistic, also called a z-score, could be a number that indicates what number of standard deviations a score produced from a z-test is above or below the mean population.

T-Test: A t-test is an inferential statistic that’s won’t see if there’s a major difference within the means of two groups that are related in how. It’s most ordinarily employed when data sets, like those obtained by flipping a coin 100 times, are expected to follow a traditional distribution and have unknown variances. A t-test could be a hypothesis-testing technique that will be accustomed to assess an assumption that’s applicable to a population.

ANOVA (Analysis of Variance): ANOVA is the way to find out if experimental results are significant. One-way ANOVA compares two means from two independent groups using only one independent variable. Two-way ANOVA is the extension of one-way ANOVA using two independent variables to calculate the main effect and interaction effect.

Chi-Square Test: it is a test that assesses how well a model matches actual data. A chi-square statistic requires data that is random, raw, mutually exclusive, collected from independent variables, and drawn from a large enough sample. The outcomes of a fair coin flip, for example, meet these conditions.

In hypothesis testing, chi-square tests are frequently utilised. Given the size of the sample and the number of variables in the relationship, the chi-square statistic examines the size of any disparities between the expected and actual results.

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End Notes

Thank you for following with me all the way to the end. By the end of this article, we should have a good understanding of Complete Statistics for Data Science.

I hope you found this article useful. Please feel free to distribute it to your peers.

Hello, I’m Gunjan Agarwal from Gurugram, and I earned a Master’s Degree in Data Science from Amity University in Gurgaon. I enthusiastically participate in Data Science hackathons, blogathons, and workshops.

I’d like to connect with you on Linkedin. Mail me here for any queries.

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion

Related

Study: Workplace Im Users Seek To Bypass It Control

Despite the acknowledged benefits of instant messaging communications, workers in the U.K. are willfully using unregulated, consumer versions of the technology to circumvent corporate oversight — according to a new study.

The findings come about in a survey conducted by enterprise security firm SurfControl, which last year licensed Akonix’s L7 Enterprise Gateway for its own corporate IM solution.

While surveying U.K. workers on their instant messaging usage, SurfControl found that 42 percent of non-managerial employees said they preferred IM because of its speed, compared to e-mail.

But in what should be a cause for alarm for IT admins, almost as many workers — 31 percent — said they used consumer-grade IM primarily because it enabled them to engage in activities that they avoid over corporate e-mail, presumably because most businesses have policies explicitly stating their right to monitor e-mail.

“We’ve seen a huge take-up in the last year of the use of public IM in the workplace,” said SurfControl spokesperson Martino Corbelli. “One [reason] is because the social use of e-mail has become more and more restricted. Companies are trying to ensure that offensive material isn’t distributed, and that people don’t waste too much time on non-work related matters.”

“All that’s well and good in keeping companies and employees safe,” he said. “But all the threats, the risk of leakage of confidential data and of defamatory content, are all reintroduced the moment someone starts to use public IM, which goes unchecked by the company IT department in many instances.”

Consumer IM has long been known to be a potential liability for corporate IT. That’s because public IM is essentially designed to make it simple for users to connect to others: clients typically find ports open in users’ PC or corporate firewalls, sparing users from having to manually configure their systems. Since it’s easy to install, IM can be up-and-running without administrators’ knowledge; and because it uses open firewall ports — including the commonly unsecured port 80 — it can be hard to block, or even detect IM traffic. And that means IT staff can’t halt the spread of sensitive information, or protect the network from inbound IM worms.

Ominously, the study would seem to indicate that end-users in the workplace are aware (or at least, believe) that administers lack the ability to monitor their instant messaging conversations — and are willing to exploit that fact.

Yet public IM users in the office aren’t necessarily malicious, Corbelli said.

“Most people have no idea what they’re doing when they download IM,” he said. “They’re not doing it to spread viruses around the networks, to leak confidential company information, or to do anything else they think is bad. But all those threats exist and are real. While they are IMing their friends, it could impact the whole network.”

Users also tend to be uncertain about whether corporate Internet policies govern IM use, SurfControl found. The survey asked whether employees knew whether their company had policies in place covering IM. Twenty-six percent of non-management employees said their company had no such a policy in place while 34 percent said they didn’t know whether such a policy existed.

Corbelli said companies could reduce a great deal of their liability by developing policy on instant messaging and communicating it to employees.

