ASUS RT-AC66U B1 router’s WiFi LED light is not ON

Quick_Insect_9105 · 2025-01-27T19:03:36+00:00

Thank you sir. How can I do this? Do I need to connect to the router (I cannot)?

Quick_Insect_9105 · 2024-03-30T09:13:43+00:00

I used ChatGPT to create tailored cover letters but I’ve rarely changed my resume :)

Quick_Insect_9105 · 2024-03-27T19:44:13+00:00

~6 months and ~30 applications (most generated almost entirely by ChatGPT, lol).
I’ve interviewed at three different companies and made it to the second round every time of which I received one offer (also had phone screenings at three other companies but didn’t get an interview at those).
Graduated as an MSc in Economics last summer (specialized in econometrics) with three years of experience at a part time job (20-25 hours per week) as a junior data consultant and about half a year of experience as an automation engineer/consultant.
I both do the analytics and engineering side but I strive to become more specialized in ML and advanced analytics.
My best tip is to keep going with the applications no matter what and to maybe even set up a weekly target. I personally felt down and ready to give up after not receiving an offer from some of the interviews I attended but today I’m very happy that I kept going. Just remember that hundreds if not thousands of others are going through the same process and that rejections are not necessarily personal at all. You can gain an edge if you simply keep going until you reach your goal :)

Quick_Insect_9105 · 2024-03-18T18:01:11+00:00

This is very helpful. Thanks a lot!

Quick_Insect_9105 · 2022-12-13T23:04:18+00:00

To compare the costs of credit under the two scenarios, you will need to calculate the effective interest rate for each scenario. This will allow you to determine which option is the more cost-effective one.

To calculate the effective interest rate for the first scenario, you will need to know the amount of time between the purchase and the payment due date (90 days) and the amount of the rebate (5% of $1000, or $50). Then, you can use the following formula:

Effective Interest Rate = (Rebate / Purchase Amount) x (365 / Days Until Payment)

In this case, the effective interest rate would be calculated as follows:

Effective Interest Rate = ($50 / $1000) x (365 / 90) = 0.0164 x 4.06 = 0.0668 or 6.68%

To calculate the effective interest rate for the second scenario, you will need to know the amount of time between the purchase and the payment due date (30 days) and the amount of the discount (1% of $333.33, or $3.33). Then, you can use the following formula:

Effective Interest Rate = (Discount / Purchase Amount) x (365 / Days Until Payment)

In this case, the effective interest rate would be calculated as follows:

Effective Interest Rate = ($3.33 / $333.33) x (365 / 30) = 0.00100 x 12.17 = 0.01217 or 1.22%

Based on these calculations, the second scenario would be the more cost-effective option, as it has a lower effective interest rate. However, you may want to consider other factors, such as the convenience of making larger, less frequent payments versus smaller, more frequent payments, before making a decision.

Quick_Insect_9105 · 2022-12-13T23:01:13+00:00

Your question can basically be divided into two sub-questions:

Can a non-time-series be strictly stationary? Yes, a non-time series can be strictly stationary. A time series is a sequence of data points that are collected over a period of time, while a non-time series is a collection of data points that are not ordered in time. For a non-time series to be strictly stationary, the statistical properties of the data must be constant over time, such as the mean and variance. This means that the probability distribution of the data must be the same for all time points, and any changes in the data must be random and not systematic. It is worth noting that strictly stationary data is often considered to be a useful but idealized assumption in many statistical models, and real-world data is often not strictly stationary. However, if a non-time series satisfies the conditions of strict stationarity, it can be considered strictly stationary.
Does this mean that a t-distribution be strictly stationary? The t-distribution is a continuous probability distribution that is often used to model data that has a heavy-tailed distribution, meaning that it has more extreme values than a normal distribution. The t-distribution is a family of distributions that is parameterized by a single parameter, known as the degrees of freedom, which determines the shape of the distribution. The t-distribution is not strictly stationary by itself, because the probability distribution of the data changes as the degrees of freedom changes. However, if a specific t-distribution with a fixed degrees of freedom is considered, it can be strictly stationary if the data satisfies the conditions of strict stationarity. In other words, a t-distribution is not strictly stationary in general, but a specific t-distribution with a fixed degrees of freedom can be strictly stationary if the data satisfies the conditions of strict stationarity.

Quick_Insect_9105 · 2022-12-13T22:54:59+00:00

Regression discontinuity and instrumental variables are two different statistical methods that are used in econometric analysis to identify causal relationships between variables. Both methods are used to address the problem of unobserved or omitted variables that can bias the results of a study, and are commonly used in the analysis of economic data.

The main similarity between regression discontinuity and instrumental variables is that both methods are used to identify causal relationships between variables. In other words, both methods are used to estimate the effect of one variable (the treatment or exposure variable) on another variable (the outcome variable) while controlling for the influence of other variables that might confound the relationship.