“Once it’s communicated to everybody, straight away you tend to get self-regulation,” he said. “There are boundaries that they know they have to work within. That’s what we’re trying to move the public consciousness towards. We managed to move things along on the mail side, so people know that there are certain boundaries they need to use that tool within.”

Block And Report Imessage Spam Messages To Apple

iMessage, Apple’s own messaging service for iPhone, iPad, iPod Touch and Mac users, has been quite a success since it appeared on iOS 5 and OS X Lion. Yet, like most similar services of the kind, it’s had its share of issues and problems, with the main one being the lack of a proper blocking/reporting feature for spam messages. iOS 7 came with a feature allowing users to block those causing the offense. To bring this further, Apple recently rolled out a spam reporting feature allowing anyone to report troublesome users/messages to Apple.

Late last year, vulnerabilities were exposed in Apple’s iMessage infrastructure, which allowed just about anybody to spam another’s device with a simple Dos-like attack. This attack, alongside two to three other small vulnerabilities, highlighted the need for Apple to have a far more firm approach to security with iMessage, and since then, we’ve seen the company make significant developments in an attempt to do exactly that.

Differentiate Between A SMS/MMS and an iMessage

You shouldn’t mix these two up. This method is for reporting spam iMessages, while for spam SMS’s, be sure to contact your mobile service provider. On iOS, the main difference between a regular SMS/MMS and a iMessage is that a SMS will have a green box around it, while an iMessage will have a blue one, as shown below:

Another difference is that iMessages will have “iMessage” written on top of the window:

For Mac OS X users, by default, all your messages should be iMessages as SMS/MMS aren’t currently supported by OS X.

Report An iMessage

Screenshot of the message you received (How to take a screenshot: iOS/OS X)

Full email address/contact number from which you received the iMessage

The date and time you received the message

Here’s the complete process as Apple outlines on its website:

A Simple Method To Block iMessages in iOS

While the method detailed above could be better implemented with a simple “Spam” button, Tim Cook hasn’t decided to include such a feature, so we’re currently stuck with the method above to report spam messages. There is a simple workaround, though, to easily block iMessages coming from unknown numbers in iOS. All messages coming from numbers not saved in your Contacts list will automatically be blocked. To do this:

1. Open up the iMessage from the sender you would like to block.

4. Simply scroll down and select “Block this caller” to block the sender.

That’s it.

Conclusion

Many new features have emerged in iOS over the years, and amongst those, iMessage is arguably the most useful in the day-to-day routine. While it has its fair share of issues, its nice to see that work is being done to ensure a much improved overall service for the future. With the method narrated above, you should easily be able to report and block spam messages in iMessage.

Shujaa Imran

Shujaa Imran is MakeTechEasier’s resident Mac tutorial writer. He’s currently training to follow his other passion become a commercial pilot. You can check his content out on Youtube

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Ska2 Gene May Predict Severity Of Ptsd

Suicide Risk in Veterans: A New Indicator? Changes in SKA2 gene associated with brain cell loss, more severe post-traumatic stress disorder (PTSD)

U.S. Navy photo by Mass Communication Specialist 3rd Class Anna Van Nuys/Released

The statistics are grim. Veterans of the conflicts in Iraq and Afghanistan have a 41 to 61 percent higher risk of suicide than the United States’ general population, according to a 2014 study in the Annals of Epidemiology. This risk is far higher than seen in veterans from earlier wars, so high that both the US Senate and the House of Representatives unanimously passed a bill in early 2024 to improve suicide prevention programs at the US Department of Veterans Affairs (VA).

The suicide risk is “alarmingly high,” says Naomi Sadeh, assistant professor of psychiatry at Boston University’s School of Medicine (MED) and a psychologist at the National Center for PTSD. And the numbers raise important questions for researchers: which veterans will turn to suicide, and why? While risk rises for many reasons, PTSD—post-traumatic stress disorder—has emerged as one of the strongest predictors. But “not every veteran develops PTSD or becomes suicidal,” says Sadeh. “Our biggest challenge right now is predicting who is going to attempt suicide—we’re not really very good at that yet.”

Sadeh is lead author on a September 2024 study in Molecular Psychiatry pointing to a biomarker that might bring researchers a little closer to the goal of better predicting—and perhaps treating—PTSD and suicide risk: a gene called SKA2. Biomarkers, short for “biological markers,” are measurable indicators of health or disease, things like blood cholesterol or antibody levels. Many biomarkers don’t provide a definitive diagnosis of disease, but—in conjunction with other health information—can indicate risk, and also possible avenues of treatment. Scientists across disciplines are actively searching for biomarkers that may indicate early signs of diseases like Alzheimer’s, Parkinson’s, lung cancer, and, in this case, risk of suicide. “In the past, we’ve relied on self-reporting to estimate suicide risk—veterans telling us when they have depression or symptoms of PTSD,” says Sadeh. “The field is looking for more objective measurements, and that’s where biomarkers come in.”

SKA2 emerged as a possible biomarker for suicide risk in 2014, when researchers from Johns Hopkins compared the brains of people who had died from suicide to those who died from other causes. When screening the genomes of people who had died from suicide, the researchers looked for genes that were methylated—tagged with a tiny molecule of one carbon and three hydrogen atoms known as a methyl group—differently than in other genome samples. Methylation is one of the primary ways that the body (or the environment) switches genes on and off. The Johns Hopkins group found that in the brains of people who had died from suicide, the SKA2 gene was methylated in a certain location and thereby switched off. That study, published in the American Journal of Psychiatry in 2014, also found the same changes in SKA2 in the blood of live patients experiencing suicidal thoughts.

Though researchers still aren’t sure exactly what role SKA2 plays in the body, or why turning it off might increase risk for suicide, studies have shown that it helps regulate the “HPA axis,” a hormonal system that plays a role in our fight-or-flight response and other reactions to stress. Early research indicates that the SKA2 protein protects brain cells from damage, and that when the gene is turned off, stress hormones in the brain can cause cell damage and death.

The Hopkins study turned a spotlight on SKA2 and caught the eye of Miller and Sadeh. “It was a great study,” says Sadeh, “but one limitation was that it didn’t look at the brains of living individuals. So we wondered if we could bridge the gap between postmortem brains and living people.”

To cross this divide, Sadeh examined data from the VA’s Translational Research Center for TBI and Stress Disorders database, which contains health information—including brain scans, blood tests, and the results of comprehensive psychological exams—from about 200 veterans who have faced trauma. By analyzing the data, she found that methylated SKA2 was associated with more severe symptoms of PTSD and a loss of tissue in several regions of the brain. The team did not find any correlation between methylated SKA2 and depression.

“If you think about how PTSD relates to suicide, emerging evidence suggests that anxiety and stress, and the biochemical correlates of that stress, take a toll on the brain, especially in areas of the prefrontal cortex that regulate emotion and would normally inhibit self-destructive impulses,” says Miller. “Identifying the genes involved may give us new insights into the biological mechanisms linking PTSD to suicide.”

“The psychiatric medications we have for PTSD are still in early stages of development and only modestly effective,” says Miller. “If SKA2 expression turns out to be really important for brain health, we could try to develop drugs that enhance its activity.”

Even if other studies continue to correlate SKA2 with PTSD and brain cell death, this biomarker is unlikely to become a stand-alone indicator of suicide risk, says Sadeh. Whether or not a person is at risk for suicide depends not just on genetics, but on family history, social supports, mental illness, access to firearms, and myriad other factors. But she thinks this biomarker could provide a useful tool in conjunction with other tests.

“It might help us improve risk prediction,” says Sadeh. “And if you have limited resources, it would help us direct them to those people who are at the highest risk.”

The possibility of genetic tests for PTSD or suicide risk raises questions, as well: could the tests someday prevent soldiers from entering combat, or screen people out of the military altogether? Miller says a genetic screen is unlikely in the foreseeable future, given the complex nature of PTSD and suicide risk. But he hopes that understanding genes like SKA2 will lead to better treatments. “The psychiatric medications we have for PTSD are still in early stages of development and only modestly effective,” says Miller. “If SKA2 expression turns out to be really important for brain health, we could try to develop drugs that enhance its activity, or act on methylation at a particular brain site, or use genetic profiles to match treatments to patients—that’s the exciting potential.”

Miller also hopes that the research may eventually apply to other groups known to suffer from PTSD and higher risk of suicide, such as victims of child abuse and sexual assault. “We’re still a ways from knowing whether studies in veterans apply to other at-risk populations,” he says, “but PTSD is not unique to veterans, nor is suicide.”

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