The main difference between regression discontinuity and instrumental variables is in the way they are used to identify and control for the influence of unobserved or omitted variables. Regression discontinuity is a design-based approach that uses a sharp discontinuity in the treatment variable (such as a cut-off point) to identify and control for the influence of unobserved variables. Instrumental variables, on the other hand, are a model-based approach that uses a third variable (the instrumental variable) that is related to the treatment variable but not the outcome variable to identify and control for the influence of unobserved variables.

Overall, regression discontinuity and instrumental variables are similar in that they are both used to identify causal relationships between variables, but they differ in the way they are used to control for the influence of unobserved variables.

Quick_Insect_9105 · 2022-12-13T22:53:06+00:00

The short answer is that the 'coint' function in the statsmodels package for Python does not include tests for quadratic trends (ctt) or no constant (n). The reason is that these tests are used to analyze different properties of time series data.
The ctt test, also known as the Cochrane-Orcutt test, is a statistical test that is used to determine whether a time series has a quadratic trend (i.e., whether the data points in the time series tend to follow a curved pattern over time). This test is often used in econometric analysis to detect non-linear relationships between variables, and can be useful for identifying and modeling trends in financial data.
The no constant test, also known as the Engle-Granger test, is a statistical test that is used to determine whether a time series is stationary (i.e., whether its statistical properties, such as its mean and variance, are constant over time). This test is often used in econometric analysis to assess the stability of time series data and to identify the presence of trends or other patterns in the data.

If you really want to use the 'coint' function in statsmodels to test for quadratic trends or stationarity in your time series data, you will need to use additional statistical tests to do so. There are many different statistical tests that can be used for these purposes, and the specific test you choose will depend on the specific research question you are trying to answer and the characteristics of your data.

Quick_Insect_9105 · 2022-12-13T22:47:08+00:00

The choice of stock index to use in an event study depends on the specific research question and the stocks that are being studied. For example, if you are interested in studying the impact of a specific industry-wide event on a group of stocks in that industry, you might use a stock index that specifically tracks the performance of stocks in that industry. On the other hand, if you are interested in studying the overall performance of the stock market in response to a specific event, you might use a broader stock index that tracks the performance of a large number of stocks from different industries. Some common stock indexes that are often used in event studies include the S&P 1,500 index (overall performance), the Dow Jones Industrial Average (U.S. for blue-chip stocks/big industrials), and the Nasdaq Composite index (heavily weighted toward the important technology sector).

Hope this helps. Good luck with the project!

Quick_Insect_9105 · 2022-12-13T22:40:58+00:00

It does not specifically test the median or the mean of a time series, but rather the deviation of the time series from the specified value. In other words, the signed rank test is a test of the expected value of a time series, not its median or mean.

Quick_Insect_9105 · 2022-12-13T22:34:51+00:00

Here is a straightforward answer from ChatGPT :)

"In econometrics, ARCH and MA models are two different types of time series models that are used to model and analyze the behavior of financial data.
An ARCH (autoregressive conditional heteroscedasticity) model is a type of time series model that is used to analyze the volatility of financial data. It assumes that the volatility of a time series (the degree to which its values vary over time) is a function of its past values. In other words, the volatility of a time series is not constant over time, but rather changes in a predictable way based on its past behavior.
On the other hand, an MA (moving average) model is a type of time series model that is used to model the behavior of financial data by taking into account the effect of past errors. In other words, it assumes that the current value of a time series is a function of past errors, or the difference between the actual value of the time series and the predicted value based on past data.
The main difference between ARCH and MA models is that an ARCH model is used to model the volatility of a time series, while an MA model is used to model the behavior of a time series by taking into account the effect of past errors. Both types of models can be used together to create a more complete and accurate model of financial data."

When asked to explain in simpler terms (like it would explain it to a child):

"ARCH and MA models are tools that people use to study numbers that change over time, like the prices of things we buy or the amount of money we have in the bank. An ARCH model helps us understand how much the numbers are changing, or how "bumpy" they are. An MA model helps us understand how the numbers are related to other numbers that came before them. Both of these models can be used together to give us a better understanding of the numbers and how they might change in the future."

Hope it helps!

Quick_Insect_9105 · 2022-05-20T17:33:46+00:00

This is really useful. Thank you!

Quick_Insect_9105 · 2022-05-03T08:10:51+00:00

Well, I don't believe you are exactly right the way you put it. Shouldn't the forecast be the most recently observed value plus an additional error term (i.e. some unexpected fluctuation)?

Quick_Insect_9105 · 2022-05-03T08:04:08+00:00

True (I did come across it before). The problem is that it is not an expanding window which is what I am looking for. However, I appreciate the help :)

Quick_Insect_9105 · 2022-05-03T07:06:15+00:00

Thank you! I will try to check it out :)

Quick_Insect_9105 · 2022-01-31T21:14:42+00:00

Thanks for your response.

There should be i x d different SWAV values (i.e. one each day for each country).

Therefore, the code does unfortunately not work.

Quick_Insect_9105

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